genetics of tree architecture in peach (prunus persica...

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GENETICS OF TREE ARCHITECTURE IN PEACH (Prunus persica (L.) Batsch)

By

OMAR CARRILLO MENDOZA

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2012

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© 2012 Omar Carrillo Mendoza

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To my wife Patricia and my Mom Marcela with love

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ACKNOWLEDGMENTS

I am thankful to my advisor Dr. Jose Chaparro and retired professor Dr.

Wayne Sherman, it has been a pleasure to learn from them and be part of the

Stone Fruit Breeding Program. To the members of my committee: Dr. Rebecca

Darnell, Dr. Kevin Folta, Dr. Matias Kirst and Dr. Jeffrey Williamson for all the

guidance during my research. To Dr. James W. Olmstead for allowing me and

Patricia for teaching me how to use the real time PCR machine. To Bruce Topp,

Jean-Clement Marceillou, Jose Gandia, Jorge Rodriguez, Mohamed Benzit, Paul

Lyrene and Thomas Beckman for sharing their experience. To Mark Gal, Cecil

Shine, and John Thomas for their valuable help on the farm. To Valerie for her

support in the lab. To Elia Ulivi and Dario Chavez for helping me to take field

data. To my friends, Andres, Aparna, Divya, Marga, Mitra, Octavio, Preeti,

Silvia,Yuan and especially Kendra for their friendship and kindness. To my

Mexican fellows: Alberto, Aurora, Miriam, Nicacio, Oscar, Paola, Paula and

Sebastian for helping me to adjust to my new home and reminding me my

beloved home country. I am especially grateful to CONACYT and the people of

Mexico for granting me with a scholarship that made possible this achievement.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................. 7

LIST OF FIGURES ........................................................................................................ 10

LIST OF ABBREVIATIONS ........................................................................................... 12

ABSTRACT .................................................................................................................... 14

CHAPTER

1 LITERATURE REVIEW ........................................................................................... 16

Introduction .............................................................................................................. 16

Plant Architecture .................................................................................................... 16 Axillary Meristem Development and Branching ....................................................... 17 Apical Dominance.................................................................................................... 18

Plant Architecture and Agriculture ........................................................................... 20 Temperate Fruit Tree Architecture .......................................................................... 20

Peach Tree Architecture .......................................................................................... 22

2 DEVELOPMENT OF A BRANCHING INDEX FOR EVALUATION OF PEACH SEEDLINGS USING INTERSPECIFIC HYBRIDS ..................................... 26

Introduction .............................................................................................................. 26 Materials and Methods ............................................................................................ 27

Branching Index Formula .................................................................................. 27 Plant Material .................................................................................................... 29

Data Collection .................................................................................................. 29 Statistical Analysis ............................................................................................ 30

Results and Discussion ........................................................................................... 30

3 BRANCHING AND BLIND NODE INCIDENCE IN INTERSPECIFIC BACKCROSS FAMILIES OF PEACH ..................................................................... 44

Introduction .............................................................................................................. 44 Branching in different Prunus species ............................................................... 44

Blind nodes in peach ......................................................................................... 46 Materials and Methods ............................................................................................ 48

Plant Material .................................................................................................... 48 Plant Management ............................................................................................ 48 Branching Index Data Collection ....................................................................... 49 Blind node data collection ................................................................................. 50

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Identification of Selfs ......................................................................................... 50

Statistical Analysis ............................................................................................ 51 Results and Discussion ........................................................................................... 52

Branching Index ................................................................................................ 52 Blind Nodes ....................................................................................................... 55 Heritability for Branching and Blind Nodes ........................................................ 57

4 MAPPING CANDIDATE GENES and QTLs ASSOCIATED WITH BRANCING AND BLIND NODES IN Prunus sp. INTERSPECIFIC BACKCROSS FAMILIES ......................................................................................... 66

Introduction .............................................................................................................. 66 Materials and Methods ............................................................................................ 71

Plant Material .................................................................................................... 71 DNA Extraction ................................................................................................. 72 Candidate Gene Primer Design ........................................................................ 73

Branching Index Data Collection ....................................................................... 73 Blind Node Data Collection ............................................................................... 73

Candidate Gene Primer PCR Optimization ....................................................... 74 Candidate Gene Sequencing ............................................................................ 74 Genotyping ........................................................................................................ 75

SSR markers .............................................................................................. 75 Candidate gene genotyping with restriction enzymes ................................. 76

Candidate gene genotyping with high resolution melt analysis ................... 77 Statistical Analysis ............................................................................................ 77

Results and Discussion ........................................................................................... 78 Phenotypic Differences within and among Backcross Families ........................ 78 Polymorphism in Branching and Blind Node Candidate Genes ........................ 79

Genetic Maps .................................................................................................... 82 Branching Index QTLs ...................................................................................... 86

Blind Node QTLs ............................................................................................... 88 Allelic effects from QTLs ................................................................................... 89 Relationships between Branching and Blind Node QTLs .................................. 91

QTLs Detection by Candidate Genes ............................................................... 92

5 CONCLUSIONS .................................................................................................... 110

APPENDIX: COMPLEMENTARY TABLES AND FIGURES ........................................ 115

LIST OF REFERENCES .............................................................................................. 136

BIOGRAPHICAL SKETCH .......................................................................................... 150

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LIST OF TABLES

Table page 2-1 Mean total branch number per tree and branching index values in

peach x almond and peach x P. kansuensis F1 hybrid populations. ................... 36

2-2 Mean number of first order branches, and the mean number of second, third and fourth order branches. ............................................................ 36

2-3 Mean total branch number per tree and branching index means for first, second, third and fourth order branching clusters ....................................... 37

3-1 Total number of progeny and number of contaminating self-pollinated progeny in the interspecific backcross families. . ................................................ 60

3-2 Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL) . .................................................... 60

3-3 Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL). ..................................................... 60

3-4 Mean branching index (BI) and blind node incidence for the main axis (BNM) and lateral branches (BNL) in the interspecific backcross. ...................... 61

3-5 Orthagonal contrasts of backcross families by parent for branching index (BI) and blind node incidence in the main axis (BNM). .............................. 61

3-6 Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL) in the interspecific backcross.. .......................................................................................................... 61

3-7 Orthagonal contrasts of backcross families by parent for branching index (BI) and blind node incidence in the main axis (BNM). .............................. 62

3-8 Covariates measured in the interspecific backcross families (winter of 2010).. ................................................................................................................. 62

3-9 Covariates measured in the interspecific backcross families (winter of 2011).. ................................................................................................................. 62

4-1 Interspecific backcross families used for studies in tree architecture.. ................ 95

4-2 Mean branching index (BI) and blind node incidence for the main axis (BNM) and lateral branches (BNL) in the interspecific backcross.. ..................... 95

4-3 Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL).. .................................................... 95

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4-4 Statistics for single nucleotide polymorphic positions (SNP) within each branching and blind node candidate gene. ................................................. 96

4-5 Number of heterozygous single nucleotide polymorphic positions (SNP) within different Prunus genotype sequences.. .......................................... 97

4-6 Haplotypes found in single nucleotide polymorphic positions in branching and blind nodes candidate genes. ...................................................... 98

4-7 QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x (FG x P. kan) combined families. ..................................................... 99

4-8 QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x (FG x TNP) combined families. ........................................................ 99

4-9 QTLs associated with branching index (BI) in ‘UF97-47’ x (‘UF97-47’ x P. kan). ................................................................................................................ 99

4-10 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30wbs’ and ‘UFSharp’ x (FG x P. kan). ..................... 100

4-11 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30 wbs’ and ‘UFSharp’ x (FG x TNP) ........................ 101

A-1 Microsatellite markers used to identify self-pollinated in the interspecific backcross populations studied.. .................................................... 115

A-2 Specific primers designed to amplify candidate genes associated with axillary meristem formation (AMF) and outgrowth (AMO). ................................ 116

A-3 Single nucleotide polymorphisms detected in PpAXR1 amplicon.. ................... 117

A-4 Single nucleotide polymorphisms detected in PpBRC1 amplicon.. ................... 117

A-5 Single nucleotide polymorphisms detected in PpBRC2 amplicon.. ................... 118

A-6 Single nucleotide polymorphisms detected in PpCUC1 amplicon.. ................... 118

A-7 Single nucleotide polymorphisms detected in PpCUC2 amplicon.. ................... 119

A-8 Single nucleotide polymorphisms detected in PpCUC3 amplicon.. .................. 119

A-9 Single nucleotide polymorphisms detected in PpLAS amplicon ........................ 120

A-10 Single nucleotide polymorphisms detected in PpMAX1 amplicon.. ................... 120

A-11 Single nucleotide polymorphisms detected in PpMAX2 amplicon. .................... 121


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A-14 Single nucleotide polymorphisms detected in PpPIN amplicon. ........................ 122

A-15 Single nucleotide polymorphisms detected in PpREV amplicon.. ..................... 123

A-16 Single nucleotide polymorphisms detected in PpSPS amplicon.. ...................... 123

A-17 SSR and morphological markers selected to use in the mapping of backcross populations. ...................................................................................... 124

A-18 Candidate genes genotyped with restriction enzymes ...................................... 125

A-19 Candidate genes genotyped with high resolution melt analysis. ....................... 126

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LIST OF FIGURES

Figure page 2-1 Branching index values generated in ‘UF97-47’ peach x ‘Tardy

Nonpareil’ (TNP). ................................................................................................ 38

2-2 Reduced branching typical of a three-year-old ‘Flordaguard’ peach x ‘Tardy Nonpareil’ almond hybrid in winter of 2008. ............................................. 39

2-3 Profuse branching typical of a three-year-old ‘Flordaguard’ peach x P. kansuensis hybrid in winter of 2008.. .................................................................. 39

2-4 Branching index values generated for year 2007 in ‘UF97-47’ x ‘Tardy Nonpareil’, ‘Okinawa’ x P. kansuensis ‘A’............................................................ 40

2-5 Branching index values generated for year 2008 in ‘UF97-47’ x ‘Tardy Nonpareil’, ‘Okinawa’ x P. kansuensis ‘A’’.. ......................................................... 41

2-6 Branching index values calculated in 2007 as a predictor for branching number in year 2008. .......................................................................................... 42

2-7 Branching index values calculated in 2008. ........................................................ 43

3-1 Branching index values of interspecific backcross progenies.............................. 63

3-2 Distribution of blind node incidence in lateral branches within interspecific backcross families in 2010. ............................................................. 64

3-3 Distribution of blind node incidence in lateral branches within interspecific backcross families in 2011. ............................................................. 65

4-1 Genes involved in axillary meristem formation and outgrowth and their interactions.. ...................................................................................................... 102

4-2 ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis) backcross combined linkage map.. ................................................................... 103

4-3 ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) backcross combined linkage map.. ................................................................... 104

4-4 ‘UF97-47’ x (‘UF97-47’ x P. kansuensis) backcross linkage map.. .................... 105

4-5 QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis) combined families. ....................... 106

4-6 QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’ almond). ................................... 106

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4-7 QTLs associated with branching index (BI) in ‘UF97-47’ x (‘UF97-47’ x P. kansuensis).. ................................................................................................. 107

4-8 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30wbs’ and ‘UFSharp’. .............................................. 108

4-9 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP 00-30 wbs’ and ‘UFSharp’. ............................................ 108

4-10 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘UF97-47’ x (‘UF97-47’ x P. kan). ........................................ 109

A-1 Individual interspecific Prunus backcross genetic maps.. ................................. 129

A-2 QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x P. kan 3). .. ........................................................................................................... 130

A-3 QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x P. kan 6). ............................................................................................................... 130

A-4 QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x P. kan 3)... ............................................................................................................. 131

A-5 QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x P. kan 6). ............................................................................................................... 131

A-6 QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x TNP)... ............................................................................................................... 132

A-7 QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x TNP).. ................................................................................................................ 132

A-8 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30wbs’ x (FG x P. kan 3). ....................................... 133

A-9 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30wbs’ x (FG x P. kan 6) .......................................... 133

A-10 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘UFSharp’ x (FG x P. kan 3).. .............................................. 134

A-11 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) ‘UFSharp’ x (FG x P. kan 6).. .................................................. 134

A-12 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) ‘AP00-30 wbs’ x (FG x TNP).. ................................................. 135

A-13 QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘UFSharp’ x (FG x TNP).. .................................................... 135

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LIST OF ABBREVIATIONS

AM Apical meristem

AMF Axillary meristem formation

AMO Axillary meristem outgrowth

AP Attapulgus

AT Annealing temperature

AXR Auxin resistant

BC Backcross

BI Branching index

BLAST Basic local alignment search tool

BNM Blind nodes in the main axis

BNL Blind nodes in the lateral shoots

BRC Branched

BP Base pair

CAPS Cleaved amplified polymorphism sequence

cM Centimorgan

CUC Cup-shaped cotyledon

DNA Deoxyribonucleic acid

FG Flordaguard

FP Foliar primordia

HRMA High resolution melt analysis

LAS Lateral suppressor

LG Linkage group

LOD Logarithm of odds

MAX More axillary growth

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OKI Okinawa

PCR Polymerase chain reaction

PS Peach selection

P. kan Prunus kansuensis

QTL Quantitative trait loci

PIN Pinhead

SMS Shoot multiplication signal

RAX Regulator of axillary growth

REV Revoluta

SNP Single nucleotide polymorphism

SPS Supershoot

SSR Simple sequence repeat

TF Terminal flower

TNP Tardy Nonpareil

UF University of Florida

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

GENETICS OF TREE ARCHITECTURE IN PEACH (Prunus persica (L.) Batsch)

By

Omar Carrillo Mendoza

February 2012

Chair: Jose X. Chaparro Major: Horticultural Science

Little effort has been made to understand the genetic control of tree

architecture in peach. In addition, the occurrence of blind nodes is a critical factor

that affects peach tree architecture and productivity in subtropical climates. In

this study, a branching index was developed to facilitate the assessment of

branching intensity of the trees. Seven backcross families were developed using

‘Flordaguard’ peach x P. kansuensis or ‘Tardy Nonpareil’ almond F1s

backcrossed to ‘AP00-30wbs’, ‘UFSharp’ or ‘UF97-47’ peach selections and

evaluated for branching index and blind node frequency during the winters of

2010 and 2011. P. kansuensis backcrosses presented increased branching and

lower blind node incidence whereas almond backcrosses presented less

branching and higher blind node incidence, resembling the P. kansuensis and

almond F1 parents. There was also broad variability for branching and blind

nodes within the P. kansuensis and TNP almond backcross families influenced

by the peach parents that were used to generate the backcross populations. The

moderate heritability and year-to-year correlation for these traits indicate that

they are affected by the environment, but selection for reduced branching and

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lower blind node incidence is feasible. SSRs and a set of 14 candidate genes

related with branching and bud development in Arabidopsis were used to map

QTLs associated with these two traits in the seven backcrosses. SNPs were

found within the candidate gene sequences in the different Prunus parents.

Genetic maps containing the selected SSRs and candidate genes were obtained

for each backcross family and a combined map for all the P. kansuensis families

and all the almond families. Branching and blind nodes QTL were detected in the

individual backcross family analysis, and the combined P. kansuensis and ‘Tardy

Nonpareil’ almond families analysis. The candidate genes tested did not map to

the location of the major QTLs, PpCUC1 and PpBRC2 mapped to minor QTL for

branching and blind nodes, respectively. The QTLs found in this study represent

the first steps toward marker assisted selection for reduced branching and

reduced incidence of blind nodes in commercial peach cultivars.

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CHAPTER 1 LITERATURE REVIEW

Introduction

Most temperate fruit tree breeding programs pay major attention to aspects such

as fruit quality, chilling requirement, crop load and tolerance to some diseases. Limited

effort has been devoted to tree architecture and tree branching patterns (Laurens et al.,

2000). As labor and pruning costs of fruit trees have increased, size control and

architecture has gained importance. Tree architecture will continue to increase in value

as a major trait for tree fruit breeders (Segura et al., 2008).

Plant Architecture

Plant architecture can be considered as the organization of plant components in

space that may change over time (Godin et al., 1999). It is the result of endogenous

growth processes and exogenous constraints (Barthelemy and Caraglio, 2007),

Higher plants exhibit a variety of architectures that are defined in great part by

growth determinacy, branching patterns and node elongation (Wang and Li, 2008).

These are the most important morphological traits to depict and analyze plant

architecture (Barthelemy and Caraglio, 2007).

Growth is determinate when the meristem dies or becomes a specialized structure

after a period of growth such as a flower, losing any further capacity to develop.

Indeterminate growth occurs when the meristem never loses the capacity to grow (Halle

et al., 1978).

Branching can be defined by several sub-traits:

SYLLEPTIC OR PROLEPTIC. Sylleptic branching occurs when the axillary meristem elongates immediately after its initiation and prolleptyc when the axillary meristem remains dormant for a time before elongating (Wu and Hinckley, 2001).

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MONOPODIAL OR SYMPODIAL. Monopodial is defined by the presence of one main axis and sympodial is defined by the presence of several axes (Barthelemy and Caraglio, 2007).

RHYTHMIC OR CONTINUOUS. Alternation of branched and unbranched nodes within the main axis is rhythmic branching and diffuse generation of axillary shoots along the entire axis is continuous branching (Halle et al., 1978).

ACROTONIC, MESOTONIC AND BASITONIC. This sub-trait depicts the preferential position for the generation of lateral branches within the plant axis being the basal part (basitonic) conferring the bushy appearance, median (mesotonic) and superior (acrotonic) giving arborescent form (Barthelemy and Caraglio, 2007).

Node elongation and number determine the longitude of the plant axis and

therefore the exploration of the plant in the vertical space (orthotropy) and the horizontal

space (plagiotropy) (King and Maindonald, 1999; Tomlinson, 1978).

Also, the concept of reiteration is used to describe the natural repetition of the

branching patterns at different organizational levels of the plant body (Costes et al.,

2006).

Several other quantitative and qualitative traits and sub-traits have been used as a

reference for depicting and analyzing plant architecture according to the objective of the

study. Some examples are: complexity of branching order within a plant, the insertion

angle of the lateral shoots, the preformation or neoformation of the organs (Barthelemy

and Caraglio, 2007). This large list of traits and sub-traits is a consequence of the

complexity of plant architecture and the multiple approaches to the study of this topic.

Axillary Meristem Development and Branching

Plant architecture is the result of the activity of meristems (Costes et al., 2006).

Branch development consists of two distinct steps: the initiation of an axillary meristem

and its succeeding growth (Wang and Li, 2008). Axillary meristems, which afterward

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function as shoot apical meristems (SAMs), grant plants with unlimited growth potential

(Alvarez et al., 2006).

Axillary meristems are derived from the peripheral zone of the axillary meristem

(Wang and Li, 2008). The lateral organ primordium is induced by local auxin

accumulation in the peripheral zone of the apical meristem. The subsequent primordia

are formed in distant zones where auxin has not been depleted by the preexisting

primordial determining leaf and associated axillary meristem arrangement or phyllotaxis

(Fleming, 2005; Reinhardt et al., 2000; Reinhardt et al., 2003).

Once initiated, the axillary meristem develops into a lateral bud and whether that

bud outgrows to form a new shoot or stays dormant dictates branching patterns

(Aguilar-Martinez et al., 2007). There are three types of dormancy in lateral buds: 1)

endodormancy, which is growth regulation by the internal factors within the bud; 2)

ecodormancy, which is growth regulation by environmental factors that limit plant growth

such as water, nutrients and temperature; and 3) paradormancy, which is growth

regulation by an external organ or tissue other than the dormant bud, for example apical

dominance (Lang et al., 1985).

Apical Dominance

Apical dominance is the inhibiting control exerted by the shoot tip over axillary

meristems maintaining the lateral buds in a paradormant stage (Cline, 1997). It is known

that auxin and cytokinin are related to the maintenance of apical dominance; auxin

synthesized in the apical meristem is known to retain and cytokinin is known to release

apical dominance (Hartig and Beck, 2006).

Nevertheless, several hypotheses have been formulated to explain apical

dominance that might be incorporated in a single model (Dun et al., 2006): 1) the

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classical hypothesis which affirms regulation by auxin levels and secondary

messengers like cytokinin; 2) the auxin transport hypothesis which suggests auxin

movement in a transport stream as the regulatory operator; and 3) the bud transition

hypotheses which postulates different bud developmental stages having variable levels

of sensitivity to auxins and other signals.

Auxin does not enter directly into the bud (Booker et al., 2003), and some studies

suggest the existence of an additional messenger suppressing axillary meristem

outgrowth (Booker et al., 2004; Stirnberg et al., 2007; Stirnberg et al., 2002). The

second messenger has been referred to as a graft-transmissible shoot multiplication

signal (SMS) (Johnson et al., 2006).

Gomez-Roldan et al. (2008) and Umehara et al. (2008) simultaneously but

separately identified a third group of important compounds working with branching

mutants; these compounds are strigolactones. Strigolactones belong to the group of

terpenoid lactones and are signaling molecules involved in the promotion of seed

germination of the parasitic plants Striga, Orobanche and Phelipanche spp.. They are

also implied as a factor for hyphal branching in arbuscular mycorrhizal fungi

(Bouwmeester et al., 2007). Arabidopsis mutants that had profuse branching were

defective in strigolactone production and the two genes causing the profuse branching

phenotype (MAX3 and MAX4) encoded carotenoid-cleaving dioxygenases. Other genes

involved in branching are AXR1 (auxin resistant) which encodes a protein that is

required for response to auxins in Arabidopsis (Stirnberg et al., 1999) and TB1 (teosinte

branched1) from maize (Doebley et al., 1997), which is considered responsible for the

differences in the suppression of axillary branches between maize and its wild ancestor

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teosinte. Additional recently discovered genes that encode transcription factors involved

with axillary meristem formation include REV (Revoluta), LAS (lateral suppressor) and

the CUC (cup-shaped cotyledon) genes (Otsuga et al., 2001).

Plant Architecture and Agriculture

Understanding and manipulating plant architecture has had great impact on

agriculture. During the green revolution, yield potential was increased by breeding for

ideotypes or plants with improved architecture in cereal crops (Khush, 2001).

Rice and wheat cultivars were tall and leafy, had weak stems and a harvest index

of 0.3; they were not suitable for high fertilization regimes because plants grew too tall

and lodged (Slafer et al., 1999). The sd gene in rice (Yang and Hwa, 2008) and Rht in

wheat (Keyes and Sorrells, 1989) were incorporated to successfully reduce plant height

and increase tillering. Dwarf plants did not lodge when fertilizers were applied,

increasing harvest index by 60% (Slafer et al., 1999). In the case of maize, breeding

has focused on erect leaves that allow higher plant densities and shorter plants in

tropical climates (Johnson et al., 1986).

Temperate Fruit Tree Architecture

Tree fruit architecture has been managed by means of cultural practices and

breeding. The aim, as in other crops, is to increase light interception and therefore yield

and quality in addition to facilitating orchard management (Tworkoski et al., 2006).

Cultural practices for controlling growth and vigor in perennial fruit trees has been

achieved primarily by the use of dwarfing rootstocks (Lockard and Schneider, 1981),

pruning and training (Stephan et al., 2007), application of growth regulators (Erez,

1986), fertilization (Jordan et al., 2009) and deficit irrigation (Chalmers et al., 1981).

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Dwarfing rootstocks have been widely used in apples. The M.9, M.26, M.27 and

the MM rootstock series from East Malling, UK, and their progenies have benefited

apple production by decreasing plant height and inducing precocity in fruit production.

The reduction in plant stature has permitted the development of high intensity planting

systems (Webster et al., 2003). Dwarfing rootstocks for sweet cherry have also been

developed. The triploid ‘Gisela 6’ (Prassinos et al., 2009) rootstock reduces tree size up

to 50% (Schmidt and Gruppe, 1988). However, dwarfing rootstocks for commercial

peach production are not available (DeJong et al., 2001; Masabni et al., 2007).

Pruning and training techniques together with rootstocks are the main tree size

control techniques used in modern fruit production, the evolution of these has been

more rapid in apples and pears than in peaches (Loreti and Massai, 2002; Stephan et

al., 2007). Tree training systems, besides open center, with specialized pruning

methods have been developed such as ‘tatura’, ‘palmette’ or ‘fusetto’ (Loreti and

Massai, 2002; Porter et al., 2002). Summer pruning has also been implemented to

control vegetative shoot development (Marini and Barden, 1987). Nonetheless, pruning

and training represents a great cost for growers (DeJong et al., 2005).

Different growth retardants such as cloromequat chloride, uniconazol and

Paclobutrazol have been used to favor a reproductive response on the tree at the

expense of vegetative development (Bahadori and Arzani, 2008; Erez, 1986).

Paclobutrazol is not registered for peach production in the United Sates and in other

tree fruit crops in different countries (Loreti and Massai, 2002). Paclobutrazol is used to

control tree height in commercial peach production in Australia (Erez, 1986).

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In addition, fertilization practices can affect tree architecture. Fall N increases

proleptic branches but decreases sylleptic shoots in peach (Jordan et al., 2009).

Reduced N application is suggested as a mean to reduce vegetative growth and

excessive branching (Weinbaum et al., 1992).

Deficit irrigation to control vegetative growth is a potential alternative for certain

edafic and climatic conditions such as semi-arid or Mediterranean areas (Chalmers et

al., 1981). Nevertheless, extra care must be taken in order to not compromise

accumulation of reserves, fruit quality and flower bud differentiation (Johnson et al.,

1992). Deficit irrigation is not an option for summer rainy areas like Florida.

Breeding for tree form and size has been hindered by a poor understanding of

genetics on these traits (Segura et al., 2007). However, several QTL studies in apple

have been carried out to dissect tree architecture into genetic and environmental effects

(Segura et al., 2007; Segura et al., 2008). Breeding for unconventional fruit tree

architecture has been very limited, and most of the effort invested and success obtained

has been in apples for compact spur types (Barthelemy and Caraglio, 2007). One

striking example is the ‘McIntosh’ apple mutant ‘Wijcik’ which is a columnar type with

compact internodes, reduced lateral branching and augmented production of spurs,

optimal for high density orchards (Kelsey and Brown, 1992).

Peach Tree Architecture

Peach trees are vigorous and are propagated on vigorous rootstocks. High density

orchards require severe pruning that induces strong vegetative growth resulting in

shading and reduction of flower bud formation and fruit quality (Marini and Corelli-

Grappadelli, 2006). The peach industry has lower productivity and does not have high-

density production systems when compared to the apple industry (Scorza et al., 2006).

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Peaches with predominant basal and intermediate branching, resulting in a

somewhat branched but open tree architecture that permits fruit hanger production

without excessive shading of the internal part of the tree, are wanted for commercial

purposes (Cook et al., 1999).

Peach mutants with contrasting growth habits have been identified. These altered

types can potentially be planted in higher density as they have better light interception

and need less pruning, increasing the potential for productivity (Scorza, 1984; Scorza et

al., 1986). These different growth types are controlled by single-genes (Niu et al., 2004).

There are three horticultural classes (Bassi et al., 1994).

The first class groups the standard shapes with differences in size: 1) Standard

type, which is typical of commercial peaches, has vigorous acropetal growth,

moderately strong apical dominance and one-year-old fruiting shoots (Marini and

Corelli-Grappadelli, 2006); 2) Brachytic dwarf (dwdw) has significantly reduced

internodes and dense canopy. The density of second order branches is high (Fideghelli

et al., 2003). Dwarf cultivars have been developed (Hansche, 1989), but the dense

canopy complicates fruit thinning, harvesting and reduces fruit quality (Giovannini and

Liverani, 2005); 3) Semidwarf corresponds to trees that are intermediate between

standard and dwarf. Internode length can be equal or shorter than standard type

(Scorza, 1984). Two types have been recognized: the “bushy’ type which is present with

a double recessive gene (b1b1b2b2) and the ‘A72’ that is single recessive and presents

incomplete dominance (Nn) (Hu and Scorza, 2009); 4) Spur-type are trees with a high

production of fruit spurs. Internode length and tree size is similar to the standard peach

(Scorza, 1987); 5) Compact trees (Ct) are smaller than standard but have a denser

24

canopy, wide branch angles, relatively short internodes and longer lateral shoots (Bassi

et al., 1994). They have a higher number of second and third order twigs (Scorza,

1984).

The second and the third class have different shapes than the standard class. The

second class includes: 1) Columnar or pillar (brbr), which has a small canopy with

branches growing in upright fashion with very close angles and reduced number of

lateral branches (Scorza, 1984); 2) Upright type, which is intermediate between

standard and columnar and is heterozygous for the Br gene that shows incomplete

dominance (Niu et al., 2004).

The third class corresponds to the weeping type (plpl) which is used for

ornamental purposes, and is shorter than standard, compact and semidwarf trees due

to the pendulous nature of its branches, although canopy size is similar to standard type

(Bassi et al., 1994). Weeping peach tree branches contain less lignin in the proximal

part of the shoot and the formation of secondary xylem is delayed compared to standard

branches resulting in less physical support and downward growth (Shen et al., 2008).

There are also interactions between these growth forms (Scorza et al., 2002).

Dwarfed pillar trees can be obtained after hybridizing dwdw and brbr trees, and compact

pillar trees (Ct_brbr) that have potential for ornamental use.

Some commercial peach cultivars with these altered architectures have been bred

and released; however, they have had little impact commercially. Other than use of

these single gene growth variants, little effort has been made to understand the genetic

control of tree architecture or branching in peach. At the University of Florida Peach

Breeding Program, previous observations have detected the existence of twiggy (more

25

branching) and non twiggy (less branching) standard peach genotypes that would

require less pruning, besides branching differences in peach relatives such as almond

(P. dulcis (Mill.) D.A. Webb) and kansu peach (P. kansuensis Rehder). Branching in

peach depends on physiological functions, but intensity levels vary from one cultivar to

another; the environment (light, humidity, nutrition, temperature and plant density) alters

plant architecture but it is mainly ruled by the genetics of the plant (Genard et al., 1994).

On the other hand, peach tree architecture is as well influenced by failure in the

development of axillary meristems that reduces the potential for lateral branching and

later flowering and fruiting (Richards et al., 1994). Bud development failure has been

reported as ‘blind nodes’ in peach and is predominant in subtropical areas with warm

summers (Boonprakob and Byrne, 2003; Richards et al., 1994). Nevertheless there is

genetic variability for incidence of blind nodes in a range of 0-90% depending on the

genotype (Richards et al., 1994; Wert et al., 2007).

The objective of this study was to understand the genetics for branching and blind

nodes in peach interspecific backcrosses by developing a method to evaluate

branching, evaluating distinct parents and progenies and doing QTL and candidate

gene analysis.

26

CHAPTER 2 DEVELOPMENT OF A BRANCHING INDEX FOR EVALUATION OF PEACH

SEEDLINGS USING INTERSPECIFIC HYBRIDS

Introduction

Tools for the evaluation and early selection of architectural traits expressed early

and late in plant development would represent a major advancement for fruit tree

breeders (Laurens et al., 2000).

Several means to depict plant growth or plant models have been developed with

differences in the complexity according to the end use or application. In the last two

decades enormous progress has been made in the fields of agriculture, forestry and

environmental sciences (Fourcaud et al., 2008).

A group of growth models consider environmental factors and their interactions,

physiological processes such as nutrient uptake, photosynthesis and carbon allocation.

This group is known as the process-based model (Battaglia and Sands, 1998). Another

group, the functional and structural models, link growth process to plant morphogenesis

(Fourcaud et al., 2008), where three dimensional reconstruction of plant structures by

AMAP simulation software has been developed. (Prusinkiewicz, 2004). Digital 3-D

representations of tree trunk and branches have been obtained from a single 2-D

picture by means of geometric models in computer graphics (Cheng et al., 2007). The

drawback for these methods is that often calculations are time consuming and are

restricted to a few individuals (Fourcaud et al., 2008).

Simpler crop and forest growth models are used for making predictions on plant

development in agriculture and forestry. Many of these models apply functions to fitted

data without considering physiological processes involved in growth and morphogenesis

27

and can be used efficiently in breeding programs where hundreds or thousands of trees

have to be evaluated (Fourcaud et al., 2008).

Analysis of lateral branching has been used to study architectural traits, since

branches determine tree architecture and the shape of the tree crown (Cheng et al.,

2007). In apple, lateral branching variability is heritable and used as a decisive factor for

selection (Godin et al., 1999; Segura et al., 2006).

Plant indexes have been developed to describe branching (Morita and Collins,

1990). An index based on the plastochron development of Glecoma hederacea L. was

used to analyze stolon growth and branching (Birch and Hutchings, 1992). An index

based on length and density or number of first order and second order roots was used

to describe quantitatively the degree of branching in maize roots (Morita et al., 1992).

The hypotheses tested for this study is that a branching index based on the

number of first and higher order branches in a tree, is a tool that can help to evaluate,

characterize and predict branching in Prunus seedling trees.

The aim of this research was to develop a simple, fast, non-destructive, reliable

branching index that could be used to evaluate the architecture of peach seedlings, thus

facilitating the early selection of individuals with desirable tree architecture or less

twiggy phenotypes and for mapping genes related to branching intensity in peach.

Materials and Methods

Branching Index Formula

A branching index (BI) equation categorizing plants into twiggy, intermediate and

non-twiggy phenotypes while at same time maintaining quantitative differences within

these categories was desired. The branching index was specifically designed for the

evaluation of peach and interspecific Prunus seedlings in the high-density fruiting

28

nursery management of the University of Florida stone fruit breeding program. The

branching index value is the multiplication of partial index values for each branching

order existent within the plant: 1st order branches that arise from the main axes, second

order branches arising from first order branches and so on. The branching index (BI)

formula is:

BI =

k

i

nx i

i

1

1002

Where:

x = Absence (x=0) or presence (x=1) of first, second, third, or subsequent order

branches, n = number of branches within a branching order and k = the maximum order

of branching

For illustration purposes we mention the next cases: plants having no branches

have BI values of 1. Plants having only first order branches; first and second order

branches; and first, second and third order branches would have values of 2<BI<4,

4<BI<8, and 8<BI<16, respectively. For example, a plant with a single stem (i.e. no

branches) would result in the equation 2(0+ (0/100)) and yield a value of 1. For a plant with

3 primary branches and no higher order branches, the equation 2(1+ (3/100)) would yield a

value 2.04. The branching index for a plant with 3 primary and 2 secondary branches

would be calculated as 2(1+ (3/100)) x 2(1+ (2/100)) and yield a value of 4.14. Although

permutations of this equation were performed using weights for the presence of

branches (1.5, 2.0, 2.5, and 3) and weights for branch numbers (ranging from 100 to

1000), the first equation was better able to separate the plants into different clusters

29

representing different branching orders showing qualitative contrasts. For this reason,

the first equation was chosen for the index.

Plant Material

The germplasm used to generate the populations for validating the branching

index equation represents 3 sexually compatible Prunus species with differing growth

habits: Kansu peach (P. kansuensis Rehder), almond (P. dulcis (Mill.) D.A. Webb) and

peach (P. persica (L.) Batsch). Kansu peach, a wild peach relative, has a dense

canopy with profuse branching under the growing conditions of the southeastern United

States. In contrast, almond has reduced branching, an open tree canopy and can

produce short branches or spurs. Commercial peach germplasm typically has a

branching architecture that is intermediate to the two described previously, but includes

both twiggy and non-twiggy phenotypes.

F1 hybrid populations of peach X P. kansuensis and peach x P. dulcis were

generated and planted in Gainesville, Florida; 0.5 meters apart in a single row. The

plants were lightly pruned the first growing season by cutting off suckers from the basal

part while the main axis was maintained throughout the entire experiment.

Data Collection

Data was collected on the total number of branches represented by total number

of tips, number of first order branches, and the number of second, third and fourth order

branches from three randomly selected first order branches on 2- and 3-year-old

seedlings of ‘UF97-47’ peach x ‘Tardy Nonpareil’ (TNP) almond, ‘Okinawa’ (Oki) peach

x P. kansuensis ‘A’, ‘Flordaguard’ (FG) peach x P. kansuensis ‘A’, and FG peach x TNP

almond. These sample branches were located at the basal, intermediate and upper third

of the tree in years 2007 and 2008. Trees were between 1.5 and 2 meters tall when 2

30

years old and ranged from 4 to 5 meters tall when 3 years old. The 1st, 2nd, 3rd and 4th

order branching data were used to calculate an index value to estimate and predict the

number of branches.

A comparison of the accuracy of branching index based on one branch per tree

(randomly selected in the basal, intermediate or upper third of each tree) versus the

mean of three branches per tree was performed to determine if data from only one first

order branch were sufficient to obtain a good correlation between the branching index

and the total number of branches represented by total number of tips.

Statistical Analysis

The statistical analysis consisted of power regression, analysis of variance, Tukey

mean test between the different families evaluated and the clusters formed by trees that

reached the same branching order (1st, 2nd, 3rd and 4th order) for both years. The

statistical analysis was performed using SAS®, version 9.1.

Results and Discussion

Results showed that ‘UF97-47’ peach x ‘Tardy Nonpareil’ (TNP) almond and

‘Flordaguard’ (FG) peach x TNP hybrids had the fewest mean total number of branches

and lowest branching index in both years (Table 2-1 and Figure 2-1). Peach x TNP

hybrids also had fewer 1st and 2nd order branches in 2007 and fewer 1st, 2nd and 3rd

order branches in 2008. Therefore, branch production in peach x almond hybrids is

reduced at all levels (Figure 2-2). These results agree with casual observations in

Byron, GA where peach x almond F1 hybrids tend to have reduced branching when

compared to peach seedlings. Gradziel et al. (2002) have also reported that branching

is suppressed in current and previous seasons’ growth in ‘Nonpareil’ almond x peach

hybrids and that the trait is dominant in crosses. Although the mean total number of

31

branches was higher in ‘UF97-47’ x TNP than in FG x TNP in both 2007 and 2008, the

means were not significantly different. Among peach x almond hybrids, analysis of 1st,

2nd and 3rd order branch data indicated that ‘UF97-47’ x TNP had the most 1st order

branches in both 2007 and 2008, and the most 2nd and 3rd order branches in 2008, yet

the differences were significant only for 2007 1st order branch data.

The largest number of total branches and branching index was observed in peach

x P. kansuensis ‘A’ hybrids (Table 2-1 and Figure 2-3). Increased branching was also

observed for higher order branching in P. kansuensis hybrids (Table 2-2). The 3rd order

cluster in 2007 and the 4th order cluster in 2008 were composed entirely of peach x P.

kansuensis hybrids (Figure 2-1). In addition all but three data points in the 3rd order

cluster for 2008 were peach x P. kansuensis hybrids. ‘Okinawa’ (Oki) peach x P.

kansuensis F1 hybrids had the greatest total branches in both 2007 and 2008.

However, Oki x P. kansuensis was significantly different from FG x P. kansuensis only

in 2007. Comparison of the FG x TNP and FG x P. kansuensis F1 families shows that

the FG x P. kansuensis F1 family had greater than 4-fold the mean total number of

branches in both 2007 and 2008, and approximately 5-fold more 1st order and 2-fold

more 2nd order branches than the FG x TNP family.

FG can be compared to ‘UF97-47’ as both were crossed to TNP. FG can also be

compared to Oki as both were crossed to P. kansuensis. In these comparisons, FG

consistently produced progeny with the smallest mean total number of branches and

lowest branching index value when compared to ‘UF97-47’ and Oki for both the TNP

and P. kansuensis F1 hybrids. FG is characterized by long, whippy branches and

reduced branching (Sherman et al., 1991), while Oki and ‘UF97-47’ produced shorter

32

and twiggier branches. The variation in branching observed in the hybrid populations

indicates that there is a large genetic component to branching propensity in peach and

related species. Genard et al. (1994) reported that branching in peach depends on

physiological factors, although induction levels varied from one cultivar to another.

Similarly, the number of lateral shoots in apricot was significantly affected by genotype

in cumulative data for the first two years while three years were required to differentiate

between tree architectures (Legave et al., 2006). They proposed that it is necessary to

observe the expression of genetic contrasts in growth and branching on appropriate

parts of the limbs, and use the early expression of these traits to allow early selection of

preferred tree architecture (Legave et al., 2006).

The values generated by the branching index equation plotted against the total

number of meristem tips grouped the progeny into clusters of seedlings that were

differentiated by the presence or absence of 1st order, 2nd order, 3rd order and 4th order

branches (Figure 2-1). The 1st order cluster in 2007 and the 1st and 2nd order clusters in

2008 consisted entirely of peach x TNP hybrids. The 3rd order branching cluster in 2007

and the 4th order branching cluster in 2008 consisted of peach x P. kansuensis hybrids.

Only the 2nd order branching cluster for 2007 and the 3rd order branching cluster for

2008 contained both peach x TNP almond and peach x P. kansuensis hybrids.

Although the distributions of the observed total number of branches overlapped

between clusters, the mean total number of branches was different between the first,

second and third order groups in 2007 and between the second, third and fourth order

groups in 2008 (Table 2-3). The mean number of branches in the first order and second

order branching groups were not significantly different in 2008. This lack of significance

33

in 2008 between the first order and second order groups may have resulted because

only three trees had not undergone second order branching in the sampled limbs. The

first order branching cluster consisted of trees with index values between roughly 2 and

4, second order branching cluster between 4 and 8, third order branching cluster

between 8 and 16, and fourth order branching cluster greater than 16 (Figures 2-4 and

2-5).

The regression lines for 2007 (Figure 2-4a) and 2008 (Figure 2-5a) demonstrate

the general trend of the groups generated by the index. The regression between the

index values and the total number of apical meristems were 0.72 and 0.78 for 2007 and

2008, respectively. However, these regression lines do not show the within cluster

progression of branch number as the index values increase. In 2007, trees that

developed first, second and third order branching were tightly grouped. It was possible

to fit regression lines with high r2 values, indicating that the index values were good

predictors of branching intensity within each cluster (Figure 2-4b). The number of plants

with third order branches increased and plants with fourth order branches appeared in

2008 (Figure 2-5b). Index values increased from 2007 to 2008 as the trees increased in

size and complexity (Figures 2-4a and 2-5a). Furthermore, branch index values became

more dispersed as branch number and branching complexity increased resulting in a

decrease in the goodness of fit of the 2008 regression lines (Figure 2-5). However, the

lowest index values within each branching cluster identified the plants with the fewest

number of branches. The regression of the 2007 branching index values and the total

number of branches observed in 2008 was 0.71 (Figure 2-6), indicating that the index

values generated in 2007 were good predictors of branching in 2008.

34

Counting the number of 1st order, 2nd order, and 3rd order branches on each

seedling requires a significant time investment. Therefore, we compared the linear

regressions generated by using data from each of the first order branches sampled

versus the mean of three sampled branches in 2008. The results indicate that the three-

branch data (Figure 2-5a) generates index values that are better estimators of tree

branching than individual branch data for a basal, intermediate and upper branch

(Figures 2-7a, 2-7b and 2-7c , respectively). Among individual branches, those in the

basal and upper third of the canopy (Figures 2-7a and 2-7c) give more accurate indexes

(r2 = 0.62 and 0.65) than those in the intermediate third of the canopy (r2 = 0.52) (Figure

2-7b). The upper branch gives the highest regression value and the best separation of

the branching clusters of the individual branches (Figure 2-7c).

In conclusion, the developed index revealed differences in branching patterns

among interspecific hybrids over two growing seasons and the index values calculated

in two-year-old trees were good predictors of the number of branches observed in the

third year. Furthermore, the index was more precise when three first order branches

were sampled per tree rather than one. The results indicate that selection of trees with

an index value below 8 at two years and below 12 at three years of age would select

trees with reduced branching. Operationally, the index would be based on the mean

from three first order branches and used to select trees within the branching clusters

generated by the index. The lowest index values within each branching cluster typically

have the fewest branches.

This index could be used in peach breeding as a quantitative way of selecting

individuals with decreased branching and less twiggy tree architecture. Further

35

research is necessary to evaluate the ability of this index to predict branching in larger

trees. However, the close tree spacing used in this experiment will prevent the further

analysis of this population due to shading and competition between trees.

36

Table 2-1. Mean total branch number per tree and branching index values in peach x almond and peach x P. kansuensis F1 hybrid populations in years 2007 and 2008.

Familyz 2007 2008 Branches

(#) Branching index value

Branches (#)

Branching index value

Oki x P. kan 97.20 ay 10.34 a 245.16 a 19.56 a FG x P. kan 63.80 b 8.32 b 235.33 a 13.20 a ‘UF97-47’ x TNP 15.63 c 2.48 c 45.00 b 5.06 b FG x TNP 5.80 c 2.44 c 26.75 b 5.05 b z (TNP) ‘Tardy Nonpareil’, (FG) ‘Flordaguard’, (P. kan) P. kansuensis and (Oki) ‘Okinawa’. y Means followed by different letters are significantly different, Tukey (P≤ 0.05). Table 2-2. Mean number of first order branches, and the mean number of second, third

and fourth order branches in three first order branches sampled per tree of peach x almond and peach x P. kansuensis F1 hybrids.

Familyz Year Progeny evaluated (#)

First order (#)

Second order (#)

Third order (#)

Fourth order (#)

2007 Oki x P. kan 10 26.40 ay 8.36 a 2.00 a 0.00 FG x P. kan 10 22.40 a 5.76 a 1.33 a 0.00 ‘UF97-47’ x TNP 8 11.37 b 2.73 b 0.00 a 0.00 FG x TNP 5 4.40 c 2.60 b 0.00 a 0.00 2008 Oki x P. kan 31 43.23 a 25.17 a 13.96 a 0.65 a FG x P. kan 8 32.22 b 15.26 b 12.41 a 0.11 a ‘UF97-47’ x TNP 8 19.88 c 4.92 c 2.46 b 0.00 a FG x TNP 4 12.25 c 3.25 c 0.25 b 0.00 a z (TNP) ‘Tardy Nonpareil’, (FG) ‘Flordaguard’, (P. kan) P. kansuensis and (Oki) ‘Okinawa’. y Means followed by different letters are significantly different, Tukey (P≤ 0.05).

37

Table 2-3. Mean total branch number per tree and branching index means for first, second, third and fourth order branching clusters in peach x almond and peach x P. kansuensis F1 hybrid populations in years 2007 and 2008.

Branching clusterz

2007 2008

Branches (total #)


Branches (total #)


First order 11.33 cy 2.12 c 11.64 c 2.15 c Second order 42.80 b 4.72 b 43.00 c 4.70 c Third order 83.65 a 10.09 a 194.79 b 12.85 b Fourth order 0.00 ND x 314.01 a 27.78 a z Cluster was assigned upon the branching order reached by the tree. y Means followed by different letters are significantly different, Tukey ( P≤ 0.05). x ND=No data, no trees reached fourth order branching in year 2007.

38

0

50

100

150

200

0 2 4 6 8 10 12 14


Me

riste

m t

ips (

#)

97-47 X TNP FG X Pkan Oki X PKan FG X TNP

1st order 2

nd order 3

rd order

A

B

Figure 2-1. Branching index values generated for A) year 2007 and B) 2008 in ‘UF97-47’ peach x ‘Tardy Nonpareil’ (TNP), ‘Okinawa’ (Oki) x P. kansuensis ‘A’ (P. kan), ‘Flordaguard’ (FG) x P. kansuensis ‘A’ and ‘Flordaguard’ x ‘Tardy Nonpareil’. Vertical lines show borders between trees having different branching order.

0

100

200

300

400

500

0 5 10 15 20 25 30 35 40


Meriste

m tip

s (

#)

97-47 X TNP Oki X P kan FG X P kan FG X TNP

1st order 2

nd order 3

rd order 4

th order

39

Figure 2-2. Reduced branching typical of a three-year-old ‘Flordaguard’ peach x ‘Tardy Nonpareil’ almond hybrid in winter of 2008. Photo courtesy of author.

Figure 2-3. Profuse branching typical of a three-year-old ‘Flordaguard’ peach x P. kansuensis hybrid in winter of 2008. Photo courtesy of author.

40

y = 3.0393x1.4166

r2 = 0.7198

0

50

100

150

200

0 2 4 6 8 10 12 14


Me

riste

m tip

s (

#)

y = 2E-05x17.018

r2 = 0.5728

y = 3E-05x9.0011

r2 = 0.8976

y = 3E-05x6.422

r2 = 0.7615

0

50

100

150

200

0 2 4 6 8 10 12 14


Me

riste

m tip

s (

#)

1st order

1st and 2nd order

1st, 2nd, and 3rd order

A

B

Figure 2-4. Branching index values generated for year 2007 in ‘UF97-47’ x ‘Tardy Nonpareil’, ‘Okinawa’ x P. kansuensis ‘A’, ‘Flordaguard’ x P. kansuensis ‘A’ and ‘Flordaguard’ x ‘Tardy Nonpareil’. A) regression line generated for the entire 2007 data set. B) regression lines generated for the 1st, 2nd, and 3rd order clusters formed by the index.

41

y = 4.6758x1.343

r2 = 0.7805

0

100

200

300

400

500

600

0 5 10 15 20 25 30 35 40


Meriste

m tip

s (

#)

y = 2.2273x1.4484

r2 = 0.6744

y = 1.5115x1.8821

r2 = 0.352

y = 0.004x5.7412

r2 = 0.8576

0

100

200

300

400

500

0 5 10 15 20 25 30 35 40


Me

riste

m t

ips (

#)

1st order

1st and 2nd order

1st, 2nd, and 3rd Order

1st, 2nd, 3rd, and 4th order

A

B

Figure 2-5. Branching index values generated for year 2008 in ‘UF97-47’ x ‘Tardy Nonpareil’, ‘Okinawa’ x P. kansuensis ‘A’, ‘Flordaguard’ x P. kansuensis ‘A’ and ‘Flordaguard’ x ‘Tardy Nonpareil’. A) regression line generated for the entire 2008 data se. B) regression lines generated for the 2nd, 3rd and 4th order clusters formed by the index.

42

Figure 2-6. Branching index values calculated in 2007 as a predictor for branching number in year 2008 using ‘UF97-47’ x ‘Tardy Nonpareil’ (TNP), ‘Okinawa’ (Oki) x P. kansuensis (P. kan), ‘Flordaguard’ (FG) x P. kansuensis and ‘Flordaguard’ x ‘Tardy Nonpareil’.

A

0

100

200

300

400

0 2 4 6 8 10 12 14

Branching index value 2007

Tip

s 2

00

8 (

#)

97-47 X TNP Oki X Pkan FG X Pkan FG X TNP

y = 9.7655x

1.4004

r2

= 0.7166

0

100

200

300

400

500

0 5 10 15 20 25 30 35 40 45 50 55


Tip

s (

#)

97-47 x TNP Oki x Pkan FG x Pkan FG x TNP

y = 8.1441x

1.1742

r2 = 0.6263

43

B

C

Figure 2-7. Branching index values calculated in 2008. Index value was calculated using total number of first order branches per tree and the number of second, third and fourth order branches within a randomly selected first order branch in A) basal, B) intermediate and C) upper third of the canopy.

0

100

200

300

400

500

0 5 10 15 20 25 30 35 40 45 50 55


Tip

s (

#)


y = 6.307x1.472

r2 = 0.6545

0

100

200

300

400

500

0 2 4 6 8 10 12 14 16 18


Tip

s (

#)


y = 15.19x1.0424

r2 = 0.5262

44

CHAPTER 3 BRANCHING AND BLIND NODE INCIDENCE IN INTERSPECIFIC BACKCROSS

FAMILIES OF PEACH

Introduction

Branching in different Prunus species

Peach is a member of the family Rosaceae, subfamily Prunoidae, within the

subgenus Amygdalus, which includes peaches, peach relatives and almonds although

Prunus mira Koehne, P. kansuensis Rehd., and P. davidiana (Carr.) Franch are

considered to be the most closely related species to peach (Bielenberg et al., 2009).

Amygdalus subgenus members are sexually compatible and produce viable and fertile

F1 hybrids (Mowrey et al., 1990; Martinez-Gomez et al., 2003). Consequently these

species have been used to expand the genetic resources in peach scion and rootstock

breeding for insect, pathogen, and nematode resistance. Interspecific hybrids have also

been used as a source of polymorphisms for genetic studies in peach (Guillaumin et al.,

1991; Gradziel, 2002; Martinez-Gomez et al., 2003; Ledbetter and Sisterson, 2008)

which has a narrow genetic pool (Mowrey et al., 1990; Gradziel, 2002).

Although growth forms in peach, such as dwarf, pillar, weeping and compact have

been studied (Scorza et al., 2006), little effort has been devoted to the study of tree

architecture and branching. The standard peach tree has vigorous acropetal growth,

moderately strong apical dominance and one- year-old fruiting shoots (Marini and

Corelli-Grappadelli, 2006). Nonetheless variation in tree structure can be observed in

peach cultivars released from the University of Florida breeding program. Cultivars such

as ‘UFSun’ and ‘UFOne’ have a spreading growth habit while cultivars such as

‘Flordaprince’ and ‘UFO’ have a more upright growth habit.

45

There is additional genetic diversity for tree structure in closely related Prunus

species that could be used to modify the architecture of peach trees (Scorza and Okie,

1990). For instance, the root-knot nematode resistant rootstock ‘Flordaguard’, which

has remarkably long pendulous branches, is a sixth generation descendant from the

related species P. davidiana C-26712, (Sherman et al., 1991).

P. kansuensis ‘A1’ trees grown in Byron, GA are short statured and highly

branched trees. Peach x P. kansuensis hybrids are vigorous and intermediate in

characteristics, with a higher production of lateral branches than standard peach trees

(Grassell, 1974).

Almond develops lateral branches similar to peach and perennial spurs (Gradziel,

2002) although a great diversity of tree architecture can be found in this species (Kester

and Gradziel, 1990). Gradziel et al. (2002) developed a classification system for branch

architecture in almond based on the suppression of lateral shoot development using

data from both previous and current year growth flushes. ‘Tardy Nonpareil’ almond was

categorized as having limited branching in current and previous season growth,

meaning that laterals developed only on the basal half to two-thirds of the shoot. In

addition, most hybrids of this cultivar tended to express this growth habit, showing that it

is heritable and has a propensity towards dominance of this trait. Analysis of peach x

almond F1s backcrossed to almond indicated that tree size was larger than peach and

the bearing habit was similar to peach but with some prevalence of fruiting spurs

(Gradziel, 2002).

For apricot there is an influence from the genotype in sylleptic branching for three

locations. This influence was greater when considering cumulative effects after three

46

years of growth (Legave et al., 2006). In 1-year-old progeny high broad sense

heritability was found to be 0.54 for number of axillary shoots (Segura et al., 2006;

Segura et al., 2007).

Blind nodes in peach

The occurrence of blind nodes is an additional factor affecting peach tree

architecture and productivity. A blind node is defined as a node lacking axillary flower

and vegetative buds (Boonprakob et al., 1996). Blind nodes can make the training of

young trees difficult and decreases potential yields in areas prone to late frosts where

the crop depends on higher flower bud density to escape from poor fruit set (Richards et

al., 1994).

Differences in blind node frequency among cultivars and locations can have a

large impact on the pruning and potential yield in peach (Wert et al., 2007). A wide

range of blind node frequency has been reported for the University of Florida peach

germplasm, demonstrating that there is genetic variability for blind node incidence, and

breeding against this disorder should be feasible if its mode of inheritance can be

determined (Richards et al., 1994).

Blind node incidence is associated with high temperatures during bud

development in the mid summer (Richards et al., 1994; Boonprakob and Byrne, 2003).

Low chill peaches typically ripen before the summer and the growing conditions are

conducive to rapid growth and high blind node frequency (Byrne et al., 2000).

Higher rates of blind nodes are observed in warmer sites like central and

southwest Florida than in north-central Florida (Wert et al., 2007). Trees grown in the

highlands of the subtropics or coastal climates that have cool summers do not show

blind nodes, but when taken to warm humid climates such as Florida often exhibit this

47

disorder (Richards et al., 1994). Additionally, susceptible varieties do not present blind

nodes in locations with hot dry summers like Sevilla, Spain or Hermosillo, Mexico,

where climatic conditions inhibit vegetative growth during the summer (Byrne et al.,

2000).

Boonprakob et al. (2006) studied anatomical differences between normal and blind

nodes in ‘Earligrande’ and ‘June Gold’ peach in the early spring (March, April and May)

and summer (June, July and August). Early season shoots presented well developed

buds with the procambium connected to the stem and prophyll growth. Late season

shoots presented mostly blind nodes and anatomic observations showed that there

were empty axils with partial development of stem procambium to the position of the

aborted axillary buds. In some cases an axillary meristem was observed but with very

limited growth.

A genetic disorder named noninfectious bud failure (NBF) has been described in

almond (Kester et al., 2004); the disorder is characterized by bud death, rough bark and

erratic budbreak. Similar to peach, the incidence of NBF is associated with increased

mean temperatures in June. The predisposition of a branch to express the bud failure

phenotype increases with the number of vegetative seasons.

A phenotype similar to blind nodes has been described as “foxtail” in pine (Lanner,

1966). The foxtail phenotype is characterized by the lack of or greatly reduced number

of lateral branches and is typically associated with the growth of pine trees in exotic

environments. Pine provenances vary in the expression of the foxtail phenotype and

studies in Pinus caribaea var. hondurensis indicate that it has a heritability of 0.17

(Ledig and Whitmore, 1981).

48

Little is known about the genetics and inheritance of this phenotype for peach

although previous research indicates that highly branched Prunus kansuensis and

reduced branching ‘Tardy Nonpareil’ almond will transmit these features to their

respective progenies (Carrillo-Mendoza et al., 2010). In this research we are testing

that the phenotypic variation for branching and blind nodes is under genetic control.

The objective of this research was to evaluate the branching intensity and blind

node incidence and determine their inheritance in interspecific Prunus backcross

families.


Plant Material

Seven backcross (Table 3-1) families were generated in 2008 and planted in

Gainesville, Florida. The parents were selected based upon their contrasting branching

intensity and blind node incidence. ‘Flordaguard’ rootstock (FG) x Prunus kansuensis ‘A’

(P. kan) hybrids showed higher branch production and lower blind node incidence than

FG x ‘Tardy Nonpareil’ almond (TNP) hybrids. Two selections from P. kan F1 (seedlings

number 3 and 6) and one selection from TNP F1 (seedling number 1260) were used as

male parents. One ‘UF97-47’ x P. kansuensis hybrid was also selected for generating

backcrosses as a male parent. The peach selections ‘AP00-30wbs’, ‘UFSharp’ and

‘UF97-47’ were used as backcross female parents. FG x P. kan and FG x TNP hybrids

backcrossed to ‘AP00-30wbs’ and ‘UFSharp’ are defined as species level backcrosses.

‘UF97-47’ x P. kan backcrossed to ‘UF97-47’ is defined as a genotype backcross.

Plant Management

‘UFSharp’ and ‘UF97-47’ backcross seeds were harvested at maturity and soaked

in 0.4% Captan fungicide solution, placed in a bag of moist perlite, and on the same day

49

of harvest placed into a cold chamber at 7ºC. Breeding selection ‘AP00-30wbs’ has a

fruit development period below 110 days and therefore required in vitro culture to

ensure a high germination frequency. Seeds were removed under aseptic conditions

and cultured in a modified KNOPS germination media (Lyrene, 1980). The cultures

were stratified in a fridge at 7ºC for approximately 8 weeks. Seeds showing radicle

protrusion were planted into cone containers of sphagnum peat and perlite (1:1)

containing 6kg/m3 of 15-9-12 controlled release fertilizer. Seedlings were grown in a

greenhouse at approximately 27ºC for 16 weeks and planted in the field in October

2008 in a complete randomized block design with a total of four blocks. ‘Flordaguard’

rootstocks were planted simultaneously with the backcross seedlings in each block and

were budded in May 2009 with buds from each parent used to generate the

backcrosses.

Pruning consisted of removing basal suckers that might interfere with weed

control. Four fertilizer applications were made during the growing season with 125 g of

10-10-10 fertilizer in 2009 and 200 g in 2010. Weeds were controlled by three

applications of Roundup® each growth season. Plants were irrigated in drought periods

during the growing season at weekly intervals.

Branching Index Data Collection

Data for total number of first order branches and total number of second, third and

fourth order branches from three randomly and representative selected first order

branches located on the lower, intermediate and upper part of each tree were obtained

during the winters of 2010 and 2011. Branching index was calculated for each tree

(Carrillo-Mendoza et al. 2010). Trunk diameter was measured at the crown of the tree

50

with a caliper and tree height with a ruler in the winter of 2010 and 2011 to aid as

covariates for the branching index.

Blind node data collection

Blind node frequency was measured as the percentage of blind nodes from the

total number of nodes in the uppermost 50 nodes of the main axis of the tree, and the

percentage of blind nodes from three randomly selected lateral branches that grew the

previous season. These branches were chosen because they represent the genotype

and genotype x environmental interaction from the previous growing season.

Identification of Selfs

Six SSRs markers used for mapping were also utilized to distinguish and separate

contaminating progeny originating from self-pollination from true backcross progeny

(Appendix, Table A-1).

Genomic DNA was extracted from leaf tissue of all the individuals within each

backcross family (Table 3-1) using the CTAB method (Doyle, 1991), DNA isolation was

confirmed by electrophoresis at 120 volts for 60 minutes on a 1% agarose gel stained

with ethidium bromide and TBE buffer (10.8g Tris Base, 5.5g Boric acid, 4mL 0.5M

EDTA (pH 8.0)). Lambda DNA of 5, 25, 50, and 100ng/µL was loaded next to the

samples to obtain a visual estimate of the concentration of isolated DNA. The gel was

photographed on a transilluminator and afterwards DNA concentration was determined

by spectrophotometry. Genomic DNA from the parents was amplified by polymerase

chain reaction (PCR) using six polymorphic SSRs (Table 3-2) and informative markers

per family that differentiated selfs from true backcrosses. SSR markers that segregated

for alleles that differed by less than 4 base pairs were amplified using labeled primers.

PCR was carried out in a total volume of 10µL containing 1 µL 10X ThermoPol Reaction

51

Buffer (10mM KCl, 10mM (NH4)2SO4, 20mM Tris-HCl, 2mM MgSO4, 0.1% Triton X-100,

pH 8.8 @ 25ºC), 1 µL 5 µM forward primer, 1 µL 5 µM reverse primer, 0.8 µL 2.5mM

dNTP, 0.2 µL Taq DNA Polymerase, 3 µL DNA grade water, and 3 µL approx. 10ng/µL

DNA. PCR reactions were done on an Eppendorf® thermal cycler using the following

cycling parameters: initial denaturation for 3 min. at 94ºC, followed by 40 cycles of 30 s.

at 94ºC, 30 s. at the primer specific annealing temperature (Table 4-1), and 1 min. at

72ºC; and a final extension of 7 min. at 72ºC. PCR products were separated on a 3.5%

electrophoresis agarose gel stained with ethydium bromide at 220 volts and observed

and photographed on a transilluminator to corroborate amplification and to determine

the approximate size of the amplified DNA fragments. Markers segregating for alleles

that differed by more than 6 bp were visualized and genotyped by size separation after

four hour electrophoresis. Markers segregating for alleles that differed by less than 6 bp

were fluorescently labeled and detected by capillary electrophoresis. A 100x-400x

dilution of PCR product was sent to the ICBR Genetics Analysis Laboratory at the

University of Florida for fragment analysis on an ABI® 3730 Automated Sequencer.

Allelic segregation was visualized using the Soft Genetics analysis program

GeneMarker® (SoftGenetics, State College, United States) version1.


ANOVA, Tukey’s multiple comparisons of means, contrast estimates, chi-square,

correlation and regression tests were performed for branching index and blind node

incidence using PROC MIX in SAS® version 9.2. Branching index was analyzed using

blind node incidence, tree diameter and height as covariates to assess the effect of

blind nodes and tree vigor on branching. Blind node incidence data were transformed

using a logarithmic function.

52


Branching Index

Branching intensities among the parents used for developing the backcross

families were not significantly different (P≤ 0.05) after their first season of growth (Table

3-2). Significant differences were observed after the second growth season (Table 3-3).

P. kansuensis hybrids had the highest value, the peach genotypes were intermediate,

and the TNP hybrid had the lowest mean branching index values. The lack of

significance in 2010 could be due to the short growing season the budded parental

trees had in their first growth season. The trees were budded in May 2009 and had only

a 6-month growing season, but there are similar tendencies to the significant results of

2011.

The branching behavior of the parents was transmitted to the different backcross

progenies for branching intensity in 2010 (Table 3-4). P. kan backcrosses presented the

highest and TNP backcrosses had the lowest branching index. P. kan hybrids

backcrossed to ‘UFSharp’ and ‘UF97-47’ ranked higher, P. kan hybrids backcrossed to

‘AP00-30wbs’ were intermediate and TNP hybrids backcrossed to peach ranked lower

for branching. These results are similar to those obtained by Gradziel (2002) where

TNP and its progeny presented limited branching relative to other almonds crossed to

peach and P. webbii progenies. The contrast test (Table 3-5) confirms the previous

result, where the effect for branching from P. kan hybrid offspring was highly significant

(P ≤ 0.001) from that of TNP hybrid offspring. Some differences were found in the

progeny from different peach genotypes in 2010 (Table 3-5). ‘UFSharp’ and ‘UF97-47’

progeny had a higher branching index than ‘AP00-30wbs’. In this case families having

53

TNP were not considered when compared to ‘UF97-47’, since ‘UF97-47’ was not

crossed with TNP hybrids.

Branching index values for the winter 2011 increased in all the backcross families

as a result of an increment in the number of first, second, third and fourth order

branches (Figure 3-1). Few individuals from the ‘UFSharp’ x (FG x P. kan 6) backcross

families in 2010 had generated fourth order branches and more P. kan backcross

progeny presented third order branches compared to TNP backcross progeny.

Branching complexity increased in 2011 with a larger number of progeny having fourth

order branching and some P. kan progeny expressing an even greater branching index

compared to TNP values in this category. Transgressive segregation was found for

branching index in the different backcross families suggesting that peach and almond

may have alleles with different phenotypic effects at many genes controlling branching

(Figure 3-1).

The differences found in 2010 were conserved among the different families in

2011 (P≤ 0.05) (Table 3-6). The FG x P. kan and ‘UF97-47’ x P. kan offspring branched

more profusely than FG x TNP offspring. The orthogonal contrasts show that P. kan

progeny had significantly higher branching (P ≤ 0.001) than TNP (Table 3-7), and that

there were no differences among the peach genotypes even though ‘AP00-30wbs’ had

reduced branching in 2010 when compared to the other two selections. In general, P.

kan progeny developed more profuse branching, and almond progeny developed less

complex and lower branching. These results imply the potential of TNP or similar

almonds in breeding for less twiggy peach trees that can be easier to prune (Gradziel,

2002).

54

The covariates, blind node incidence and tree height did not appear to have a

significant effect on branching index (P≤ 0.05). However, tree trunk diameter was

significant. In addition, a positive and moderate relationship between tree diameter and

branching (r=0.45, P≤ 0.05) was found. Conversely, tree height had no association with

branching (r=0.05, P> 0.05). These results demonstrate that there is an association

between tree diameter and branching intensity. Additionally, there were significant

differences (P≤ 0.05) for tree trunk diameter among the backcross families (Tables 3-8

and 3-9), where the ‘UFSharp’ x (FG x P. Kan) backcross family had the greatest

average diameter. However, no significant differences were detected for tree height,

demonstrating that there is a contribution from genotype to trunk diameter as well as

branching. Field observations indicated that some FG x TNP offspring had low

branching even though they were taller than other trees, and in some cases many FG x

P. kan offspring that were profusely branching were not tall but had a large canopy and

occupied a wide space. Blind nodes in the main axis and lateral branches had a

negative but low relationship with branching index (r=-0.19 and -0.21, P≤ 0.05,

respectively). This indicates that presence or absence of vegetative buds is a

component of branching but does not have the greatest impact, since branching can be

also a consequence of bud density, tree vigor and strength of apical dominance.

Blocks did not have a significant effect on branching index, but there was

significant difference (P≤ 0.001) between the two years of evaluation. The differences

were a consequence of the increase in the number and complexity of branches but also

suggest effects from different growing seasons as the two years data correlate

55

moderately (r=0.58, P≤ 0.05). As a consequence, additional growing seasons for

evaluation for branching may be useful.

Blind Nodes

The parents chosen for originating the backcross progeny had significant

differences (P≤ 0.05) in the incidence of blind nodes on the main axis and lateral

branches. The peach selections ‘AP00-30wbs’ and ‘UFSharp’ demonstrated the highest

incidence in 2010 (Table 3-2) and 2011 (Table 3-3), whereas FG x TNP was

intermediate and all P. kan hybrids had the lowest percentage of blind nodes. The

incidence of blind nodes in ‘UF97-47’ peach was similar to that of the FG x P. kan

hybrids in 2010; however, in 2011 the frequency increased to a level similar to the FG x

TNP hybrids, indicating a significant environmental effect.

Backcross families as well as the parents showed significant differences (P≤ 0.05)

for blind nodes in the main axis and lateral branches in 2010 (Table 3-4) and 2011

(Table 3-6). ‘AP00-30wbs’ x (FG x TNP) and ‘UFSharp’ x (FG x TNP) backcross hybrids

presented the highest incidence of blind nodes, ‘AP00-30wbs’ x (FG x P. kan) and

‘UFSharp’ x (FG x P. kan) backcross hybrids were intermediate, and ‘UF97-47’ x

(‘UF97-47’ x P. kan) backcross hybrids had the lowest incidence of blind nodes. Blind

node presence in the main axis and branches are highly correlated (r=0.90 P≤ 0.05) and

often the incidence is slightly higher in the main axis than lateral branches.

The orthogonal contrasts between different parents used to generate the

backcross progeny, confirms the previous results. Differences in the frequency of blind

nodes between P. kan and TNP almond progeny in 2010 (Table 3-5) and 2011 (Table

3-7) were highly significant (P ≤ 0.001). Among the standard peach selections, ‘UF97-

47’ offspring had the lowest (P ≤ 0.001) blind node incidence when compared to ‘AP00-

56

30wbs’ and ‘UFSharp’, indicating that there is intraspecific as well as interspecific

variability for blind node propensity as indicated by Richards (1994).

There was high variability for blind node frequency within the backcross families.

Blind node frequency in lateral shoots of FG x P. kan and FG x TNP progeny ranged

from 0-90% in 2010 and 2011 (Figure 3-2 and Figure 3-3, respectively), showing a

broad segregation for the trait in progeny derived from contrasting parents. Even though

the range of blind nodes was similar for both families, the means were significantly

different in both years, with TNP progeny having a higher mean incidence of blind

nodes than P. kan progeny. The proportion of individuals that had low incidence,

classified as having 0-30% of blind nodes, was around 0.70 in 2010 (Figure 3-2) and

2011 (Figure 3-3) in FG x P. kan backcross populations. In contrast, the proportion of

progeny with a low incidence of blind nodes in FG x TNP was approximately 0.30 in

2010 and 2011. A chi-square test showed that these proportions are significantly

different (P<0.001) between these two families. ‘UF97-47’ x (‘UF97-47’ x P. kan)

backcross had the narrowest range with 100% of individuals in the lower blind node

categories in 2010 and 2011. In contrast to the two previous families, this family had

less phenotypic variation; both of the parents used to generate this backcross had low

average blind node frequency (Tables 3-3 and 3-4). These results suggest that blind

node frequency is under genetic control and that use of parents with low blind node

frequency such as P. kansuensis will produce offspring with fewer blind nodes. Similar

to the branching trait, transgressive segregation was observed in the backcross progeny

again suggesting that peach and P. kansuensis carry different alleles at multiple loci

controlling the blind node trait.

57

There were no significant differences among blocks for blind node frequency.

There were significant differences (P≤ 0.05) for blind nodes in the main axis and lateral

branches among the two years of evaluation, suggesting different responses during

different growing seasons. The overall mean for 2010 in the main axis and lateral

shoots was 28.4 and 23.0 percent, and for 2011 there was an increase to 36.9 and 33.7

percent. Nevertheless, there is an association for the incidence of blind nodes between

the two years of evaluation (r= 0.80, P≤ 0.05). Variation was noticed in some trees,

primarily in progeny with a blind node incidence between 10 to 50%. However, trees

with an incidence of 70% or more were the most constant in both years. These results

indicate that a genotype with high blind node presence will likely have the same

behavior in the following seasons. On the other hand, trees with an incidence of 10 to

70% may require additional time for accurate evaluation. Individuals with the lowest

incidence, 0 to 10%, in 2010 or 2011 rarely had a blind node incidence above 25% in

the other year, demonstrating that selection for the lowest or no incidence range is

necessary. There are no known thresholds for a maximum incidence of blind nodes that

would risk a profitable yield, but selection for fewer blind nodes is necessary in climates

with late spring freezes (Richards et al., 1994).

Heritability for Branching and Blind Nodes

Narrow sense heritability was calculated by midparent-offspring regression for

branching index and blind node incidence in main axis and lateral branches. Branching

index narrow sense heritability was 0.37 and blind node heritabilities were 0.21 and 0.20

for main and lateral branches, respectively.

Branching index narrow sense heritability was moderate. Segura et al. (2006)

reported a broad sense heritability of 0.54 for number of sylleptic branches in one-year-

58

old-apples (2007); Segura et al. (2006) reported on one-year-old apricot broad sense

heritabilities of 0.71 for trunk branching, 0.51 for long sylleptic shoot branching and 0.69

for the percentage of branching nodes. Branching and other growth traits heritability

estimates for apple and apricot appear to be very variable confirming the complexity of

these traits (Liebhard et al., 2003; Segura et al. 2007). Different factors such as density

of lateral vegetative buds, percentage of buds that break and elongate shoots and plant

vigor affect shoot branching. Besides, different methods were used to evaluate

branching. Segura et al. (2006 and 2007) dissected the trait in number of sylleptic

branches originated in trunk and long sylleptic shoots, number of shoots per unit of

length and percentage of branching nodes. In this study the branching index was used

as a tool to evaluate the intensity and complexity of branching as an overall mean to

depict tree branching in breeding populations, giving additional explanation for the

differences in heritability estimates. An additional source of variation that affected

heritability was year or the two different growth seasons of evaluation, where significant

differences were found between 2010 and 2011 (P≤0.001).

Despite the complex nature of branching, the data supports that parents with low

branching index will produce lower branching offspring, as almond hybrids produced

significantly lower branching on average backcrosses compared to P. kansuensis

hybrids.

Blind node heritabilities were in the low range, indicating that it is a complex trait.

Complex traits are influenced by many genes and the environment. Previous research

has shown that summer temperatures impact the occurrence of blind nodes (Richards

et al., 1994; Boonprakob et al., 2003). The broad and transgressive segregation

59

observed in the backcross families is also indicative of the complexity of this trait. There

were also significant differences between the two years of evaluation 2010 and 2011

(P≤0.05) that affected heritability.

The peach genotypes ‘AP00-30wbs’ and ‘UFSharp’ had the highest frequency of

blind nodes and contributed to the broad range of segregation in the different progenies.

On the other hand, the male parent’s relatively small differences contributed to the

differences between the families. The narrowest range of segregation was detected in

the ‘UF97-47’ x (‘UF97-47’ x P. kan) backcross population (Figures 3-2 and 3-3) where

both parents had the lowest blind node frequency among standard peaches and

hybrids, and produced the progeny having the lowest mean incidence of this disorder.

These results suggest that breeding for fewer blind nodes is possible by using parents

with lower frequencies of blind nodes, even though eradicating or reducing greatly this

disorder from the breeding program will take several generations and high selection

intensity as indicated by the low heritability estimate.

In conclusion, the P. kansuensis and TNP almond parents and progeny contrasted

in branching and blind node propensity. P. kansuensis parents and progeny showed

higher branching and lower blind node incidence compared to TNP parents and

progeny, demonstrating that the traits are heritable. Low and moderate heritability

estimates reveal that these are polygenic traits that are impacted by the environment.

The data obtained will be used to identify QTLs and candidate genes that can be used

to assist breeding for trees with low incidence of blind nodes and reduced branching.

60

Table 3-1. Total number of progeny and number of contaminating self-pollinated progeny in the interspecific backcross families used for studies in branching and blind nodes. FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis and TNP=‘Tardy Nonpareil’ almond.

Family Individuals (#) Selfs (#)

‘AP00-30 wbs’ x (FG x P. kan 3) 66 8 ‘AP00-30 wbs’ x (FG x P. kan 6) 62 12 ‘AP00-30 wbs’ x (FG x TNP) 78 13 ‘UFSharp’ x (FG x P. kan 3) 85 12 ‘UFSharp’ x (FG x P. kan 6) 99 18 ‘UFSharp’ x (FG x TNP) 126 24 ‘UF97-47’ x (‘UF97-47’ x P. kan) 88 9

Table 3-2. Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL) of the parental genotypes (winter of 2010).

Parent BI BNM (%) BNL (%)

‘Flordaguard’ x P. kansuensis 3 6.8 a z 6.8 b 7.4 b ‘Flordaguard’ x P. kansuensis 6 5.5 a 1.7 b 3.4 b ‘Flordaguard’ x ‘Tardy Nonpareil’ 3.8 a 13.3 ab 11.6 ab ‘AP00-30wbs’ 6.1 a 60.0 a 54.3 a ‘UFSharp’ 6.5 a 44.1 a 48.1 a ‘UF97-47’ 7.2 a 2.5 b 2.9 b ‘UF97-47’ x P. kansuensis 7.8 a 3.3 b 1.7 b z Means followed by different letters are significantly different, Tukey (P≤ 0.05).

Table 3-3. Mean branching index (BI) and blind node incidence values for the main axis (BNM) and lateral branches (BNL) of the parental genotypes (winter of 2011).


‘Flordaguard’ x P. kansuensis 3 21.0 a z 2.2 b 4.0 b ‘Flordaguard’ x P. kansuensis 6 17.8 ab 7.5 b 2.8 b ‘Flordaguard’ x ‘Tardy Nonpareil’ 6.6 b 14.8 ab 19.8 ab ‘AP00-30wbs’ 10.0 ab 53.3 ab 57.0 a ‘UFSharp’ 13.2 ab 77.5 a 71.4 a ‘UF97-47’ 10.5 ab 17.5 ab 16.3 ab ‘UF97-47’ x P. kansuensis 17.8 ab 1.1 b 0.0 b z Means followed by different letters are significantly different, Tukey (P≤ 0.05).

61

Table 3-4. Mean branching index (BI) and blind node incidence for the main axis (BNM) and lateral branches (BNL) in the interspecific backcross families (winter of 2010). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.


‘AP00-30wbs’ x (FG x P. kan 3) 5.7 ab z 20.5 b 18.4 b ‘AP00-30wbs’ x (FG x P. kan 6) 5.6 ab 24.9 b 21.3 ab ‘AP00-30wbs’ x (FG x TNP) 3.9 b 34.5 ab 32.1 a ‘UFSharp’ x (FG x P. kan 3) 7.3 a 18.4 bc 14.5 bc ‘UFSharp’ x (FG x P. kan 6) 8.2 a 23.2 b 18.4 b ‘UFSharp’ x (FG x TNP) 5.2 b 44.4 a 34.4 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 8.3 a 10.7 c 11.5 c z Means followed by different letters are significantly different, Tukey (P≤ 0.05). Table 3-5. Orthagonal contrasts of backcross families by parent for branching index (BI)

and blind node incidence in the main axis (BNM) and lateral branches (BNL) (winter of 2010).

Parent groups contrasted

BI Group mean

P value BNM Group mean

P value BNL Group mean

P value

P. kansuensis ‘Tardy Nonpareil’

7.0 4.0

<0.001 z 19.5 39.4

<0.001 16.8 33.2

<0.001

‘AP00-30wbs’ ‘UFSharp’

5.0 6.9

0.0325 26.6 28.6

0.7838 23.9 22.4

0.2529

‘AP00-30wbs’ ‘UF97-47’

5.6 8.3

0.0119 22.7 10.7

0.0064 19.8 11.5

0.0212

‘UFSharp’ ‘UF97-47’

7.7 8.3

0.0605 26.4 10.7

0.0021 16.4 11.5

0.0465

z Alpha = 0.05 Table 3-6. Mean branching index (BI) and blind node incidence values for the main axis

(BNM) and lateral branches (BNL) in the interspecific backcross families (winter of 2011). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


‘AP00-30wbs’ x (FG x P. kan 3) 17.3 ab z 27.7 bc 24.4 b ‘AP00-30wbs’ x (FG x P. kan 6) 20.7 a 30.1 b 28.4 b ‘AP00-30wbs’ x (FG x TNP) 9.5 b 46.1 ab 43.5 a ‘UFSharp’ x (FG x P. kan 3) 19.8 a 27.9 b 23.3 bc ‘UFSharp’ x (FG x P. kan 6) 18.6 a 30.3 b 26.7 b ‘UFSharp’ x (FG x TNP) 11.9 b 55.9 a 52.4 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 21.3 a 14.1 c 12.9 c z Means followed by different letters are significantly different, Tukey (P≤ 0.05).

62

Table 3-7. Orthagonal contrasts of backcross families by parent for branching index (BI) and blind node incidence in the main axis (BNM) and lateral branches (BNL) (winter of 2011).

Parent groups contrasted

BI Group mean

P value BNM Group mean

P value BNL Group mean

P value

P. kansuensis ‘Tardy Nonpareil’

19.5 10.7

<0.001 z 26.0 51.0

<0.001 23.1 41.8

<0.001

‘AP 00-30 wbs’ ‘UFSharp’

15.8 16.7

0.6902 34.6 38.1

0.6477 32.1 34.1

0.9537

‘AP00-30wbs’ ‘UF97-47’

19.0 21.3

0.1769 28.9 14.1

<0.001 26.4 12.9

<0.001

‘UFSharp’ ‘UF97-47’

19.2 21.3

0.1032 26.4 12.9

<0.001 25.0 12.9

<0.001

z Alpha = 0.05 Table 3-8. Covariates measured in the interspecific backcross families (winter of 2010).

FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.

Parent Trunk diameter (mm)

Tree height (m)

‘AP00-30wbs’ x (FG x P. kan 3) 35.3 ab 2.17 a ‘AP00-30wbs’ x (FG x P. kan 6) 35.5 ab 2.16 a ‘AP00-30wbs’ x (FG x TNP) 30.0 b 2.09 a ‘UFSharp’ x (FG x P. kan 3) 45.6 a 2.40 a ‘UFSharp’ x (FG x P. kan 6) 43.0 a 2.60 a ‘UFSharp’ x (FG x TNP) 37.7 ab 2.69 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 39.4 ab 2.12 a z Means followed by different letters are significantly different, Tukey (P≤ 0.05). Table 3-9. Covariates measured in the interspecific backcross families (winter of 2011).


Parent Trunk diameter (mm)

Tree height (m)

‘AP00-30wbs’ x (FG x P. kan 3) 73.08 ab 5.04 a ‘AP00-30wbs’ x (FG x P. kan 6) 72.32 ab 4.84 a ‘AP00-30wbs’ x (FG x TNP) 62.00 b 4.60 a ‘UFSharp’ x (FG x P. kan 3) 83.66 a 4.85 a ‘UFSharp’ x (FG x P. kan 6) 80.70 a 5.32 a ‘UFSharp’ x (FG x TNP) 73.20 ab 5.44 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 70.07 b 4.76 a z Means followed by different letters are significantly different, Tukey (P≤ 0.05).

63

A

B

Figure 3-1. Branching index values of interspecific backcross progenies in A) 2010 and

B) 2011. Vertical lines show borders between trees having different branching orders. Arrows indicate blind node parental F1 hybrid mean (F1), backcross family mean (BC) and parental peach selection mean (‘UFSharp’ or ‘UF97-47’). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.

1st order 2

nd order 3

rd order 4

th order

‘UF97-47’ x (‘UF97-47’ x P. kan) ‘UFSharp’ x (FG x TNP) ‘UFSharp’ x (FG x P. kan 6)

0 5 10 15 20 25 30 35 40 45 50

Branching index 2010

F1

F1

F1

BC

BC

BC

‘UF97-47’

‘UFSharp’

‘UFSharp’

0 5 10 15 20 25 30 35 40 45 50

Branching Index 2011

‘UF97-47’ x (‘UF97-47’ x P. kan) ‘UFSharp’ x (FG x TNP) ‘UFSharp’ x (FG x P. kan 6)

1st order 2

nd order 3

rd order 4

th order

F1

F1

F1 ‘‘UFSharp’

‘UFSharp’

‘UF97-47’ BC

BC

BC

64

Figure 3-2. Distribution of blind node incidence in lateral branches within interspecific backcross families in 2010. Arrows indicate blind node parental F1 hybrid mean (F1), backcross family mean (BC) and parental peach selection mean (‘UFSharp’ or ‘UF97-47’). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.

‘UFSharp’ x (FG x TNP)

‘UF97-47’ x (‘UF97-47’ x P. kan)

Incidence of blind nodes (%)

0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

Indi

vidu

als

(%)

0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

Indi

vidu

als

(%)

0

20

40

60

80

0 10 20 30 40 50 60 70 80 90


Indi

vidu

als

(%)

Indiv

idua

ls (

%)

‘UFSharp’ x (FG x P. kan 6)

F1 BC ‘UFSharp’

F1

BC

BC

F1

‘UFSharp’

‘UF97-47’

65

Indiv

idua

ls (

%)


‘UFSharp’ x (FG x P. kan 6) 0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

Indi

vidu

als

(%)

‘UFSharp’ x (FG x TNP) 0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

Indi

vidu

als

(%)

‘UF97-47’ x (‘UF97-47’ x P. kan) 0

20

40

60

80

0 10 20 30 40 50 60 70 80 90

Incidence of blind nodes(%)

Indi

vidu

als

(%)

BC

BC

‘UFSharp' F1

F1

F1

BC BC

‘UFSharp’

‘UF97-47’

Figure 3-3. Distribution of blind node incidence in lateral branches within interspecific backcross families in 2011. Arrows indicate blind node parental F1 hybrid mean (F1), backcross family mean (BC) and parental peach selection mean (‘UFSharp’ or ‘UF97-47’). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.

66

CHAPTER 4 MAPPING CANDIDATE GENES AND QTLs ASSOCIATED WITH BRANCING AND

BLIND NODES IN Prunus sp. INTERSPECIFIC BACKCROSS FAMILIES

Introduction

Breeding for unconventional fruit tree architecture that would reduce production

costs and increase yield potential has been very limited. Progress in this area has been

hindered by a poor understanding of the genetic control of these traits (Segura et al.,

2007). There is genetic diversity for tree structure in closely related Prunus species that

could be used to modify the architecture of peach trees (Scorza and Okie, 1990). For

instance the rootstock ‘Flordaguard’, which has long pendulous branches, is a sixth-

generation descendant from P. davidiana and peach (Sherman et al., 1991). P.

kansuensis ‘A1’ and peach hybrid trees grown in Byron, GA are short statured and

highly branched trees. Almond (P. dulcis) develops lateral branches similar to peach

and perennial spurs (Gradziel, 2001) although a great diversity of tree architecture can

be found in this species (Kester and Gradziel, 1990); for instance, ‘Tardy Nonpareil’

almond and its hybrids have limited branching in current and previous growth season.

In addition to increasing phenotypic variability, interspecific hybridization in Prunus

has been used to facilitate genetic studies by raising the percentage of informative

markers (Scorza and Okie, 1990; Laurens et al., 2000; Martinez-Gomez et al., 2003).

Cultivated peach has been through several genetic bottlenecks and has low levels of

genetic diversity (Mowrey et al., 1990; Dirlewanger et al., 2004a). The levels of

polymorphism detected with different markers ranges from ~25-35% depending on the

marker system used (Yamamoto et al., 2002).

Formation of axillary lateral buds and consequent lateral bud outgrowth control the

shoot branching pattern, an important factor for plant architecture (McSteen and Leyser,

67

2005). Blind nodes are nodes lacking axillary flower and vegetative buds due to a failure

in the formation of axillary meristems. Blind node incidence is another factor that affects

peach tree architecture and productivity (Boonprakob et al., 1996). There is genetic

variability for blind node incidence, and breeding against this disorder should be feasible

if its mode of inheritance can be determined (Richards et al., 1994).

Genome mapping and quantitative trait analysis of architectural traits in peach can

be the framework for understanding the genetic basis of these and other complex traits

(Tanksley et al., 1989). Previous genetic maps for Prunus serve as a source of markers

and facilitate QTL detection (Chaparro et al., 1994; Dirlewanger et al., 2004a). Markers

associated closely with advantageous traits can be used to select parents for crosses

and cull undesirable progeny from crosses soon after germination, reducing time,

expense and effort of maintaining and evaluating larger number of offspring (Bernardo,

2008; Bielenberg et al., 2009).

QTL analysis for branching has been done in several species. Mapping of the

branching locus (b1) in sunflower identified 15 associated RFLP markers and provided

an opportunity for marker-assisted selection for branching sunflower plants that can be

used as restorer lines (Rojas-Barros et al., 2008). Three significant QTLs were detected

for the number of lateral branches in cucumber with additive variation for each QTL

ranging from 1.6 to 29.5% (Li et al., 2008). Four QTLs for the total number of sylleptic

branches were detected in apple, which explained 64% of the observed variability,

suggesting a strong and complex genetic control for branching.

An alternative approach for identifying the genes controlling a trait is the candidate

gene approach where previous knowledge of biochemical and signaling pathways

68

involved in detectable changes in the phenotype can supply a list of genes that can be

mapped and tested for association with the trait (Kloosterman et al., 2010).

In the past decade, branching mutants have been used to identify the genes

responsible and elucidate metabolic pathways responsible for plant architecture

differences in Arabidopsis and other species (Figure 4-1).

An apical source of auxin as well as active polar auxin transport are required for

apical dominance, AXR-1 (auxin resistant) protein is required for response to auxins in

Arabidopsis; axr-1 mutants display increased lateral branching and other pleiotropic

effects such as defects in stem elongation and lateral root formation (Stirnberg et al.,

1999). The axr-1 mutants have a defect in auxin regulated transcription and do not

respond to the application of exogenous auxin (McSteen and Leyser, 2005). In

Arabidopsis there is a negative regulation of cytokinins mediated by AXR1 (Nordstrom

et al., 2004). Auxin regulation of the strigolactone and branching related genes MAX3

and MAX4 occurs by the intervention of AXR1 (Brewer et al., 2009).

Auxin transported from the apex does not enter the bud directly, suggesting the

existence of a second branching inhibitor messenger (Brewer et al., 2009). Candidate

genes for the branching inhibitor came from profuse branching mutants with fewer

pleiotropic effects, max (more axillary growth) in Arabidopsis, rms (ramosus) in pea, dad

(decreased apical dominance) in petunia and htd (high tillering dwarf) in rice (Bennett et

al., 2006). MAX1 encodes a cytochrome P450 family member and acts as an

intermediary between MAX 3-4 and MAX2 in synthesizing a carotenoid derived

hormone that inhibits branching (Booker et al., 2004). Carotenoid-cleaving dioxygenase

7 (CCD7) and carotenoid-cleaving dioxygenase 8 (CCD8) are encoded by

69

MAX3/RMS5/HTD1) (Johnson et al., 2006) and by MAX4/RMS1/DAD (Arite et al.,

2007), respectively. MAX2/RMS4 encodes a leucine-rich repeat F box protein which is

involved in the transduction of a branching inhibitor from MAX1 at the end of the MAX

pathway and acts only in the shoot meristem (Booker et al., 2004; Arite et al., 2007).

Carotenoid cleavage enzymes are involved in biosynthesis of strigolactones

(Matusova et al., 2005). Gomez–Roldan (2008) et al. and Umehara et al. (2008) found

that max3 and max4 in Arabidopsis, rice and pea mutants were defective in

strigolactone production and that exogenous application of strigolactones restored wild-

type reduced branching demonstrating the important role of strigolactones in branching

regulation.

In monocots, genes arresting bud development have been identified. Teosinte

branched1 (tb1) from maize (Doebley et al., 1997), is considered responsible for the

differences in the suppression of axillary branches between maize and its wild ancestor

teosinte. TB1 encodes transcription factors containing a TCP domain that regulates cell

division (Cubas et al., 1999) and inhibits branching without pleiotropic effects.

Branched1 (BRC1) and Branched2 (BRC2) from Arabidopsis show sequence homology

to TB1 and also control bud development. Expression patterns of BRC1 and BRC2

indicate that they are expressed primarily within the bud. Mutant and expression

analyses show that BRC1 and BRC2 are downstream of the MAX pathway and

transcription is affected by environmental stimuli, such as high density, where less

branching corresponded to more than double BRC1 mRNA expression (Aguilar-

Martinez et al., 2007). In tomato two Arabidopsis BRC1 like paralogues have been

70

identified, SlBRC1 and SlBRC2, which are expressed in arrested axillary buds (Martin-

Trillo et al., 2011).

The supershoot (SPS) gene from Arabidopsis encodes a cytochrome P450

involved in axillary meristem formation and development. SPS mutants show an over-

proliferation of branches and higher cytokinins levels (Tantikanjana et al., 2001).

Mutants defective in the formation of axillary meristems have been characterized

(Figure 4-1) (Wang and Li, 2006).

PIN1 (pin-formed) is an auxin efflux carrier that develops auxin gradients in the

shoot apical meristem and subsequent formation of axillary meristems (Benkova et al.,

2003). The pin1 mutant is unable to form lateral organs but the mutant phenotype can

be complemented by exogenous application of auxin in Arabidopsis and tomato

(Reinhardt et al., 2000).

Revoluta (REV) is required for initiation of floral and shoot lateral meristems. REV

encodes a homeodomain/leucine zipper transcription factor, and in rev mutants there is

a complete absence of meristem activity in leaf axils (Otsuga et al., 2001). Tomato (Ls)

and Arabidopsis (LAS) lateral suppressor and rice (MOC1) monoculm1 are orthologous

genes (Wang and Li, 2008), LAS is a member of the GRAS (giberellin-insensitive (GI),

repressor of GA1-3 (RGA) and scare-crow (SCR)) transcription factor family and mutants

for these genes fail to produce axillary meristems during vegetative development (Greb

et al., 2003; Li et al., 2003). Expression analysis showed that LAS acts upstream of

REV in axillary meristem development (Greb et al., 2003).

CUC1, CUC2 and CUC3 (cup-shaped cotyledons) and CUC3 orthologous CUP

(cupuliformis) in Anthirrinium and NAM (no apical meristem) in petunia encode

71

transcription factors within the transcription factor NAC gene family (no apical meristem)

(NAM). Arabidopsis transcription activactor/factor (ATAF) and cup-shaped cotyledon

(CUC) have redundant functions and are involved in the regulation of embryonic stem

meristem formation in the axils of cotyledons and organ boundaries for the development

of separated organs besides formation of axillary meristems (Souer et al., 1996; Hibara

et al., 2006; Kwon et al., 2006; Hasson et al., 2011). CUC2 and CUC3 overlap in

function but are upstream of LAS in Arabidopsis whereas the role of CUC1 has not

been well elucidated in this process (Hibara et al., 2006).

The hypothesis tested in this study is that the genes involved in meristem

formation and outgrowth in model systems are also involved in controlling branching

and blind node development in Prunus.

The objective of the present study is to identify QTLs and candidate genes

associated with branching and blind nodes in Prunus. The identification of associated

markers will permit marker assisted selection for trees with better tree architecture and

reduced incidence of blind nodes in peach.


Plant Material

Seven backcross (Table 4-1) families were generated and planted in Gainesville,

Florida, in October 2008 in a complete randomized block design with a total of four

blocks. Clonally propagated trees of the parents used to derive the backcrosses were

planted in each block.

The parents of the backcrosses were selected based upon their contrasting

branching intensity and blind node incidence. ‘Flordaguard’ rootstock (FG) x Prunus

kansuensis ‘A’ (P. kan) F1 hybrids showed higher branch production and lower blind

72

node incidence than FG x ‘Tardy Nonpareil’ almond (TNP) F1 hybrids. Two selections

from the P. kan F1 hybrid population (seedlings number 3 and 6) and one selection from

TNP F1 hybrid population (seedling number 1260) were used as male parents. One

‘UF97-47’ x P. kansuensis hybrid was also selected for generating backcrosses as a

male parent. The peach selections ‘AP00-30wbs’, ‘UFSharp’ and ‘UF97-47’ were used

as backcross female parents. FG x P. kan and FG x TNP hybrids backcrossed to

‘AP00-30wbs’ and ‘UFSharp’ are defined as species level backcrosses. ‘UF97-47’ x P.

kan backcrossed to ‘UF97-47’ is defined as a genotype backcross.

The haploid peaches ‘UF02-01c’ and ‘AP05-18w’ were used as control templates

for the amplification and sequencing of candidate genes. The haploid sequences were

used to determine peach haplotypes.

DNA Extraction

Genomic DNA was extracted from leaf tissue of P. kan, TNP almond, FG peach,

‘AP00-30wbs’ peach, ‘UFSharp’ peach, ‘UF97-47’ peach, FG x TNP, FG x P. kan,

‘UF97-47’ x P. kan, haploid ‘UF02-01’, haploid ‘AP05-18w’ and the individuals within

each backcross family (Table 4-1) using the CTAB method (Doyle, 1991). Successful

DNA isolation was confirmed by electrophoresis of diluted DNA samples in a 1%

agarose gel made using TBE buffer (10.8g Tris Base, 5.5g Boric acid, 4mL 0.5M EDTA

(pH 8.0)) and run at 120 volts for 60 minutes before being stained with ethidium

bromide. Lambda DNA standards of 5, 25, 50, and 100ng/µL were loaded next to the

samples to obtain a visual estimate of the concentration of isolated DNA. The gel was

photographed on a transilluminator and the DNA concentration was determined by

spectrophotometry.

73

Candidate Gene Primer Design

A group of candidate genes associated with axillary meristem formation or blind

nodes and shoot outgrowth or branching were selected (Appendix, Table A-2).

Arabidopsis sequences were retrieved from the ICBR GenBank database and BLAST

was carried out using the tblastx option in Phytozome 7.0 (Joint Genome Institute and

Center for Integrative Genomics) with the Arabidopsis gene sequence as query and

peach genome as target. The output peach sequence with the highest score and e-

value was selected. Exon and intron splicing from the peach gene sequence were

determined by means of Phytozome 7.0 or tblastx using the peach sequence as query

and mRNA database as target in the GenBank BLAST application.

Once exon and intron boundaries were determined, Primer3 was used to select

primers that amplified a target containing flanking exon sequence and a single intron

(Appendix, Table A-2).

Branching Index Data Collection

Data for the total number of first order branches; and total number of second, third

and fourth order branches from three randomly selected first order branches located on

the lower, intermediate and upper part of each tree was obtained during the winters of

2010 and 2011. The branching index was calculated for each tree (Carrillo-Mendoza et

al. 2010).

Blind Node Data Collection

Blind node frequency was measured as the percentage of blind nodes from the

total number of nodes in the uppermost 50 nodes of the main axis of the tree, and the

percentage of blind nodes from three randomly selected lateral branches that grew the

74

previous season. These branches were chosen because they represent the genotype

and genotype x environmental interaction from the previous growing season.

Candidate Gene Primer PCR Optimization

The optimum annealing temperature (55-62ºC) for each primer pair was

determined and the PCR reactions run using two haploids and all the genotypes used to

generate the backcross populations. The haploids were included as controls to confirm

the amplification of single loci and to generate reference haplotype sequence data

(Appendix, Tables A-3 to A-16). PCR was carried out in a total volume of 50 µL

containing 5 µL 10X ThermoPol Reaction Buffer (10mM KCl, 10mM (NH4)2SO4, 20mM

Tris-HCl, 2mM MgSO4, 0.1% Triton X-100, pH 8.8 at 25ºC), 5 µL 5 µM forward primer, 5

µL 5 µM reverse primer, 4 µL 2.5mM dNTP, 1 µL Taq DNA Polymerase, 15 µL DNA

grade water, and 15 µL of approx. 10ng/µL DNA. PCR reactions were done on an

Eppendorf® thermal cycler using the following cycling parameters: initial denaturation

for 3 min. at 94ºC, followed by 40 cycles of 30 s. at 94ºC, 30 s. at the primer specific

annealing temperature (Appendix, Table A-2), and 1 min. at 72ºC; and a final extension

of 7 min. at 72ºC. PCR products were run on a 3.5% electrophoresis agarose gel

stained with ethidium bromide at 220 volts and observed and photographed on a

transilluminator to confirm amplification, determine the approximate size of the amplified

DNA fragment and determine if length polymorphisms were present.

Candidate Gene Sequencing

Bands of the PCR products were excised from the gel and the PCR products were

purified with Qiagen MinElute Gel Extraction Kit®. The purified PCR products and

corresponding primers (10uM) were used for sequencing at the University of Florida

ICBR Core Sequencing lab. The sequence identity was confirmed by comparison to

75

reference sequences using the blastn option of NCBI BLAST (National Library of

Medicine).

The parental sequences were aligned using Sequencher® version 5.0 sequence

analysis software (Gene Codes Corporation, Ann Arbor, MI USA). Sequence and

fragment size polymorphisms were detected by comparison of parental sequences

(Appendix, Tables A-3 to A-16).

Genotyping

SSR markers

Genomic DNA from the backcross parents was amplified by polymerase chain

reaction (PCR) using SSR markers selected from the ‘Texas’ almond x ’Earlygold’

peach (T x E) Prunus genomic reference map (Dirlewanger et al., 2004b). Polymorphic

markers spaced at a map resolution of approximately 20cM were selected and PCR

amplified in the backcross progeny (Appendix, Table A-17). PCR was carried out in a

total volume of 10 µL containing 1 µL 10X ThermoPol Reaction Buffer (10mM KCl,

10mM (NH4)2SO4, 20mM Tris-HCl, 2mM MgSO4, 0.1% Triton X-100, pH 8.8 @ 25ºC), 1

µL 5 µM forward primer, 1 µL 5 µM reverse primer, 0.8 µL 2.5mM dNTP, 0.2 µL Taq

DNA Polymerase, 3 µL DNA grade water, and 3 µL 10ng/µL DNA. PCR reactions were

performed in an Eppendorf® thermal cycler using the following cycling parameters:

initial denaturation for 3 min. at 94ºC, followed by 40 cycles of 30 s. at 94ºC, 30 s. at the

primer specific annealing temperature (Table 4-17), and 1 min. at 72ºC; and a final

extension of 7 min. at 72ºC. PCR products were size separated on a 3.5% agarose gel

stained with ethidium bromide at 220 volts and observed and photographed on a

transilluminator to confirm amplification and to determine the approximate size of the

amplified DNA fragments. Markers segregating for alleles that differed by more than 6

76

bp were visualized and genotyped by size separation after electrophoresis for a period

of 4 hours. Markers segregating for alleles that differed by less than 6 bp were

fluorescently labeled and detected by capillary electrophoresis. A 100x-400x dilution of

PCR product was sent to the ICBR Genetics Analysis Laboratory at the University of

Florida for fragment analysis on an ABI 3730 Automated Sequencer. Allelic segregation

was visualized using the Soft Genetics analysis program GeneMarker® (SoftGenetics,

State College, United States) version1.

Data for leaf and flesh color was collected and used as additional markers for

mapping and QTL analysis.

Candidate gene genotyping with restriction enzymes

The candidate gene sequences were scanned for restriction site polymorphisms to

allow the mapping candidate genes via CAPs (cleaved amplified polymorphic

sequences) (Appendix, Table A-18). DNAs representing the backcross parents and the

backcross progeny were included in each CAPs assay. PCRs were first carried out in a

total volume of 10 µL containing 1 µL 10X ThermoPol Reaction Buffer (10mM KCl,

10mM (NH4)2SO4, 20mM Tris-HCl, 2mM MgSO4, 0.1% Triton X-100, pH 8.8 at 25ºC),

1µL 5 µM forward primer, 1 µL 5 µM reverse primer, 0.8 µL 2.5mM dNTP, 0.2 µL Taq

DNA Polymerase, 3 µL DNA grade water, and 3 µL approx. 10ng/µL DNA. PCR

reactions were done on an Eppendorf® thermal cycler using the following cycling

parameters: initial denaturation for 3 min. at 94ºC, followed by 40 cycles of 30 s. at

94ºC, 30 s. at the primer specific annealing temperature (Appendix, Table A-2), and 1

min. at 72ºC; and a final extension of 7 min. at 72ºC. PCR products were run on a 3.5%

agarose gel stained with ethidium bromide at 220 volts and observed and photographed

on a transilluminator to confirm amplification.

77

PCR products were digested with restriction enzymes for 6 hours under the

manufacturers recommended conditions (Appendix, Table A-18). The samples were

genotyped by running on a 2.0% agarose gel stained with ethidium bromide at 160 volts

for four hours and photographed on a transilluminator.

Candidate gene genotyping with high resolution melt analysis

Sequences without informative restriction enzyme recognition sites were

genotyped by high resolution melt analysis (HRMA) (Appendix, Table A-19). Primer3

software was used to select primers to generate SNP containing amplicons less than

200 bp in size. PCR and HRMA were carried out using a Roche LightCycler 480 Real

Time PCR system®. PCR reactions were prepared in a total volume of 10 µL containing

5 µL of Roche High Resolution Melting Master Mix®, 1 µL 25 mM MgCL2, 0.5 µL 4mM

forward primer, 0.5 µL 4mM reverse primer, 1 µL DNA grade water and 2 µL template

DNA. The PCR cycling parameters were: one pre-incubation cycle at 95 ºC for 10

minutes, followed by 55 amplification cycles of 10 s. at 95ºC, 15 s. at the appropriate

annealing temperature (Appendix, Table A-19), and 15 s. at 72ºC. The HRMA cycling

parameters were: one minute at 95ºC, one minute at 40ºC and finally 65 to 95ºC raise

step, followed by a cooling step at 40ºC. The experiment was analyzed using the

LightCycler 480 software® version 1.5 and LightCycler 480 Gene Scanning software®;

normalized melting curves were used to obtain the genotypes. The two parents were

included in each HRMA run to ensure accurate genotyping and confirm the genotype of

each backcross individual.


Linkage maps were constructed with Mapmaker/Exp 3.0 (Lander et al., 1987)

using the SSRs, morphological and candidate gene markers. The Kosambi map

78

function was used to convert recombination frequencies to genetic map distances

(centiMorgan, cM). A minimum LOD of 3.0 and a maximum recombination fraction of

0.4 were used to declare linkage between markers. Linkage groups were formed by the

“group” command. Afterwards, marker order within each linkage group was determined

by relative LOD score using the “compare” and confirmed by the “ripple” command.

After the first map was generated, the genotype data was checked for possible

genotype errors using the Graphical Genotype (GGT) 2.0 software (Berloo, 2008). A

final map was constructed using the corrected genotype. The genotype data was tested

for segregation distortion using Pearson’s X2 test. A homogeneity test based on

Pearson’s X2 test was performed to determine if the marker segregation data from

individual backcross families could be combined for QTL analysis.

QTL analysis of branching index and blind nodes was performed with Windows

QTL-Cartographer version 2.5 (Wang et al., 2011) using the composite interval mapping

function. Background markers, or cofactors, and intermarker positions were selected by

the software program to reduce residual genetic variation from unlinked QTL. Threshold

LOD scores significant at 0.05 and 0.01 were chosen to detect putative QTL based on

significance levels determined by a permutation test (1000 permutations, 0.05% level)

(Churchill and Doerge, 1994).


Phenotypic Differences within and among Backcross Families

There were significant differences (P≤0.05) among the different backcross

populations for branching index and blind nodes in 2010 (Table 4-2) and 2011 (Table 4-

3). FG x P. kan and ‘UF97-47’ x P. kan hybrids backcrosses had more branching and

less blind node incidence whereas FG x TNP backcrosses had fewer branches and

79

more blind nodes. Despite the differences detected between FG x P. kan and FG x TNP

backcrosses, there was a wide segregation for branching and blind nodes within the

families. Branching index values ranged from values of 2 to 25 and 2 to 47 in 2010 and

2011, respectively in FG x P. kan backcrosses while they varied from 2 to 15 and 2 to

47 in 2010 and 2011, respectively for FG x TNP backcrosses. Blind nodes varied from 0

to 90% and 0 to 80% in 2010 and 2011 respectively for FG x P. kan backcrosses while

the frequency varied from 0 to 90% both years for FG x TNP backcrosses. The

proportion of individuals that had low incidence, classified as having 0-30% of blind

nodes, was around 0.70 in 2010 and 2011 for FG x P. kan backcross populations. The

proportion of individuals with low incidence of blind nodes in FG x TNP was

approximately 0.30 in 2010 and 2011. The backcross peach female parents ‘AP00-

30wbs’ and ‘UFSharp’ were intermediate in branching and had the greatest amount of

blind nodes among all the parents. These female parents contributed to the broad range

of segregation in the different progenies. On the other hand, the male parents

contributed to the differences among families, where FG X TNP almond hybrids showed

higher incidence of blind nodes compared to FG x P. kan hybrids. ‘UF97-47’ x (‘UF97-

47’ x P. kan) presented the lowest average and narrowest variation in segregation for

blind nodes (0-30% in both years) among all the families. This same family had the

highest branching frequency with variation of the branching index value ranging from 2

to 15 and 5 to 50 for 2010 and 2011, respectively. These results show that branching

and blind nodes are complex quantitative traits, influenced by multiple genes.

Polymorphism in Branching and Blind Node Candidate Genes

Most candidate gene sequences contained polymorphisms in the regions amplified

(Appendix, Tables A-3 to A-16). Some exceptions for P. kansuensis hybrids were

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PpCUC1 (Prunus persica CUC1) and PpCUC2 where no introns with SNPs could be

successfully found, and PpSPS where only one SNP but inherited from ‘Flordaguard’

peach was found. In ‘Tardy Nonpareil’ almond hybrids SNPs were not detected for

PpCUC3. Low levels of sequence polymorphism for branching candidate genes have

also been reported in Arabidopsis where a panel of 24 accessions from different

geographic regions had no polymorphism for the PINOID gene (Ehrenreich et al., 2007),

PpMAX1 primers produced two fragments when tested in the haploids, suggesting

the existence of gene duplication. The two copies had similar sequences but there was

an indel of 53 bp. The copy having the insertion was used for genotyping in order to

design primers that could amplify only with the presence of the insertion and map one

copy.

The sequences obtained from the different genotypes for branching and blind

node candidate genes (Appendix, Tables A-3 to A-16) made it possible to identify

polymorphisms between ‘Flordaguard’, P. kansuensis ‘A’, and ‘Tardy Nonpareil’; and

track the inheritance of these differences into the hybrids used to generate the

backcrosses for the present study.

There were differences among the different candidate genes for the frequency of

SNPs (Table 4-4), PpMAX 2, 3, 4 and PpSPS genes had the highest frequency of

SNPs. In most of the genes the SNP frequency within the intron regions was higher

than in the exon regions as was expected. Exceptions to this included PpMAX3, which

had a higher high frequency of SNPs in exon regions. The rate of transitions was higher

than transversions in most of the candidate genes, PpSPS and PpMAX4 had the

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highest frequency of transitions and PpAXR1 had the highest frequency of

transversions.

From a genotype perspective, there are noticeable differences for the frequency of

heterozygous positions within the branching and blind node candidate gene sequences

(Table 4-5). Among the different peach genotypes ‘Flordaguard’ and ‘UF97-47’ had the

greatest number of heterozygous positions. ‘Flordaguard’ has P. davidiana in its

pedigree which could explain the higher level of heterozygocity. However, ‘UF97-47’ a

peach selection containing only peach genotypes in its pedigree has a similar frequency

of heterozygocity (1/1000), which is high if compared to the average frequency of SNPs

in the whole genome sequence of three peach genomes (1/40,000) (Ahmad et al.,

2011). The low frequency of SNPs in peach selections and haploids is an indicator of

the low genetic variability within the UF peach breeding program, where most selections

are hybrids and few self-pollinated populations are generated. Therefore it is necessary

to use interspecific crosses to increase variability and permit mapping of segregating

traits. The almond cultivar ‘Tardy Nonpareil’ had a level of heterozygocity similar to

Flordaguard’ and ‘UF97-47’, even though almond is an outcrossing species. All

sequence fragments studied were found to be homozygous in P. kansuensis, a self-

pollinated species (Cao et al., 2011). Almond and P. kansuensis had sequence

haplotypes in 11 out of 14 loci and 12 out of 14 that were not present in the peach

genotypes studied, respectively (Table 4-6). This is manifested in the high number of

heterozygous positions for ‘Flordaguard’ x ‘Tardy Nonpareil’ (FG x TNP) and P.

kansuensis hybrid (FG x P. kan) sequences. These results confirm the power of

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interspecific crosses for genetic studies in peach as they increase the polymorphism

frequency.

Genetic Maps

The peach candidate genes and SSRs were positioned in linkage maps for each

individual FG x P. kan and FG x TNP backcross family (Appendix, Figure A-1).

The homogeneity test did not detect statistical differences (P ≤ 0.05) among the

individual FG x P. kan and FG x TNP backcross families. This permitted the generation

of a combined map from the backcross families sharing the same hybrid male parent

(Figures 4-2 and 4-3).

From all the candidate genes that were segregating, it was not possible to map the

PpMAX1 candidate gene with the linkage criteria used. BLAST analysis of the peach

genome using Phytozome 7.0 (Joint Genome Institute and Center for Integrative

Genomics) indicated the presence of two tandem open reading frames with high

homology to the Arabidopsis thaliana MAX1 gene sequence. PpMAX1 sequences were

physically located in LG1 around 34,892 kbp.

FG x P. kan combined map (Figure 4-2) consisted of 28 SSRs, two morphological

markers and 10 candidate genes. The map represents 87% coverage of the T x E

reference map, the genome map’s size was 433 cM, the average distance between

markers ranged between 9.2 and the maximum distance was 31.2 cM between

markers.

FG x TNP combined map (Figure 4-3) consisted of 26 SSRs, two morphological

markers and 12 candidate genes, the selected SSRs represent 90.6% coverage of the

T x E map, the map’s size was 369.2 cM, average marker interval was 9.2 cM and the

maximum distance was 31.9 cM.

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‘UF97-47’ x (‘UF94-47’ x P. kan) backcross map (Figure 4-4) contained 21 SSRs,

1 morphological marker and 10 candidate genes, the SSRs represent 82% coverage of

the T x E map, the map distance was 320.4 cM, the average distance between markers

was 10.1 cM, and the maximum distance was 25.1 cM.

Regions from the T x E reference map that were not represented in the FG x P.

kan backcross combined map due to the lack of polymorphic markers were at the top of

LG1, the bottom of LG1 where PpMAX4 was located, the top of LG6 where the

candidate gene PpLAS was positioned and the top of LG8. Regions from the T x E

reference map that were not represented by SSRs in the maps of the FG x TNP

backcrosses were on the top of LG1; and the bottom of LG 4 where PpCUC1 and

PpCUC2 were positioned, LG6 and LG8. Cao et al. (2011) reported low levels of

polymorphism in some genomic regions in P. kansuensis ‘Honggengansutao’ x ‘Bailey’

peach backcrosses and used sequence-related amplified polymorphisms (SRAPs) to

improve genome coverage (Cao et al., 2011).

Mapmaker was used to generate seven linkage groups in all the FG x P. kan and

FG x TNP backcross families (Figures 4-2 and 4-3). Linkage groups 1-5 and 7 were

homologous to the same LG from the T x E reference map. However, a chimeric 6/8

linkage group containing portions of LG6 and LG8 from the reference map was

generated. This results from a reciprocal translocation between LG6 and L8 near the Gr

leaf color locus (Jauregui et al., 2001; Yamamoto et al., 2001; Lambert and Pascal,

2011). This 6/8 translocation has been detected only in populations segregating for the

dominant Gr allele for red leaf color (Yamamoto et al., 2001; Dirlewanger et al., 2007;

Lambert and Pascal, 2011), similar to the FG x P. kan and FG x TNP families.

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Although the ‘UF97-47’ x (‘UF94-47’ x P. kan) population does not segregate for

green vs. red leaf, only one polymorphic marker (udp98409) was identified in LG8

(Figure 4-4) and therefore the generated genome map consists of only 7 linkage

groups. Similar difficulties in identifying polymorphic markers in linkage group 8 have

been previously reported (Dirlewanger et al., 2007).

Segregation distortion from the expected 1:1 ratio based on Pearson’s χ2 test was

detected in FG x P. kan backcrosses (Figure 4-2). Markers on LG1 and LG4 showing

segregation distortion favored homozygous individuals, while markers at LG3 and

LG6/8 favored heterozygous individuals.

All loci demonstrating distorted segregation ratios in the FG x TNP backcrosses

favored homozygous genotypes (Figure 4-3).

Four loci showed segregation distortion in the ‘UF97-47’ x (‘UF94-47’ x P. kan)

population (Figure 4-4). The loci on LG1 favored heterozygous genotype while those on

LG6/8 and LG7 favored the homozygous genotype.

The markers or regions where segregation distortion was present where similar in

the FG x P. kan and FG x TNP backcross families, indicating similar causes for the

occurrence of distorted ratios according to the cross. Segregation distortion due to

gametic selection or zygotic lethals resulting from chromosomal rearrangements is

reported to be more frequent when the level of divergence between parents increases

(Kianian and Quiros, 1992). This suggests selection acting against particular genetic

combinations from distant parents (Paterson et al., 1990) or possible mistakes in the

coupling of homologous chromosomes during meiosis I at specific chromosomal regions

(Dirlewanger et al., 2004a). Segregation distortion can also result from selection at the

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post-zygotic level in peach x almond F2 where the genetic differences between these

two species results in hybrid weakness and hybrid breakdown (Foolad et al., 1995).

The proportion of marker loci demonstrating segregation distortion has been

reported to range from 36% (Foolad et al., 1995) to 45% of loci (Joobeur et al., 1998;

Bliss et al., 2002) in F2 peach x almond crosses. In this study, 30, 37 and 12% of the

markers respectively for FG x P. kan, FG x TNP and ‘UF97-47’ x P. kan backcrosses

deviated from expected mendelian ratios, respectively.

In the FG x TNP backcross maps, approximately 60% of the markers

demonstrating segregation distortion where in LG6/8. However, the frequency for

corresponding markers in FG x P. kan was 14-36%. The presence of the self-

incompatible allele from almond causes selection against almond alleles at the pre-

zygotic level around the self-incompatible locus which is located at the bottom of LG6 in

peach and almond (Joobeur et al., 1998; Bliss et al., 2002). Similarly in the present

experiment, the peach homozygotes (peach/peach) were favored over almond

heterozygotes (peach/almond) in loci located at LG 6/8 in FG x TNP backcrosses, in

contrast with FG x P. kan which in the same region the peach/P. kan heterozygotes

were favored over the peach/peach homozygotes. Additionally, chromosomal

rearrangements are reported to lead to segregation distortion in Brassica sp. (Paterson

et al., 1990). In Prunus, all loci surrounding the 6/8 translocation breakpoint presented

segregation distortion (Jauregui et al., 2001; Lambert and Pascal, 2011). In this study

segregation distortion was also detected in the LG6/8 translocation in the FG x P. kan

and FG x TNP backcrosses.

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Branching Index QTLs

Individual backcross family analysis for FG x P. kan backcrosses presented one

QTL at LG2 in 2011 (Appendix, Figures A-2 to A-5). Combined FG x P. kan backcross

families analysis (Table 4-7 and Figure 4-5) confirmed the QTL on LG2 in 2011

(P≤0.01) and detected several minor QTLs in 2010, one in LG3 (P≤0.05), one on LG4

(P≤0.05) and one on LG5 (P≤0.01). The QTLs detected explained 28 and 17% of the

phenotypic variation for branching in 2010 and 2011, respectively.

The difference between the individual family and half-sib combined analysis

demonstrates the increased power from larger samples for identifying QTLs. For

instance, the major QTL for 2011 in LG2 was more significant in the family with larger

number of individuals ‘UFSharp’ x (FG x P. kan 6) (Appendix, Figure A-5) and in the

combined analysis of the families (Figure 4-5) where the LOD score increased

noticeably to 9.9. In addition, QTLs for 2010 that were not previously detected in

individual analysis were detected in the combined analysis.

The differences between 2010 and 2011 in the QTLs discovered were due to

moderate correlation amid the two years’ data (r=0.48, P≤0.05). This moderate

correlation suggests possible differences in branching development among different

growing seasons or during different stages of development of the tree. Therefore, a third

year evaluation is needed to confirm the QTLs detected in the second year and

evaluation during three growing seasons is recommended in future studies.

Individual backcross family analysis for FG x TNP presented one QTL on LG7

(Appendix, Figures A-6 and A-7). Combined FG x TNP backcross family analysis

(Table 4-8 and Figure 4-6) detected one QTL on LG7 (P≤0.01) in 2010 and two QTLs in

2011, one in LG4 and one in LG7 (P≤0.01). The QTLs detected in the combined

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analysis explained 6 and 12% of the phenotypic variation in 2010 and 2011,

respectively.

Similar to the FG x P. kan backcrosses, the QTL identified in LG4 in 2011 was not

present in 2010 (Figure 4-6). The lack of correspondence for all the QTLs was also

paralleled by a moderate correlation (r=0.54, P≤0.05) between the two years of data.

Branch QTL were detected in the ‘UF97-47’ x (‘UF97-47’ x P. kan) backcross

population (Table 4-9 and Figure 4-7) (P≤0.05) only in 2010. The first QTL was on LG2,

close to a QTL detected in the FG x P. kan, but detected in 2011, and a second QTL on

LG5.

The QTL with the largest effect on branching in the FG x P. kan and FG x TNP

backcross families were located in different regions (Figures 4-5 and 4-6). Previous

studies show that different QTLs can be detected in populations with different genetic

backgrounds. QTLs for floral and vegetative budbreak were situated in different

genomic regions in two different apple intraspecific F1 populations, suggesting the

influence of diverse genes on budbreak in different genetic backgrounds (Celton et al.,

2011). A plum pox virus resistance QTL detected in F1 and F2 populations of

‘Summergrand’ nectarine x P. davidiana was not conserved in a ‘Rubira’ peach x P.

davidiana F1 population (Rubio et al., 2010), indicating strong QTL interactions with the

susceptible peach and nectarine parents. In this study, the primary determinants were

‘Tardy Nonpareil’ almond and P. kansuensis ‘A’, since no significant differences were

found between ‘UFSharp’ and ‘AP00-30wbs’ for branching and blind node QTLs.

Previous research shows inconsistency of QTLs in different crops resulting from small

population sizes and sampling error (Tanksley and Hewitt, 1988; Beavis et al., 1991;

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Plomion and Durel, 1996). In many cases different loci can be segregating in different

populations. If the locus is not segregating in a particular population, there is no

segregation data to allow the detection of a QTL at that locus. More stable QTLs are

required to be useful in marker assisted breeding (Celton et al., 2011). Faster progress

in breeding commercial quality peaches would be made if we were able to detect and

select for the branching and blind node QTL segregating within P. persica. However,

the low frequency of marker polymorphism will hinder the task. The QTL found in this

study may be useful in cases where almond and P. kansuensis are being used to

introgress novel traits to peach. Additionally, our data indicates that both traits are under

strong environmental influence and that QTL by environment interactions cannot be

ignored. Therefore, additional research is needed to validate the QTL detected in the

present study.

Blind Node QTLs

Individual backcross family analysis for FG x P. kan backcrosses presented one

QTL on LG1 and LG2 (Appendix, Figures A-8 to A-11). Combined FG x P. kan

backcross family analysis confirmed the QTLs (P≤0.01) on LG1 and LG2 with the

highest LOD scores (Table 4-10, Figure 4-8) although the QTL on LG1 is only

significant for blind nodes in lateral branches in 2010 and 2011, similar to another QTL

detected on LG3. Other significant QTLs (P≤0.05) were identified on LG4, LG5 and

LG6/8, but they were not consistent across years and traits. The detected QTL

explained 25 and 19% of the variation for blind nodes in main axis and lateral branches

respectively in 2010, and 15 and 33% in 2011.

Individual backcross family analysis for FG x TNP backcrosses detected one QTL

on LG1, two at LG3, one on LG6/8 and one on LG7, which presented the highest LOD

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score (Appendix, Figures A-12 and A-13). Combined FG x TNP backcross family

analysis confirmed the QTL detected in the individual family analysis (Table 4-11,

Figure 4-9). The QTL with the highest LOD score was detected at LG7 (Table 4-11).

The QTL found explain 52 and 64% of the phenotypic variation for blind nodes in main

axis and lateral branches respectively in 2010, and 54 and 53% in 2011.

As observed for branching, the largest effect QTL discovered for blind nodes in FG

x P. kan and FG x TNP families were different. However, lower effect QTL observed on

the top of LG1 and LG3 and in the middle of LG4 and LG6/8 are localized in similar

regions in both groups of families (Figures 4-8 and 4-9).

Blind node QTLs were not detected in the ‘UF97-47’ x (‘UF97-47’ x P. kan)

population (Figure 4-10). This could be due to the small population size of the

backcross family or to the low phenotypic variability for blind nodes in this family. The

expression of blind nodes in this family was much more restricted with blind node

frequency ranging from 0 to 30% whereas the frequency of blind nodes in FG x P. kan

and FG x TNP backcross populations ranged from 0-90%.

Allelic effects from QTLs

Using the markers most closely linked to the QTLs detected, it was possible to

measure the average phenotypic effect of different alleles on branching and blind node

expression in the backcross progeny.

FG x P. kan combined analysis detected different QTLs for branching in 2010 and

2011. For the 2010 QTL, heterozygous peach/P. kan individuals for the marker closest

to the QTLs on LG5 (PpCUC3) presented reduced branching (branching index = 7.6)

compared to the peach/peach homozygotes (branching index = 10.5). When the QTL

detected in 2011 on LG2 (bppct030) was used to separate the population into

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heterozygous peach/P. kan versus homozygous peach/peach progeny, the

heterozygotes had a higher branching index value (23) in comparison to to the

homozygotes (16).

Different QTLs were detected in FG x TNP backcross families in both years. The

QTL detected in 2010 on LG7 (cppct033) separated the population in peach/almond

heterozygous individuals having a branching index value of 5 versus homozygous

peach/peach progeny which had an average of 6.2. In 2011, the QTLs on LG4

(PpCUC1) and LG7 (pms2) showed that peach/almond heterozygous individuals at both

QTLs had a lower branching index value (6.6) compared to peach/peach homozygous

(12.1).

The data obtained from the QTLs and from the backcross family phenotypes

demonstrates the effect from the P. kansuensis hybrid parent in increasing branching

and of the almond hybrid parent in reducing branching relative to peach. These results

confirm the potential of almond as a germplasm resource for breeding peach trees with

less complicated branching that would reduce pruning.

Allelic effects on blind node expression were considered using an average of blind

nodes in the main axis and lateral shoots across the two years of evaluation, since the

QTLs detected were consistent across traits and years. In the FG x P. kan backcross

populations, progeny heterozygous for the peach/P. kan alleles at LG1 (Y) and LG2

(udp96013) QTLs had fewer blind nodes (14.6%) compared to those homozygous for

the peach alleles at both loci (41.6%). Here the contribution from P. kansuensis resulted

in reduced blind node incidence in the offspring, which demonstrates the value of this

species for breeding.

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In the FG x TNP families, the three QTLs at LG1, LG3 and LG7 had different

parent of origin effects. The QTL having the highest LOD on LG7 (cppct022) indicated

that heterozygous peach/almond individuals had a higher frequency of blind nodes

(53.7%) compared to the homozygous peach/peach progeny (31.3%). At the other two

QTLs, progeny homozygous for peach allele presented a higher incidence of blind

nodes than peach/almond heterozygotes. Based upon the genotypes at the three QTLs,

the individuals that presented lower blind node frequency (18.2%) were heterozygous

peach/almond alleles at the QTLs on LG1 (PpBRC2) and LG3 (cpdct027) and

homozygous for peach/peach at the QTL at LG7 (cppct022). On the other hand, the

average for individuals with all other possible genotypes at the three QTL had a blind

node frequency of 67.9%. Indicating the contribution from both parents, peach and

peach x almond hybrids to modulate blind nodes as observed in the phenotypic data.

Relationships between Branching and Blind Node QTLs

Blind nodes and branching are associated since the formation of axillary

meristems precedes and is necessary for the development of axillary shoots. In the

present study, the blind node trait in the main axis and lateral shoots had a negative and

relatively low relationship with branching index (r=-0.19 and -0.21 respectively P≤0.05),

suggesting a negative influence from blind nodes on branching. However, other factors

such as apical dominance that control bud dormancy or extension growth act as well.

The largest effect QTLs detected for both branching and blind nodes were

localized in different regions of the same linkage group (LG2) in the FG x P. kan

backcross families (Figures 4-5 and 4-8). Similarly, the main QTLs for branching and

blind nodes were in different regions of the same linkage group (LG7) in the FG x TNP

backcross families (Figures 4-6 and 4-9). The only QTL region detected common to

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both blind nodes and branching was localized close to bppct008 on LG6/8 (Tables 4-8

and 4-11) in the FG x TNP population.

QTLs Detection by Candidate Genes

Candidate genes were found to be more commonly associated with small effect

QTLs than large effect QTLs (Figures 4-5, 4-6, 4-8 and 4-9). PpCUC3 was the closest

candidate gene marker to a minor branching QTL in the FG x P. kan backcrosses in

2010 (Table 4-7). In the FG x TNP backcrosses PpCUC1 was the closest marker to a

small effect QTL for branching in 2011 (Table 4-8). The QTLs close to PpCUC3 and

PpCUC1 had limited effects on the phenotypic variance (R2= 0.06). CUC1 and CUC3,

which are implicated in organ boundary formation and meristem identity, also play a role

in axillary meristem formation in Arabidopsis (Hibara et al., 2006). These genes are not

reported to play a role in shoot outgrowth after the axillary meristems have been

successfully formed. Hence, PpCUC1 was expected to be associated with blind nodes.

Conversely, two candidate genes associated with shoot outgrowth in Arabidopsis,

MAX2 and BRC2, were the closest markers to small effect blind node QTLs. PpMAX2

was detected in a lateral shoot blind node QTL for FG x P. kan and a main axis blind

node QTL for FG X TNP blind node in 2010. PpBRC2 was closely associated with a

main axis blind node QTL in 2011 and lateral shoot blind node QTLs in 2010 and 2011.

The contribution of these QTLs to the total phenotypic variance was low, between

R2=0.05 and 0.10. MAX2/RMS4 in Arabidopsis and pea act locally in the node to inhibit

shoot growth from the axillary bud after a mobile signal is produced by the branching

genes, MAX1, MAX3 and MAX4 (Stirnberg et al., 2007). There is no previous

information of MAX2 being involved in axillary meristem formation. Arabidopsis brc1

mutants and in a weaker manner brc2 mutants showed accelerated formation of axillary

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meristems and fewer leaf axils without buds, besides having profuse branching,

indicating some role of this gene in axillary bud development in addition to shoot

outgrowth (Aguilar-Martinez et al., 2007).

In a comparison between candidate gene association and QTL mapping for

branching genes in Arabidopsis (Ehrenreich et al., 2007), there were significant

associations between MAX2, MAX3 and SPS and branching variation in a panel of 96

accessions. However, the location of these genes did not overlap additive but epistatic

QTLs in two recombinant inbred line populations. Similarly in our study the major QTLs

did not locate in the same regions as the candidate genes, although these were

included in the QTL analysis as marker genes.

There are additional branching and blind node associated genes identified in

Arabidopsis and other species that might be used in future studies. For instance

Arabidopsis regulator of axillary meristems (RAX1, RAX2 and RAX3) are members of

the MYB family of transcription factors, the three RAX genes have redundant functions

and mutants fail to produce axillary meristems, RAX genes act early in establishing the

axillary meristem niche and is required for the expression of CUC2 (Keller et al., 2006;

Muller et al., 2006). BLAST analysis of the RAX gene sequences from Arabidopsis to

the peach genome sequence yielded many sequences with low homology; hence, it

was not possible to use this gene for candidate analysis in this study. Other branching

genes have also been identified such as AXR2, AXR3 and AXR6 and terminal flower

(TF1) in Arabidopsis (Ehrenreich et al., 2007).

The QTLs detected in this study require evaluation in additional environments and

genetic backgrounds. Fine mapping and evaluation of additional candidate genes

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located in the validated QTL regions using the peach genome sequence could help to

identify the genes responsible for branching and blind node incidence. By means of this

and future studies, it will be possible to develop an understanding of blind node

development and branching in peach making feasible the breeding of peach cultivars

with reduced branching and without blind nodes.

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Table 4-1. Interspecific backcross families used for studies in tree architecture. FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=’Tardy Nonpareil’ almond.

Family Individuals (#)

‘AP 00-30wbs’ x (FG x P. kan 3) 66 ‘AP 00-30wbs’ x (FG x P. kan 6) 62 ‘AP00-30wbs’ x (FG x TNP) 78 ‘UFSharp’ x (FG x P. kan 3) 85 ‘UFSharp’ x (FG x P. kan 6) 99 ‘UFSharp’ x (FG x TNP) 126 ‘UF97-47’ x (‘UF97-47’ x P. kan) 88

Table 4-2. Mean branching index (BI) and blind node incidence for the main axis (BNM)

and lateral branches (BNL) in the interspecific backcross families (winter of 2010). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.


‘AP00-30wbs’ x (FG x P. kan 3) 5.7 ab z 20.5 b 18.4 b ‘AP00-30wbs’ x (FG x P. kan 6) 5.6 ab 24.9 b 21.3 ab ‘AP00-30wbs’ x (FG x TNP) 3.9 b 34.5 ab 32.1 a ‘UFSharp’ x (FG x P. kan 3) 7.3 a 18.4 bc 14.5 bc ‘UFSharp’ x (FG x P. kan 6) 8.2 a 23.2 b 18.4 b ‘UFSharp’ x (FG x TNP) 5.2 b 44.4 a 34.4 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 8.3 a 10.7 c 11.5 c z Means followed by different letters are significantly different, Tukey (P≤ 0.05). Table 4-3. Mean branching index (BI) and blind node incidence values for the main axis

(BNM) and lateral branches (BNL) in the interspecific backcross families (winter of 2011). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


‘AP00-30wbs’ x (FG x P. kan 3) 17.3 ab z 27.7 bc 24.4 b ‘AP00-30wbs’ x (FG x P. kan 6) 20.7 a 30.1 b 28.4 b ‘AP00-30wbs’ x (FG x TNP) 9.5 b 46.1 ab 43.5 a ‘UFSharp’ x (FG x P. kan 3) 19.8 a 27.9 b 23.3 bc ‘UFSharp’ x (FG x P. kan 6) 18.6 a 30.3 b 26.7 b ‘UFSharp’ x (FG x TNP) 11.9 b 55.9 a 52.4 a ‘UF97-47’ x (‘UF97-47’ x P. kan) 21.3 a 14.1 c 12.9 c z Means followed by different letters are significantly different, Tukey (P≤ 0.05).

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Table 4-4. Statistics for single nucleotide polymorphic positions (SNP) within each branching and blind node candidate gene for Prunus parents and hybrids used for obtaining backcross populations.

Gene SNP frequency

Exon SNP frequency

Intron SNP frequency

Transition frequency

Transversion frequency

PpAXR1z 0.017 0.020 0.017 0.004 0.013 PpBRC1 0.009 0.000 0.012 0.003 0.006 PpBRC2 0.003 0.000 0.006 0.002 0.001 PpCUC1 0.008 0.008 0.009 0.007 0.001 PpCUC2 0.008 0.000 0.012 0.008 0.000 PpCUC3 0.003 0.000 0.004 0.003 0.000 PpLAS 0.008 0.007 0.008 0.003 0.005 PpMAX1 0.007 0.000 0.012 0.004 0.003 PpMAX2 0.024 0.016 0.027 0.011 0.013 PpMAX3 0.021 0.025 0.019 0.011 0.010 PpMAX4 0.027 0.015 0.036 0.024 0.003 PpPIN 0.012 0.013 0.011 0.007 0.005 PpREV 0.009 0.012 0.008 0.007 0.002 PpSPS 0.021 0.012 0.025 0.012 0.009 z Prefixes (Pp) before Arabidopsis gene name stands for P. persica.

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Table 4-5. Number of heterozygous single nucleotide polymorphic positions (SNP) within different Prunus genotype sequences for each candidate gene and their total frequencies. FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, and TNP=‘Tardy Nonpareil’ almond.

Gene P. persica genotypes P. kan TNP FG ‘AP00-30’ ‘UFSharp’ ‘UF97-47’

PpAXR1 0 0 0 0 0 0 PpBRC1 0 0 0 0 0 0 PpBRC2 0 0 0 0 0 0 PpCUC1 0 0 0 0 0 1 PpCUC2 0 0 0 0 0 1 PpCUC3 0 0 0 0 0 0 PpLAS 1 0 0 0 0 1 PpMAX1 0 0 0 0 0 0 PpMAX2 0 0 0 0 0 0 PpMAX3 0 0 0 0 0 2 PpMAX4 3 0 3 5 0 0 PpPIN 0 0 0 0 0 1 PpREV 0 0 0 0 0 0 PpSPS 2 0 1 1 0 0 SNP frequencies for all the sequences (5938 bp)

0.001 0 0.0006 0.001 0 0.001

z Prefixes (Pp) before Arabidopsis gene name stands for P. persica.

98

Table 4-6. Haplotypes found in single nucleotide polymorphic positions in branching and blind nodes candidate genes and their frequencies in different Prunus genotypes.

Gene Haplotype Peachz P. kansuensis ‘Tardy Nonpareil’ almond

PpAXR1y acctc ctagc accta

1 0 0

0 0 1

0 1 0

PpBRC1 ccc tca cgc

1 0 0

0 1 0

0 0 1

PpBRC2 tc cc cg

1 0 0

0 1 0

0 0 1

PpCUC1 gctaa aacgg accgg

1 0 0

1 0 0

0 0.5 0.5

PpCUC2 cta acg ccg

1 0 0

1 0 0

0 0.5 0.5

PpCUC3 at ca

1 0

0 1

1 0

PpLAS cgg cgt ctg tgg

0.9 0.1 0 0

0 0 1 0

0.5 0 0 0.5

PpMAX1 at ac cc ct

1 0 0 0

0 0 0.5 0.5

0 1 0 0

PpMAX2 gcaagtcgtc ggtgcthhtt agtggagcct

1 0 0

0 1 0

0 0 1

PpMAX3 ctaaccc ctcaccc cccagct ccctgtc tcctgcc

0.9 0.1 0 0 0

0 0 1 0 0

0 0 0 0.5 0.5

PpMAX4 ccgaaagta ccgaagggg tcgaagagg ccggagggg ctagcgggg

0.7 0.2 0.1 0 0

0 0 0 1 0

0 0 0 0 1

99

Table 4-6. Continued

Gene Haplotype Peachz P. kansuensis ‘Tardy Nonpareil’ almond

PpREV cctc tctc tgct

1 0 0

0 1 0

0 0 1

PpSPS aatgcggtga aatacggtgg aatgctgtga aatgcggtgg cccgtgacca

0.7 0.1 0.1 0.1 0

1 0 0 0 0

0 0 0 0 1

PpPIN agtcc agcca gacaa ggcaa

1 0 0 0

0 1 0 0

0 0 0.5 0.5

z Peach genotypes include ‘UF02-01’ and ‘AP05-18w’ haploids, ‘Flordaguard’, ‘AP 00-30 wbs’, ‘UFSharp’ and ‘UF 97-47’. y Prefixes (Pp) before Arabidopsis gene name stands for P. persica. Table 4-7. QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x

(FG x P. kan) combined families.

Trait and year

LG LOD peak position (cM)

Nearest marker and distance (cM)

Maximum LOD

R2

BI 2010 LG3 47.0 udp96008 (0) 2.9* z 0.04 BI 2010 LG4 34.0 pchgms5 (2) 2.7* 0.04 BI 2010 LG5 0.0 PpCUC3 (0) 4.1** 0.06 BI 2011 LG2 46.0 bppct030 (7) 9.9** 0.19 z *Significant at 0.05, **Significant at 0.01 Table 4-8. QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x

(FG x TNP) combined families.

Trait and year

LG LOD peak position cM)


Maximum LOD

R2

BI 2010 LG7 23.3 cppct033 (2) 3.51** z 0.07 BI 2011 LG4 39.3 PpCUC1 (1) 3.19** 0.06 BI 2011 LG7 29.6 pms2 (1) 3.06** 0.06 z *Significant at 0.05, **Significant at 0.01 Table 4-9. QTLs associated with branching index (BI) in ‘UF97-47’ x (‘UF97-47’ x P.

kan).

Trait and year



Maximum LOD

R2

BI 2010 LG2 32.1 bppct030 (3) 2.1* z 0.14 BI 2010 LG5 17.4 cpsct006 (5) 2.7* 0.21 z *Significant at 0.05, **Significant at 0.01

100

Table 4-10. QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30wbs’ and ‘UFSharp’ x (FG x P. kan) combined families.

Trait and year



Maximum LOD

R2

BNM 2010 LG2 30.8 udp96013 (6) 7.9** z 0.16 BNM 2010 LG4 32.9 pchgms5 (1) 2.7* 0.04 BNM 2010 LG5 28.5 cpsct006 (2) 2.8* 0.05 BNL 2010 LG1 12.5 Y (7 cM) 4.7** 0.08 BNL 2010 LG2 24.7 udp96013 (1) 4.3** 0.07 BNL 2010 LG3 21.1 PpMAX2 (1) 2.6* 0.04 BNM 2011 LG2 21.4 udp96013 (3) 6.8** 0.11 BNM 2011 LG5 29.0 cpsct006 (2) 2.8* 0.04 BNL 2011 LG1 19.4 Y (1) 5.1** 0.07 BNL 2011 LG2 29.3 udp96013 (5) 7.9** 0.12 BNL 2011 LG3 15.5 bppct039 (3) 3.5** 0.06 BNL 2011 LG4 32.3 pchgms5 (1) 2.7* 0.04 BNL 2011 LG6/8 31.1 Gr (2) 2.8* 0.04 z *Significant at 0.05, **Significant at 0.01

101

Table 4-11. QTLs associated with blind nodes in main axis (BNM) and lateral branches (BNL) in ‘AP00-30 wbs’ and ‘UFSharp’ x (FG x TNP) combined families

Trait and year



Maximum LOD

R2

BNM 2010 LG3 16.8 PpMAX2 (1) 4 .8** z 0.07 BNM 2010 LG3 54.2 cpdct027 (1) 6.3** 0.11 BNM 2010 LG6/8 32.0 bppct008 (2) 5.0** 0.09 BNM 2010 LG6/8 46.4 cppct023 (1) 4.8** 0.07 BNM 2010 LG6/8 56.7 eppcu5628 (2) 2.6* 0.05 BNM 2010 LG7 11.7 cppct033 (7) 6.5** 0.13 BNL 2010 LG1 16.0 PpBRC2 (1) 6.5** 0.10 BNL 2010 LG3 8.7 bppct039 (0) 3.2** 0.05 BNL 2010 LG3 51.2 udp96008 (3) 6.9** 0.13 BNL 2010 LG4 16.9 udp98024 (4) 2.5* 0.07 BNL 2010 LG7 10.6 cppct033 (8) 14.4** 0.29 BNM 2011 LG1 16.0 PpBRC2 (1) 3.6** 0.05 BNM 2011 LG1 56.7 bppct028 (1) 2.6* 0.04 BNM 2011 LG3 8.9 bppct039 (1) 3.2* 0.05 BNM 2011 LG3 54.0 cpdct027 (1) 5.6** 0.11 BNM 2011 LG6/8 46.1 cppct023 (1) 2.6* 0.04 BNM 2011 LG7 7.6 cppct022 (7) 11.6** 0.25 BNL 2011 LG1 15.7 PpBRC2 (1) 5.1** 0.08 BNL 2011 LG3 8.9 bppct039 (1) 3.4** 0.05 BNL 2011 LG3 53.7 cpdct027 (1) 3.3** 0.06 BNL 2011 LG6 46.1 cppct023 (1) 2.2* 0.03 BNL 2011 LG6 65.1 eppcu5628 (7) 2.6* 0.06 BNL 2011 LG7 7.0 cppct022 (7) 11.7** 0.25 z *Significant at 0.05, **Significant at 0.01

102

Figure 4-1. Genes involved in axillary meristem formation and outgrowth and their interactions. Names in brown indicate genes that were not studied in the present research. FP, foliar primordia; AM, apical meristem; AXR1, auxin-resistant1; BRC1 and BRC2, branched1 and 2; CUC1, CUC2 and CUC3, cup-shaped cotyledon; LAS, lateral suppressor; MAX1, MAX2, MAX3 and MAX4, more axillary growth1, 2, 3 and 4; PIN, pinhead; REV, revoluta; RAX1, RAX2 and RAX3 regulator of axillary growth1, 2 and 3; SPS, supershoot.

FP

AM

FP AM

103

Figure 4-2. ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis) backcross

combined linkage map. z Loci followed by asterisk(s) indicates segregation distortion at P≤0.05 (*), 0.01 (**) and 0.001 (***).

**

*

***

*

*

*

**

**

*

**

*

*

*

/8

udp960050.0

Y19.5

PpBRC230.7

bppct02742.8

PpMAX462.4

1

udp980250.0

PpMAX311.6

udp9601324.6

bppct03039.0

cpsct03453.8

2

bppct0070.0

bppct03913.0

PpMAX221.6

PpAXR142.8

udp9600847.0

cpdct02762.3

3

cpsct0390.0

udp9802412.9

pchgms532.1

bppct02363.7

4

PpCUC30.0

bppct0265.1

cpsct00630.9

bppct03252.0bppct01455.0PpBRC155.8

PpPIN59.6

5

PpLAS0.0

PpREV21.4

bppct00824.4

Gr29.1

cppct02333.4

bppct02542.8

udp9841247.9

cpdct02382.9udp9840984.4

6

cppct0220.0

cppct03318.1

pms230.6

epdcu339246.3

7

z

104

Figure 4-3. ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) backcross combined linkage map. z Loci followed by asterisk(s) indicates segregation distortion at P≤ .05 (*), 0.01 (**) and 0.001 (***).

**

***

**

Y0.0

PpBRC215.9

PpMAX433.0

cppct02948.4

bppct02857.6

1

udp980250.0

PpMAX312.5

udp9601323.9

bppct03031.9

cpsct03443.8

2

bppct0070.0

bppct0398.6

PpMAX216.1

PpAXR131.5

udp9600839.0

cpdct02754.4

3

udp980240.0

epdc383229.0

PpCUC139.8PpCUC241.8

4

bppct0260.0

cpsct00618.9

bppct03234.8

bppct01441.8

PpBRC147.7PpPIN49.3

5

PpLAS0.0

eppcu17945.3

PpSPS9.0

PpREV23.8

bppct00834.0

Gr40.4

cppct02345.7

bppct02551.0

eppcu562858.5

eppcu472677.4

6

cppct0220.0

cppct03318.9

pms227.5

epdcu339247.7

7

**

**

***

*

* *

**

***

**

***

***

***

/ 8

105

Figure 4-4. ‘UF97-47’ x (‘UF97-47’ x P. kansuensis) backcross linkage map. z Loci followed by asterisk(s) indicates segregation distortion at P≤0.05 (*), 0.01 (**) and 0.001 (***).

Y0.0

PpBRC214.7

bppct02729.4

bppct01638.4

PpMAX463.5

1

udp980250.0

PpMAX39.0

udp9601325.7

bppct03034.7

2

bppct0390.0

PpMAX29.0

PpAXR121.8

udp9600829.0

cpdct02754.1

3

udp980240.0

pchgms518.7

bppct02343.8

4

PpCUC30.0

bppct0269.1

cpsct00621.9

bppct03234.7

bppct01438.3

PpBRC149.2

PpPIN54.6

5

PpLAS0.0

PpREV10.9

bppct00819.9

cppct02329.0

udp9841249.8

6

cppct0330.0

pms210.9

epdcu339219.9

7

***

**

**

*

106

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010 BI 2011

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

BI 2010

BI 2011

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010 BI 2011

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010 BI 2011

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

BI 2010

BI 2011

BI 2010

BI 2011

Figure 4-5. QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x

(‘Flordaguard’ x P. kansuensis) combined families. Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

P

pC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

epcu17

94

P

pS

PS

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

16 33 12 24 9 16 31 39 29 19 35 9 24 34 40 58 21 30

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

P

pC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

epcu17

94

P

pS

PS

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

16 33 12 24 9 16 31 39 29 19 35 9 24 34 40 58 21 30

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

P

pC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

epcu17

94

P

pS

PS

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

16 33 12 24 9 16 31 39 29 19 35 9 24 34 40 58 21 30

BI 2010

BI 2011

BI 2010

BI 2011

BI 2010

BI 2011

16 33 12 24 9 16 31 39 29 19 35 9 24 34 40 58 21 30

Figure 4-6. QTLs associated with branching index (BI) in ‘AP00-30wbs’ and ‘UFSharp’ x

(‘Flordaguard’ x ‘Tardy Nonpareil’ almond) combined families. Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

107

Y

PpB

RC

2

bpp

ct0

27

bpp

ct0

16

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

cppct0

23

udp

98

41

2

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

15 29 38 9 9 22 29 17 9 22 35 11 20 29

BI 2010 BI 2011

Figure 4-7. QTLs associated with branching index (BI) in ‘UF97-47’ x (‘UF97-47’ x P.

kansuensis). Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

108

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

BNM 2010

BNL 2010

BNM 2011

BNL 2011

BNM 2010

BNL 2010

BNM 2011

BNL 2011

Figure 4-8. QTLs associated with blind nodes in main axis (BNM) and lateral branches

(BNL) in ‘AP00-30wbs’ and ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis) combined families. Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

PpC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

epcu17

94

PpS

PS

P

pR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

16 33 12 24 9 16 31 39 29 19 35 9 24 34 40 58 19 27

BNM 2010

BNL 2010

BNM 2011

BNL 2011

BNM 2010

BNL 2010

BNM 2011

BNL 2011


(BNL) in ‘AP 00-30 wbs’ and ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) combined families. Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

109

Y

PpB

RC

2

bpp

ct0

27

bpp

ct0

16

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

cppct0

23

udp

98

41

2

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

15 29 38 9 9 22 29 17 9 22 35 11 20 29

BNM 2010

BNL 2010

BNM 2011

BNL 2011

BNM 2010

BNL 2010

BNM 2011

BNL 2011


(BNL) in ‘UF97-47’ x (‘UF97-47’ x P. kan). BNM 2010 and BNM 2011 threshold at 0.01=3.2, BNL 2010 threshold at 0.01=3.0, BNL 20100 threshold at 0.01=2.8 (not shown in graph). BNM 2010, BNL 2010 and BNM 2011 threshold at 0.05=3.1, BNL 2010 threshold at 0.05=2.4 (not shown in graph).

110

CHAPTER 5 CONCLUSIONS

A branching index was developed as a tool to identify tree branching complexity

and quantity in large breeding populations. This index is based on the number of first

order branches in the tree plus the average number of second and further order

branches within three first order branches. F1 hybrids from peach x P. kansuensis (P.

kan) and peach x ‘Tardy Nonpareil’ (TNP) almond were used to validate the branching

index. Two years of evaluation showed that the total number of branches per tree and

its branching index had a fairly strong association with r2 of 0.71 and 0.78. The

branching index can be used in peach breeding programs to select trees with reduced

branching that would not need much pruning and potentially produce better quality fruit.

Branching index and blind node variability was found among seven backcross

populations of ‘Flordaguard’ peach (FG) x P. kan, ‘UF97-47’ peach x P. kan and FG x

TNP almond hybrids backcrossed to different peach selections. FG peach x P.

kansuensis backcross families had on average a branching index value of 7 and 19 and

a blind node incidence of 18 and 25% for 2010 and 2011, respectively. The FG x TNP

backcross families had on average a branching index value of 4 and 11 and a blind

node incidence of 36 and 46% for 2010 and 2011, respectively. Clones of the F1 hybrids

and peach selections used to generate the backcross populations were evaluated along

with their progeny. Peach x P. kan hybrids had higher branching and fewer blind nodes

when compared to peach x TNP, resembling the backcross progeny they generated.

Even though differences were found among populations, there was broad variability

within populations. The branching index varied from 2-25 and 4-50 in 2010 and 2011,

respectively. The incidence of blind nodes ranged from 0-90% in the FG x P. kan

111

backcross families, whereas branching index varied 2-12 and 4-38 in 2010 and 2011,

respectively. Blind node frequency ranged from 0-95% in the FG x TNP backcross

families. The peach selections used as the backcross female parents ‘AP00-30wbs’ and

‘UFSharp’ had intermediate branching and high blind node incidence, ‘UF97-47’

presented higher branching and low blind node incidence, contributing to the detected

variability within families and indicating variability within peach. The transgressive

segregation observed suggests that both traits are influenced by many genes. Narrow

sense heritability estimates were 0.37 for branching index and 0.21 for blind nodes,

indicating that both are quantitative traits affected by the environment. Nevertheless,

selection for low blind node incidence and reduced branching is feasible. As shown by

the ‘UF97-47’ x (‘UF97-47’ x P. kan) backcross family which had a blind node incidence

below 30% and its parents which also had the lowest incidence of blind nodes among

all the backcross parents . Similarly, in the case branching, the almond hybrids and their

backcross progeny showed reduced branching in comparison with the P. kan hybrids

and backcross progeny, again indicating that parental selection for reduced branching is

possible.

Detection of QTL associated with branching and blind nodes was performed using

a map generated by SSRs and a set of 14 candidate genes associated with axillary

meristem formation and outgrowth in Arabidopsis. SNPs were found within the

candidate gene sequences in the different Prunus parents. ‘Tardy Nonpareil’ almond,

‘Flordaguard’ and ‘UF97-47’ peach presented the highest level of heterozygocity in the

sequences. P. kansuensis and ‘Tardy Nonpareil’ almond had haplotypes that differed

from peach, resulting in a high number of heterozygous SNP positions in the sequences

112

of the peach x P. kansuensis or ‘Tardy Nonpareil’ almond F1s. This confirms the value

of interspecific crosses for genetic studies in peach as they increase the polymorphism

frequency.

The genomic maps generated consisted of seven linkage groups (LG), LG1-5 and

7 were homologous to the same LG from the Prunus reference map, but a chimeric 6/8

linkage group containing portions of LG6 and LG8 from the reference map was

generated as a result of a reciprocal translocation between LG6 and LG8 near the Gr

leaf color locus, which has been detected only in populations segregating for the

dominant Gr allele for red leaf color. Segregation distortion was detected mostly in loci

at LG6/8 as a result of the translocation and to the proximity to the self-incompatibility

locus in TNP almond backcrosses.

The branching QTL detected in 2010 and 2011 differed, and the moderate

correlation between the two years data is potentially responsible for the lack of stability

of the detected QTL. The QTL with the highest LOD values were detected in 2011, one

in LG2 in the FG x P. kan backcrosses which explained 19% of the phenotypic variance;

and two, one in LG4 and another in LG7 in the FG X TNP backcrosses that explained

12% of the phenotypic variance. The results suggest that more than two years of

evaluation are necessary for analysis of branching. In the FG x P. kan backcrosses,

progeny heterozygous for the major QTL in 2011 had higher branching index values

(23) when compared to progeny homozygous for the peach alleles (16). In the FG x

TNP backcross families, individuals carrying almond alleles at both of the QTLs

detected in 2011 had lower branching index values (6.6) when compared to progeny

homozygous for the peach alleles (12.1). These results demonstrate the impact of P.

113

kansuensis and TNP almond QTL for increasing and decreasing branching,

respectively.

Blind node QTL were consistent in both 2010 and 2011. The QTL with the highest

LOD score in the FG x P. kan backcrosses was detected on LG2 and minor QTL were

identified on LG1, LG3, LG4, LG5 and LG6/8. These QTL explained ~25% of the

phenotypic variation observed. In the FG X TNP backcrosses, the QTL with the highest

LOD was detected on LG7 and minor QTLs at LG1, LG3, and LG6/8, explaining ~56%

of the phenotypic variation. In the FG x P. kan backcrosses, individuals heterozygous

for the P. kan allele at the major QTL had fewer blind nodes (15.1%) compared to those

homozygous for the peach (25.9%) allele. In the FG x TNP backcross families,

individuals heterozygous for the almond allele at the major QTL had more blind nodes

(53.7%) compared to progeny homozygous for the peach allele (31.3%).

The major effect QTL in the FG x P. kan and FG X TNP backcrosses were

different, indicating that multiple loci control both traits and that different loci may

segregate in different populations.

Suprisingly, the major QTL detected for both branching and blind nodes were

localized on LG2 in the FG x P. kan backcross families and on LG7 in the FG X TNP

backcross families. Blind nodes and branching are related since the formation of axillary

meristems is necessary for the development of axillary shoots. The correlation between

blind node frequency and the branching index was -0.20, suggesting a limited negative

influence from blind nodes in branching. Other factors such as auxin levels that control

axillary bud dormancy may also have an impact.

114

Candidate genes did not map to the location of large effect QTLs. However,

PpCUC1 and PpCUC3 genes associated in Arabidopsis with axillary meristem

formation; and PpBRC2 and PpMAX2 associated in Arabidopsis with meristem

outgrowth overlapped minor effect QTLs for branching and blind nodes, respectively.

The detected QTLs need to be validated in different populations and

environments. Further research on candidate genes and fine mapping may help to

identify the genes responsible for differences in branching blind nodes in peach. This

information could be used to develop markers for breeding trees with reduced branching

and low incidence of blind nodes in peach.

115

APPENDIX COMPLEMENTARY TABLES AND FIGURES

Table A-1. Microsatellite markers used to identify self-pollinated in the interspecific backcross populations studied. FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis ‘A’, and TNP=‘Tardy Nonpareil’ almond.

Family SSRs

‘AP00-30wbs’ x (FG x P. kan 3) bppct8, bppct26, bppct30, cpsct39, epdcu3382 and udp9825

‘AP00-30wbs’ x (FG x P. kan 6) bppct8, bppct23, bppct30, pmsg2, epdcu3382 and udp9825

‘AP00-30wbs’ x (FG x TNP) bppct8, bppct26, bppct30, cpsct39, pmsg2 and udp9825

‘UFSharp’ x (FG x P. kan 3) bppct14, bppct26, epdcu3382, udp968, udp98412 and udp9825

‘UFSharp’ x (FG x P. kan 6) bppct14, bppct30, cppct33, pmsg2, udp9613 and udp9825

‘UFSharp’ x (FG x TNP) bppct14, bppct26, cppct29, epdcu3382, pmsg2 and udp9825

‘UF97-47’ x (‘UF97-47’ x P. kan) bppct8, bppct26, bppct30, cpsct39, epdcu3382 and udp9825

116

Table A-2. Specific primers designed to amplify candidate genes associated with axillary meristem formation (AMF) and outgrowth (AMO).

Gene Trait Forward primer Reverse primer ATz (ºC)

Fragment size (bp)

PpAXR1y AMO AGCAAGGACAGACAGCCCTA

CTTGGCCTTAACAGCATCGT

59 419

PpBRC1 AMO CACAAGCACCACCCCTTACT

CCTGAATGAGCAACCACTCA

57 342

PpBRC2 AMO TCAAGCGGGAATAAGGACAG

GCTCCACTGGGAATTGTTGT

57 692

PpCUC1 AMF CTTGACAGCAGCTTCACTGG

AGCTCTGCCACGGTAGAAAA

57 364

PpCUC2 AMF GTTTTCTACCGTGGCAGAGC

TTGCTCATTCGGGTCTTCTT

57 741

PpCUC3 AMF GAGTGAGCTGAGTGGGGAAG

TGGCCCTGTTGGTTCTTAAC

57 751

PpLAS AMF ATGCGCCAATTGCTCATTAC

AAAGTCGAGGATGTGGATGG

57 402

PpMAX1 AMO TTACGAGCATCTCCTTGCTG

TTGCAACTAATGGGGAAACC

57 331

PpMAX2 AMO AAAACTTGGATGCTGCTGCT

TCCGAATCTCCTCCAGATTG

57 441

PpMAX3 AMO TTGCTTCCTCGGTCTCCTAA

GAGGTAGCGTCTTGCTTTGG

57 387

PpMAX4 AMO CGGTCATTGCAGATTGTTGT

CAACCCTCTTCCATGTTCGT

57 341

PpPIN AMF ATTTCAGCTTTGGCAACAGG

GCCAAGTCCTGCATCAGATAG

57 447

PpREV AMF CGCCAGTATGTTCGAAGTGT

CCAAGGAATCAGGTCTCAGC

57 480

PpSPS AMO ACTCAAAGACGCCAGTGGTC

GCCCTTGGGGATGAAGTAAT

57 485

z AT=Annealing temperature y Prefixes (Pp) before Arabidopsis gene name stands for P. persica.

117

Table A-3. Single nucleotide polymorphisms detected in PpAXR1 amplicon (296 bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.

Genotype Position within the sequence (bp)

42 169 182 185 235 Haploid 1 A C C T C Haploid 2 A C C T C ‘Flordaguard’ A C C T C P. kansuensis ‘A’ A C C T A ‘Tardy Nonpareil’ C T A G C FGy x P. kan 3 A C C T M FG x P. kan 6 A C C T M FG x TNP M Y M K C ‘AP00-30wbs’ A C C T C ‘UFSharp’ A C C T C ‘UF97-47’ A C C T C ‘UF97-47’ x P. kan A C C T M

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-4. Single nucleotide polymorphisms detected in PpBRC1 amplicon (325bp).



41 60 73 Haploid 1 C C C Haploid 2 C C C ‘Flordaguard’ C C C P. kansuensis ‘A’ T C A ‘Tardy Nonpareil’ C G C FG x P. kan 3 Y C M FG x P. kan 6 Y C M FG x TNP C S C ‘AP00-30wbs’ C C C ‘UFSharp’ C C C ‘UF97-47’ C C C ‘UF97-47’ x P. kan Y C M

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide.

118

Table A-5. Single nucleotide polymorphisms detected in PpBRC2 amplicon (663bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


334 503 Haploid 1 T C Haploid 2 T C ‘Flordaguard’ T C P. kansuensis ‘A’ C C ‘Tardy Nonpareil’ C G FG x P. kan 3 Y C FG x P. kan 6 Y C FG x TNP Y S ‘AP00-30wbs’ T C ‘UFSharp’ T C ‘UF97-47’ T C ‘UF97-47’ x P. kan Y C

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-6. Single nucleotide polymorphisms detected in PpCUC1 amplicon (598bp).



111 222 355 399 516 Haploid 1 G C T A A Haploid 2 G C T A A ‘Flordaguard’ G C T A A P. kansuensis ‘A’ G C T A A ‘Tardy Nonpareil’ A M C G G FG x P. kan 3 G C T A A FG x P. kan 6 G C T A A FG x TNP R C Y R R ‘AP00-30wbs’ G C T A A ‘UFSharp’ G C T A A ‘UF97-47’ G C T A A ‘UF97-47’ x P. kan G C T A A


119

Table A-7. Single nucleotide polymorphisms detected in PpCUC2 amplicon (356 bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


104 237 282 Haploid 1 C T A Haploid 2 C T A ‘Flordaguard’ C T A P. kansuensis ‘A’ C T A ‘Tardy Nonpareil’ M C G FG x P. kan 3 C T A FG x P. kan 6 C T A FG x TNP C Y R ‘AP00-30wbs’ C T A ‘UFSharp’ C T A ‘UF97-47’ C T A ‘UF97-47’ x P. kan C T A

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-8. Single nucleotide polymorphisms detected in PpCUC3 amplicon (599 bp).



150 180 Haploid 1 A T Haploid 2 A T ‘Flordaguard’ A T P. kansuensis ‘A’ C A ‘Tardy Nonpareil’ A T FG x P. kan 3 M W FG x P. kan 6 M W FG x TNP A T ‘AP00-30wbs’ A T ‘UFSharp’ A T ‘UF97-47’ A T ‘UF97-47’ x P. kan M W


120

Table A-9. Single nucleotide polymorphisms detected in PpLAS amplicon (389bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


105 181 246 Haploid 1 C G G Haploid 2 C G G ‘Flordaguard’ C G K P. kansuensis ‘A’ C T G ‘Tardy Nonpareil’ Y G G FG x P. kan 3 C K G FG x P. kan 6 C K G FG x TNP Y G G ‘AP00-30wbs’ C G G ‘UFSharp’ C G G ‘UF97-47’ C G G ‘UF97-47’ x P. kan C K G

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-10. Single nucleotide polymorphisms detected in PpMAX1 amplicon (280bp).



133 201 Haploid 1 A T Haploid 2 A T ‘Flordaguard’ A T P. kansuensis ‘A’ C Y ‘Tardy Nonpareil’ A C FG x P. kan 3 M Y FG x P. kan 6 M Y FG x TNP A Y ‘AP00-30wbs’ A T ‘UFSharp’ A T ‘UF97-47’ A T ‘UF97-47’ x P. kan M T

Blue fill =Common nucleotide Green fill = Heterozygous nucleotides Yellow fill = Rare nucleotide

121

Table A-11. Single nucleotide polymorphisms detected in PpMAX2 amplicon (409bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


84 127 129 132 145 168 179 195 247 381 Haploid 1 G C A A G T C G T C Haploid 2 G C A A G T C G T C ‘Flordaguard’ G C A A G T C G T C P. kansuensis ‘A’ G G T G C T G G T T ‘Tardy Nonpareil’ A G T G G A G C C T FG x P. kan 3 G S W R S T S G T Y FG x P. kan 6 G S W R S T S G T Y FG x TNP R S W R G W S S Y Y ‘AP00-30wbs’ G C A A G T C G T C ‘UFSharp’ G C A A G T C G T C ‘UF97-47’ G C A A G T C G T C ‘UF97-47’ x P. kan G S W R S T S G T Y

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-12. Single nucleotide polymorphisms detected in PpMAX3 amplicon (328bp).



94 98 113 152 174 191 250 Haploid 1 C T A A C C C Haploid 2 C T C A C C C ‘Flordaguard’ C T A A C C C P. kansuensis ‘A’ C C C A G C T ‘Tardy Nonpareil’ Y C C T G Y C FG x P. kan 3 C Y M A S C Y FG x P. kan 6 C Y M A S C Y FG x TNP Y Y M W S C C ‘AP00-30wbs’ C T A A C C C ‘UFSharp’ C T A A C C C ‘UF97-47’ C T A A C C C ‘UF97-47’ x P. kan C Y M A S C Y


122

Table A-13. Single nucleotide polymorphisms detected in PpMAX4 amplicon (332bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


44 81 138 151 203 210 215 219 220 Haploid 1 C C G A A A G T A Haploid 2 C C G A A A G T A ‘Flordaguard’ C C G A A R G K R P. kansuensis ‘A’ C C G G A G G G G

‘Tardy Nonpareil’ C T A G C G G G G FG x P. kan 3 C C G R A G G G G FG x P. kan 6 C C G R A G G G G FG x TNP C Y R R M G G G G ‘AP00-30wbs’ C C G A A A G T A ‘UFSharp’ C C G A A R G K R ‘UF97-47’ Y C G A A R R K R ‘UF97-47’ x P. kan Y C G R A G R G G

Blue fill =Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-14. Single nucleotide polymorphisms detected in PpPIN amplicon (429 bp).



44 62 201 272 286 Haploid 1 A G T C C Haploid 2 A G T C C ‘Flordaguard’ A G T C C P. kansuensis ‘A’ A G C C A ‘Tardy Nonpareil’ G R C A A FG x P. kan 3 A G Y C M FG x P. kan 6 A G Y C M FG x TNP R G Y M M ‘AP00-30wbs’ A G T C C ‘UFSharp’ A G T C C ‘UF97-47’ A G T C C ‘UF97-47’ x P. kan A G Y C M


123

Table A-15. Single nucleotide polymorphisms detected in PpREV amplicon (465 bp). FG=‘Flordaguard’ peach, P. kan=Prunus kansuensis, TNP=‘Tardy Nonpareil’ almond.


70 71 368 389 Haploid 1 C C T C Haploid 2 C C T C ‘Flordaguard’ C C T C P. kansuensis ‘A’ T C T C ‘Tardy Nonpareil’ T G C T FG x P. kan 3 Y C T C FG x P. kan 6 Y C T C FG x TNP Y S Y Y ‘AP00-30wbs’ C C T C ‘UFSharp’ C C T C ‘UF97-47’ C C T C ‘UF97-47’ x P. kan Y C T C

Blue fill = Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide. Table A-16. Single nucleotide polymorphisms detected in PpSPS amplicon (469 bp).



17 65 78 104 114 131 140 149 192 278 Haploid 1 A A T G C G G T G A Haploid 2 A A T G C G G T G A ‘Flordaguard’ A A T R C G G T G R P. kansuensis ‘A’ A A T G C G G T G A ‘Tardy Nonpareil’ C C C G T G A C C A FG x P. kan 3 A A T G C G G T G R FG x P. kan 6 A A T G C G G T G R FG x TNP M M Y R Y G R Y S A ‘AP00-30wbs’ A A T G C G G T G A ‘UFSharp’ A A T G C K G T G A ‘UF97-47’ A A T G C G G T G R ‘UF97-47’ x P. kan A A T G C G G T G A

Blue fill = Common nucleotide. Green fill = Heterozygous nucleotides. Yellow fill = Rare nucleotide.

124

Table A-17. SSR and morphological markers selected to use in the mapping of backcross populations.

LG Markerz AT (ºC)y

Locus (cM)

Backcross familyx Reference

1 udp96005 57 29.2 1, 2, 4 and 5 Cipriani et al., 1999 1 Yw 35.0 All Dirlewanger et al., 2004b 1 bppct027 57 47.3 1, 2, 4, 5 and 7 Dirlewanger et al., 2002 1 bppct016 57 55.2 7 Dirlewanger et al., 2002 1 cppct029 55 65.1 3 and 6 Aranzana et al., 2002 1 bppct028 57 77.4 3 and 6 Dirlewanger et al., 2002 2 udp 98025 57 9.6 All Cipriani et al., 1999 2 udp 96013 57 27.8 All Cipriani et al., 1999 2 bppct030 57 38.0 All Dirlewanger et al., 2002 2 cpsct034 62 48.6 1, 2, 3, 4, 5 and 6 Mnejja et al., 2004 3 bppct007 57 11.2 1, 2, 3, 4, 5 and 6 Dirlewanger et al., 2002 3 bppct039 57 18.0 All Dirlewanger et al., 2002 3 udp96008 57 36.4 All Cipriani et al., 1999 3 cpdct027 62 46.4 All Mnejja et al., 2005 4 cpsct039 62 1.8 1, 2, 4 and 5 Mnejja et al., 2004 4 udp98024 57 11.3 All Cipriani et al., 1999 4 pchgms5 57 24.1 1, 2, 4 and 5 Sosinski et al., 2000 4 epdc3832 57 34.1 3 and 6 Cipriani et al., 1999 4 bppct023 57 45.4 1, 2, 4, 5 and 7 Dirlewanger et al., 2002 5 bppct026 57 5.2 All Dirlewanger et al., 2002 5 cpsct006 57 21.7 All Dirlewanger et al., 2002 5 bppct032 57 34.7 All Dirlewanger et al., 2002 5 bppct014 57 44 All Dirlewanger et al., 2002 6 eppcu1794 55 4.1-

14.9 3 and 6 Howad et al., 2005

6 bppct008 57 30.1 All Dirlewanger et al., 2002 6 Grw

35.0 1, 2, 3, 4 and 5 Dirlewanger et al., 2004b 6 cppct023 55 41.5 All Aranzana et al., 2002 6 bppct025 57 56.4 1, 2, 3, 4 and 5 Dirlewanger et al., 2002 6 udp 98412 57 72.0 1, 2, 4, 5 and 7 Cipriani et al., 1999 7 cppct022 50 18.6 1, 2, 3, 4 and 5 Aranzana et al., 2002 7 cppct033 50 38.9 All Aranzana et al., 2002 7 pms 2 55 47.8 All Unpublished 7 epdcu3392 57 64.7 All Jung et al., 2008 8 eppcu5628 55 13.0-

18.8 3 and 6 Howad et al., 2005

8 eppcu4726 60 30.1-40.9

3 and 6 Howad et al., 2005

8 cpdct023 62 42.6 1,2,4 and 5 Mnejja et al., 2004 8 udp 98409 57 44.5 1,2,4 and 5 Cipriani et al., 1999 z TxE=almond (‘Texas’) x peach (‘Earlygold’) reference map. y Annealing temperature.

125

x Backcross families where the markers were polymorphic and informative for use: 1. ‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 3) 2. ‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 6) 3. ‘AP00-30wbs’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) 4. ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 3) 5. ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 6) 6. ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) 7. ‘UF97-47’ x (‘UF97-47’ x P. kansuensis). w Morphological markers: Y=Flesh color (white/yellow), Gr=leaf color (red/green), Flesh color. Table A-18. Candidate genes genotyped with restriction enzymes

Gene Restriction enzyme Family genotyped y Enzyme units

PpCUC3z PacI 1,2,4,5,7 6 PpMAX2 HphI All 5 PpMAX3 TaqI All 8 PpMAX4 HinfI All 7 PpREV AccI 1,2,4,5,7 5 PpREV BsmBI 3,6 5 PpSPS HinfI 3,6 7 z Prefixes (Pp) before Arabidopsis gene name stands for P. persica. y 1. ‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 3) 2. ‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 6) 3. ‘AP00-30wbs’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) 4. ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 3) 5. ‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 6) 6. ‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’) 7. ‘UF97-47’ x (‘UF97-47’ x P. kansuensis).

126

Table A-19. Candidate genes genotyped with high resolution melt analysis.

Gene Forward primer Reverse primer Annealing temperature

(ºC)

Fragment size (bp)

PpAXR1z ATTGGGTGACCTTGGAAACA

CTTGGCCTTAACAGCATCGT

57 194

PpBRC1 CACAAGCACCACCCCTTACT

CTTCTTCTCGGGATCTGTGG

57 181

PpBRC2 GATGAATTCGTCCACTTCTGAG

CTTGCCTCGACCCTCGAT

57 150

PpCUC1 TTGCAGACAAGGCAAAGATG

TCATCCCAACAAGAGCACAA

57 171

PpCUC2 GATGGGGGAGAAAGAGTGGT

TCGGTAGCTCTAGCTCTTCCAC

57 197

PpLAS TCCCAGCTTGACTTCTCCTC

AGTGCCTCCTCGTTGTTGTT

57 242

PpMAX1 TCATTTCCCTCTTGTCTTCCA

GTTGTCGTCCTCCTCTTCCA

57 191

PpPIN ATTGGCCTCACCTGGTCTCT

GAATATTGTATTGCACCAATGCT

57 174

z Prefixes (Pp) before Arabidopsis gene name stands for P. persica.

127

‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 3)

‘AP00-30wbs’ x (‘Flordaguard’ x P. kansuensis 6)

udp960050.0

Y19.2

PpBRC232.4

bppct02740.5

PpMAX469.1

1

udp980250.0

PpMAX39.8

udp9601319.6

bppct03036.3

cpsct03449.5

2

bppct0070.0

bppct03911.5

PpMAX221.3

PpAXR145.5

udp9600852.1

cpdct02765.3

3

cpsct0390.0

udp9802411.5

pchgms528.2

bppct02350.6

4

PpCUC30.0

bppct0268.1

cpsct00636.7

bppct03261.0

bppct01464.2

PpBRC167.2PpPIN68.8

5

PpLAS0.0

PpREV24.3bppct00825.9

Gr29.1

cppct02335.6

bppct02542.1udp9841243.7

cpdct02374.9

udp9840978.9

6

cppct0220.0

cppct03320.4

pms235.3

epdcu339250.8

7

***

*

**

**

*

*

*

/8

/8

udp960050.0

Y24.3

PpBRC237.5

bppct02750.7

PpMAX465.6

1

udp980250.0

PpMAX311.5

udp9601340.1

bppct03053.3

cpsct03471.8

2

bppct0070.0

bppct03914.9

PpMAX223.0

PpAXR139.7udp9600841.3

cpdct02754.5

3

cpsct0390.0

udp9802414.9

pchgms535.3

bppct02367.3

4

PpCUC30.0

bppct0263.2

cpsct00629.6

bppct03248.1bppct01449.7PpBRC151.3PpPIN54.5

5

PpLAS0.0

PpREV24.7bppct00826.4

Gr38.6

cppct02346.7

bppct02558.8

udp9841263.7

cpdct02398.7

udp98409102.2

6

cppct0220.0

cppct03313.2

pms226.4

epdcu339241.9

7

*

*

*

***

* *

***

*

*

**

*

*

z

128

‘AP00-30wbs’ x (‘Flordaguard’ x ‘Tardy Nonpareil’)

‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 3)

**

*

udp960050.0

Y15.8

PpBRC226.8

bppct02736.8

PpMAX461.8

1

udp980250.0

PpMAX311.0

udp9601320.6

bppct03035.9

cpsct03448.3

2

bppct0070.0

bppct03911.0

PpMAX219.2

PpAXR145.8

udp9600851.2

cpdct02768.0

3

cpsct0390.0

udp9802411.5

pchgms529.3

bppct02357.7

4

PpCUC30.0

bppct0266.8

cpsct00635.2

bppct03256.7

bppct01460.8PpBRC162.8

PpPIN66.9

5

PpLAS0.0

PpREV25.2

bppct00827.9

Gr30.6

cppct02336.0

bppct02541.4udp9841242.8

cpdct02379.2udp9840981.2

6

cppct0220.0

cppct03321.5

pms235.4

epdcu339249.7

7

***

*

*

*

*

*

*

** **

/8

***

Y0.0

PpBRC216.6

PpMAX434.7

cppct02948.4

bppct02859.2

1

udp980250.0

PpMAX313.7

udp9601323.1

bppct03029.8

cpsct03443.5

2

bppct0070.0

bppct0399.4

PpMAX216.1

PpAXR132.7

udp9600840.8

cpdct02760.0

3

udp980240.0

epdc383233.8

PpCUC144.6

PpCUC247.6

4

bppct0260.0

cpsct00618.1

bppct03231.8

bppct01438.5

PpBRC145.2PpPIN46.5

5

PpLAS0.0

eppcu17945.4

PpSPS9.0

PpREV22.7

bppct00833.5

Gr38.9

cppct02344.3

bppct02549.7

eppcu562860.5

eppcu472682.2

6

cppct0220.0

cppct03321.2

pms230.6

epdcu339250.2

7

***

**

***

** **

***

**

* *

*

*

**

***

*

*

*

/8

129

‘UFSharp’ x (‘Flordaguard’ x P. kansuensis 6)

‘UFSharp’ x (‘Flordaguard’ x ‘Tardy Nonpareil’)

Figure A-1. Individual interspecific Prunus backcross genetic maps. z Loci followed by

asterisk(s) indicates segregation distortion at P ≤ 0.05 (*), 0.01 (**) and 0.001 (***).

udp960050.0

Y19.3

PpBRC229.4

bppct02741.6

PpMAX460.4

1

udp980250.0

PpMAX312.2

udp9601324.4

bppct03038.8

cpsct03453.2

2

bppct0070.0

bppct03913.3

PpMAX222.4

PpAXR142.4

udp9600847.4

cpdct02762.9

3

cpsct0390.0

udp9802412.6

pchgms532.2

bppct02365.3

4

PpCUC30.0

bppct0265.0

cpsct00628.6

bppct03251.0bppct01454.0PpBRC155.0

PpPIN59.0

5

PpLAS0.0

PpREV21.4

bppct00824.4

Gr30.4

cppct02335.4

bppct02546.6

udp9841252.6

cpdct02387.0

udp9840990.1

6

cppct0220.0

cppct03318.8

pms230.0

epdcu339246.9

7

*

*

*

*

**

**

*

*

*

** **

/8

***

*

*

*

Y0.0

PpBRC215.5

PpMAX432.0

cppct02948.5

bppct02856.5

1

udp980250.0

PpMAX311.1

udp9601323.8

bppct03032.7

cpsct03443.5

2

bppct0070.0

bppct0398.0

PpMAX216.0

PpAXR130.6

udp9600837.7

cpdct02750.8

3

udp980240.0

epdc383231.9

PpCUC142.7PpCUC243.5

4

bppct0260.0

cpsct00619.5

bppct03237.0

bppct01444.1

PpBRC149.4PpPIN51.2

5

PpLAS0.0

eppcu17945.3

PpSPS8.8

PpREV24.3

bppct00834.4

Gr41.5

cppct02346.8

bppct02552.1

eppcu562859.2

eppcu472677.7

6

cppct0220.0

cppct03317.5

pms225.5

epdcu339246.1

7

**

***

**

*

*

*

**

**

**

**

***

***

/ 8

130

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

24 37 11 40 53 15 23 15 35 30 25 38 47 59 13 26

BI 2010 BI 2011

Figure A-2. QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x P. kan

3). Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05. BI 2011 threshold at 0.01=3.1 (not shown in graph).

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

19 32 40 10 20 11 21 45 11 28 8 37 24 36 43 20 35

BI 2010 BI 2011

Figure A-3. QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x P. kan

6). Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

131

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

P

pC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

16 27 37 11 21 11 19 45 11 29 7 35 25 36 42 21

BI 2010 BI 2011

Figure A-4. QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x P. kan 3).

Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05. BI 2011 threshold at 0.01=3.0 (not shown in graph).

).

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

).

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

).

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

BI 2010

BI 2011

BI 2010

BI 2011

19 31 43 12 24 13 22 43 13 32 5 31 21 29 43 46 18 30

Figure A-5. QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x P. kan 6).

Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05

132

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

P

pM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

P

pC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

epcu17

94

P

pS

PS

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

17 34 14 23 9 16 33 41 34 18 32 9 23 33 39 60 21 30

BI 2010 BI 2011

Figure A-6. QTLs associated with branching index (BI) in ‘AP00-30wbs’ x (FG x TNP).

Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05. BI 2010 threshold at 0.01=3.3, BI 2011 threshold at 0.01=3.1 (not shown in graph).

Y

PpB

RC

2

PpM

AX

4

cppct0

29

bpp

ct0

28

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

udp

98

02

4

epd

c38

32

PpC

UC

1

PpC

UC

2

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

P

pB

RC

1

PpP

IN

PpLA

S

epcu17

94

P

pS

PS

PpR

EV

bpp

ct0

08

G

r cppct0

23

bpp

ct0

25

epcu56

28

epcu47

26

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

15 32 11 23 8 16 32 31 19 37 9 24 34 41 52 59 17 25

BI 2010 BI 2011

Figure A-7. QTLs associated with branching index (BI) in ‘UFSharp’ x (FG x TNP).

Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05.

133

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

P

pC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

G

r

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

24 37 11 40 53 15 23 15 35 30 25 38 47 59 13 26

BNM 2010

BNL 2010

BNM 2011

BNL 2011

BNM 2010

BNL 2010

BNM 2011

BNL 2011

Figure A-8. QTLs associated with blind nodes in main axis (BNM) and lateral branches

(BNL) in ‘AP00-30wbs’ x (FG x P. kan 3). Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05. BNL 2010 and BNM 2011 threshold at 0.01=3.0, BNL 2011 threshold at 0.01=3.1 (not shown in graph).

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

LG 1 LG 2 LG 3 LG 4 LG 5 LG 6/8 LG 7

19 32 40 10 20 11 21 45 11 28 8 37 24 36 43 20 35

BNM 2010

BNL 2010

BNM 2011

BNL 2011

BNM 2010

BNL 2010

BNM 2011

BNL 2011


(BNL) in ‘AP00-30wbs’ x (FG x P. kan 6). Solid lines represent LOD threshold at 0.01, dashed lines represent LOD threshold at 0.05. BNL 2010 and BNM 2011 threshold at 0.01=3.1, BNL 2010 and BNM 2011 threshold at 0.01=3.0 (not shown in graph).

134

udp

96

00

5

Y

PpB

RC

2

bpp

ct0

27

PpM

AX

4

udp

98

02

5

PpM

AX

3

udp

96

01

3

bpp

ct0

30

cpsct0

34

bpp

ct0

07

bpp

ct0

39

PpM

AX

2

PpA

XR

1

udp

96

00

8

cpdct0

27

cpsct0

39

udp

98

02

4

pch

gm

s5

bpp

ct0

23

PpC

UC

3

bpp

ct0

26

cpsct0

06

bpp

ct0

32

bpp

ct0

14

PpB

RC

1

PpP

IN

PpLA

S

PpR

EV

bpp

ct0

08

Gr

cppct0

23

bpp

ct0

25

udp

98

41

2

cpdct0

23

udp

98

40

9

cppct0

22

cppct0

33

pm

s2

epcu33

92

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BIOGRAPHICAL SKETCH

Omar Carrillo Mendoza was born on 1977. He received his primary education in

the public educational system of D.F. and Hidalgo State in Mexico. He got his Bachelor

degree in Agronomy from Universidad Autonoma Chapingo, Mexico; and a Master of

Science degree in fruit science from Colegio de Postgraduados, Mexico. He worked as

an assistant breeder and researcher in temperate fruit trees, small fruits and cactus

pear at Colegio de Postgraduados from 2001 to 2006. He was a collaborator in the

subtropical Mexico strawberry breeding program, breeding and releasing four

strawberry varieties. In 2007 he got a scholarship from the National Council of Science

from Mexico (CONACyT) to study a doctorate at the University of Florida. He began

working in 2008 with Dr. Jose Chaparro in the stone fruit breeding program of the

Horticultural Sciences Department as a PhD student and graduate research assistant.

genetics of tree architecture in peach (prunus persica...

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