molecular advancements in reproduction: from hair to calves collaborator s & dr. milt thomas

Molecular Advancements in Reproduction: from Hair to Calves

Collaborators & Dr. Milt Thomas

Milt Thomas, Department of Animal and Range Sciences

Technology Implementation?


Molecular Advancements in Reproduction: From hair to calves.Outline:I.History II. Discovery: SNP from the GH-IGF axisIII. Discovery: QTL detectionIV. Discovery: transcriptome & proteomeV. Building research populations VI. Conclusions


Molecular Advancements in Reproduction: From hair to calves.

Participants: Past to Present

Konni Shirley, Pablo Luna, Sunday Peters, Kasey DeAtley, Gail Silver, Amelia Clayshulte, Tanner Schaub

Jim Reecy, Rohan Fernando, Kadir Kazilkaya, Dorian Garrick, Zhiliang Hu, John Pollak, Bob Weaber

Juan Medrano, Gonzalo Rincon, David Riley, Chad Chase

2006-35205-16651, 2008-35205-18751, 2009-35205-05100

Konni Shirley, Department of Animal and Range Sciences

Trait CC CT TT χ2

P-

value

First observed estrus 61.8 36.4 1.8 30.0 0.01

Estrus at synchronization 66.3 33.7 0 9.1 0.01

Pregnant during 1st breeding season 57.1 41.9 1.0 53.2 0.01

AI pregnancy 66.7 33.3 0 3.3 0.07

Calved by 2 yr of age 59.2 39.8 1.0 51.6 0.01

Percentages of IGF-I genotypes for categorical reproductive trait

Evolution of a Fertility ProjectNBCEC Large Herd Managers Symposium, KC -2005

– Spring born Brangus (n = 190) heifers

– Born 1997 to 2002

– Progeny of 14 Brangus sires


Evolution of a Fertility ProjectNBCEC Large Herd Managers Symposium, KC -2005

Initial Results from Rex Ranch Project Relative NMSU Angus Heifers

HerdPregnancy

StatusNumber

of Heifers

Genotypic Frequency, %

CC CT TT

1 non-pregnant 160 15 49.4 35.6

1 pregnant 166 19.2 41.6 39.2

3 non-pregnant 75 20 44 36

3 pregnant 85 20 40 40

NMSUPregnancy rate > 90% 110 10 51.8 38.2


Large Herd Managers Symposia (2005)

Identification of Molecular Markers to Improve Fertility of Beef Cattle; 200835205-18751.


QTL Detection

Validation of MAS tools in commercial

herds across environments and

production systems.

Technology Transfer

Fine Mapping

QTL Detection

Validation of MAS tools in commercial

herds across environments and

production systems.

Technology Transfer

Fine Mapping

Long-term goal: understand in molecular detail the genetic pathways regulating reproductive performance in beef cattle, with the intent of using this information to develop genetic improvement programs for fertility.


II. Discovery: SNP from the GH-IGF axis

IGF 1 38Mb

PMCH

STAT 6STAT 2

IGFBP6

SOCS2 15Mb

+ Pregnancy Associated

Plasma Protein(PAPPA)

orPappalysin

Chromosome 16

Bovine Resequencing Populationa. No common ancestors in 3 generation pedigree.b. Resequenced conserved regions of candidate genesc. 75 SNP discovered.

• Dairy breeds (n = 8/breed):

• Beef breeds (n = 6/breed):


Vista®

http://images.google.com/imgres?imgurl=http://www.clonesafety.org/imagelibrary/photos/thumbs/thumb_cow1.jpg&imgrefurl=http://www.clonesafety.org/imagelibrary/photos/&h=150&w=250&sz=10&hl=en&start=4&tbnid=70HZN2hBtuzr2M:&tbnh=67&tbnw=111&prev=/images?q=angus+cow&svnum=10&hl=en&rlz=1T4GFRC_en___US202

http://images.google.com/imgres?imgurl=http://www.mnzoo.com/animals/family_farm/images/cattle/brswiss.jpg&imgrefurl=http://www.mnzoo.com/animals/family_farm/animals/brownswiss.asp&h=158&w=221&sz=16&hl=en&start=3&tbnid=BLNLMwZC0Q6bzM:&tbnh=76&tbnw=107&prev=/images?q=brown+swiss+cow&svnum=10&hl=en&rlz=1T4GFRC_en___US202&sa=G

http://images.google.com/imgres?imgurl=http://www.ni-photos.jmcwd.com/jersey-cow2.jpg&imgrefurl=http://www.ni-photos.jmcwd.com/nature.html&h=768&w=1024&sz=178&hl=en&start=5&tbnid=Rp_n8jBgpHA2fM:&tbnh=113&tbnw=150&prev=/images?q=jersey+cow&svnum=10&hl=en&rlz=1T4GFRC_en___US202

Assessment Population: Angus – Brahman – Romosinuano (n = 650)

Diallele = Mating system involving all breed-parent combinations to create hybrids.

Admixed Population = Group of animals with mixed ancestry because have inherited some fraction of their genome from different ancestry populations (e.g., populations stratification).

Ancestry-Informative Markers (AIM) = Set of polymorphisms with significant differences in allele frequencies between breed groups.


STARS – Brooksville, FL

Gene Name SNP_assayed SNP with MAF >.10

Growth Hormone Receptor (GHR) 1 --Glycosylation-Dependent Cell Adhesion Molecule 1 (GLCM1) 9 --Insulin-like Growth Factor 1 (IGF1) 6 --Insulin-like Growth Factor Binding Protein 6 (IGFBP6) 5 --Pregnancy-Associated Plasma Protein (PAPPA) 9 2Pro-Melanin Concentrating Hormone (PMCH) 5 --Signal Transducer and Activator of Transcription 2 (STAT2) 6 1Signal Transducer and Activator of Transcription 6(STAT6) 6 --Suppressor of Cytokine Signaling 2 (SOCS2) 12 --

Total 59 3

3 Polymorphic Markers56 Ancestry Informative Markers

Genotyping Results


Phylogenetic TreeFrom AIMs

Ancestry proportion of Angus, Brahman and Romosinuano

(clusters outputted from Structure®; Bayesian-based) Ancestry Clusters

Breed Group N 1 2 3

Angus 66 0.012 0.694 0.294Brahman 59 0.878 0.029 0.082Romosinuano 129 0.040 0.461 0.498Angus x Brahman 116 0.462 0.334 0.204Angus x Romosinuano 136 0.039 0.497 0.464Brahman x Romosinuano 143 0.474 0.234 0.292


Reproductive Traits:Continuous: age of first calving, calving interval, days to calving Categorical: pregnancy %, Perfect, %

Mixed Model yijklmn = µ + Ai + Bj + Ck + Dl + Em + Fn + eijklmn, Proc Mixed,

Proc Glimmix

• yijkl= phenotypic value of traits,• µ = population mean or frequency,• Ai = fixed effect of SNP genotype or haplotype,• Bj = fixed effect of year of birth,• Ck = fixed effect of age of dam (BIF, 2006), • Dl = covariate of coefficient of ancestry,• Em = covariate of Julian birth date, • Fn = random effect of sire using the Z statistic to test if Ho:σw

2 = 0 (Littell et al., 1996),

• eijklmn = random residual error.


Least squares means ± SE and levels of significance for fertility traits among SNP genotypes of PAPPA

Trait N SNP 1 PAPPA 11 12 22 P-value

Age at First Calving, d 551 768.6 ± 36.8 787.9 ± 9.6 778.8 ± 11.2 0.9999Perfect, % 551 53.8 56.3 53.7 0.8402Calving Interval, d 441 500.1 ± 37.4a 450.4 ± 9.6b 421.2 ± 8.0b 0.0595Days to Calving, d 441 448.8 ± 38.9a 387.6 ± 10.3b 360.9 ± 8.6b 0.0289Pregnancy Rate, % 640 77.8a 85.8b 89.6b 0.0136

Trait N SNP 2 PAPPA 11 12 22 P-value

Age at First Calving, d 548 773.2 ± 13.7 785.7 ± 9.7 781.9 ± 13.3 0.9999Perfect, % 548 52.7 59.2 52.2 0.2823Calving Interval, d 439 403.4 ± 13.2a 439.9 ± 8.6b 445.1 ± 12.1b 0.0484Days to Calving, d 439 344.9 ± 13.8a 379.4 ± 9.1b 384.4 ± 12.7b 0.0288Pregnancy Rate, % 640 90.6a 87.9b 86.3b 0.0401

a,bWithin a row, means without a common superscript differ (P < 0.05)


HH Hh hh

HH Hh hh

SNP Genotypes

D

ays

to C

alvi

ng,

d

Cal

ving

In

terv

al, d

Interaction among STAT2 SNP genotypes and ancestral clusters (ALL BREED GROUPS)

Bos taurus Cluster (2 & 3)


Brahman Cluster (1) (Genotype*Cluster; P < 0.05)



HH Hh hh

HH Hh hh

SNP Genotypes

Day

s to

Cal

vin

g, d

C

alvi

ng I

nte

rval

, d

Interaction among STAT2 SNP genotypes and ancestral clusters (PUREBRED GROUPS)







Objective 1: Conduct a SNP-based whole-genome scan to identify QTL associated with heifer pregnancy rate.

Identification of Molecular Markers to Improve Fertility of Beef Cattle (USDA-NRI 200835205-18751)

Discovery: QTL detection + transcriptome & proteome

Relationship fostered from efforts of IBBA –BIC.


Population Description1.Brangus heifer pregnancy records collected since 1993. 2.DNA and phenotypes on >800 heifers from > sires born 2005, 2006, 2007.3.Trait, pregnant as a yearling, yes (80.6%) or no (19.4%).4.Simple statistics for growth and carcass traits:

Birth weight 77.2 ± 0.4 lbs205-day weight 532.2 ± 2.2 lbs365-day weight 799.0 ± 3.3 lbsRibeye area 9.8 ± 0.05 in2

Fat thickness 0.24 ± 0.002 inintramuscular fat % 4.24 ± 0.032 %

0

20

40

60

80

100

Not Pregnant Pregnant

0

10

20

30

40

50

60

70

80

Spring Fall

Number

of Sires

Heifer Pregnancy Percent

0

20

40

60

80

100

Not Pregnant Pregnant

0

10

20

30

40

50

60

70

80

Spring Fall

Number

of Sires

Heifer Pregnancy Percent

Historic Data provided by Matt Jones


Discovery: QTL detection + transcriptome & proteome

1 2 3 5 6 7 8 94

29

11 12 14 15 16 17

18

13

19 20 21 23 24 25 26 2722

10

28

Birth Weight , 365-day WeightIntramuscular Fat %Ribeye AreaRib Fat

Pregnant as yearling: (12 to 15 mo) of age to calve by 24 mo.

http://bigs.ansci.iastate.edu/bigsgui/login.htmlBIGS, Bayes C

x/y


Pipeline for Marker Discovery: Example from Chromosome 2

QTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID

Assessment and Validation.

http://bigs.ansci.iastate.edu/bigsgui/login.htmlBIGS, Bayes C

http://www.animalgenome.org/


Pipeline for Marker Discovery: Example from Chromosome 2

QTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID

Assessment and Validation

GBrowse

Cattle Genome Trackshttp://www.animalgenome.org/cgi-bin/gbrowse/cattle/#search

Cattle Genome Tracks


Pipeline for Marker Discovery: Example from Chromosome 2QTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID


Milt Thomas, Department of Animal and Range SciencesChemical Analysis and Instrumentation Lab

QTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID


RNA-Seq-Alpheus

Reads/million

TranscriptMatches

GeneMatches

Calcium binding, maganese ion binding, sugar binding, transferace activity, protein amino acid O-linked

Axon-axon contacts: link neurons with supporting schwann cells, adhesion, proliferation,apoptosis

Cell cycle arrest, regulation of mitotic cell cycle

Endonuclease reverse transcriptase

Potassium inwardly-rectifying channel, subfamily J, member 3, G protein-activated inward rectifier potassium channel 1

Cattle Genome Tracks

Transcriptomic Expression

Genomic Expression

Gene NamePre

pubertalPubertal

Prepubertal

Pubertal

LOC100138296

KCNJ3 17.7 14.03

LOC10013916

GALNT13

LOC526988 1.03 2.21

RPRM 1.38 23.67 0.99 17.57

LOC100139440 10.48 10.31


Chemical Analysis & Instrumentation LabQTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID


Quantitative Proteome Profiling FT-ICR Mass Spectrometry


QTL detection

SNPlotZ

GBrowse

Transcriptome

Proteome

Functional SNP ID


Objective 2: Develop data and DNA resources! a. Different environments and production systems.

AES = 60%; seedstock = 25%; commercial = 15%Influence of Angus = 95%; Continental = 25%; Brahman = 40%

b. Opportunity for multiple traits.


1

23

45

6

78

9

10

11

12

1314

15

16

17

18

19

A

B

C

D

Locations of Cooperators (numbers 1-23) and Research Scientists (letters A-D)

2021

22

What about Angus?

Triennial Reproduction Symposium, ASAS, Montreal.

J. F. Taylor, S. D. McKay, J. E. Decker, D. Vasco, M. C. McClure, J. W. Kim, M. A. Rolf, T. Taxis, and R. D. Schnabel. 2009. Application of genome based technologies for identifying genes and their expression that are important for livestock reproduction. J. Anim. Sci. 87 (E-Suppl. 1): 2.

~ 20 SNP explain 32.1% of the variance in heifer pregnancy EPD.

BovineSNP50 results from a deep Angus pedigree.


ConclusionsMolecular Advancements in Reproduction: from Hair to Calves1.Collaborations needed to move research forward. 2.Resequencing candidate genes can yield effective SNP for genotype to phenotype associations. 3.Genomic approaches help isolate functional mutations. 4.Admixture???

Much work to do!


Discussion

molecular advancements in reproduction: from hair to calves collaborator s & dr. milt thomas

Documents

beef breeds n

fertility of beef cattle

assayed snp

brangus n

ghigf axis discovery

different ancestry populations

large herd managers

breed groups