hoa nguyen-phuc - phd defense - 2015-08-03 final version
TRANSCRIPT
Hoa Nguyen-‐Phuc
August 3, 2015
Spatial genetic characterizations of neutral and adaptive variation
of Red Junglefowl (Gallus gallus) in South Central Vietnam
Introduction
Part I Neutral genetic variation
Part II Genetic structure dependence to landscape patterns
Part III Adaptive genetic variation
PhD-‐wide conclusions
Outline
(1) Turkey, (2) Guinea pig, Llama, (3) Pig, Rabbit, (4) Cattle, Donkey, (5) Cattle, Pig, (6) Cattle, Chicken, (7) Horse, (8) Yak, (9) Pig. Swamp Buffalo, Chicken,
(10) Pig, Chicken, (11) Dromedary, (12) Reindeer.
FAO 2007
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL ive stock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study Design
40-‐plus livestock species.
10 domestication centers.
Only 2 wild progenitor species still exist…
CENTERS OF
ORIGINS -‐
LIVESTOCK
DOMESTICATION
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL ive stock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study Design
Poultry industry is a multi-‐billion business.
40 billion chickens produced worldwide annually.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL ive stock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study Design
‘Selection wall’ of growth and
reproductive traits.
Susceptibility to zoonotic diseases.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL ive stock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study Design
Indigenous or heritage breeds for maintaining poultry genetic diversity
“Đông Tảo” heritage breed in Vietnam -‐ $US 1,000 for this ‘broiler’!
Green
South Central Vietnam
Rubin et al 2010
Red
Grey
Ceylon
Green
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL i vestock Domestication History Evolution and Distribution of Gallus Spat ial Ecology Study Design
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
F2 Hybrid
Red Junglefowl
“Bamboo” Breed
L i vestock Domestication History Evolution and Distribution of Gallus Spat ial Ecology Study Design
Storey et al 2010
①Very broad extent: a common avian species ② Regional extent: terrestrial birds limited by landscape features?
L i vestock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study DesignIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
③ Local extent: an ‘island’ model
④ Ecology & demography extent: niche and a polygynous breeding
Human density 190/km2
Gallus gallus spadiceus
Gallus gallus gallus
1960s
1920s
Before 1880
Eclipse ‘clean genotype’ plumages (Peterson & Brisbin 1998)
Sub species
L i vestock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study DesignIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Research Goal:
To investigate the spatial processes of neutral and adaptive genetic variation in wild Red
Junglefowl and the interactions with the underlying environment.
Research Questions:
① What are major processes influencing the (broad & fine-‐scale) spatial neutral
variation?
② Are landscape features deciding factors for (fine-‐scale) genetic variation?
③ Are the functionally adaptive genes more diverse than the neutral genes? Are there
higher diversity in wild Red Junglefowl than in domestic chickens?
• 7 field sites (~ 50,000ha). • East vs West sites of the AnnamiteMountain Range.•Major sites: CTN, HBA, LGO, YDN (>30 samples).• Lowland vs Highland sites.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ionsL i vestock Domestication History Evo l ution and D istribution of Gallus Spat ial Ecology Study Design
Highland -‐ HBAHighland -‐ YDD
Lowland -‐ CTNLowland -‐ LGO
14
Elephants in CTN and YDN
15
Walking snare: Very low capture efficiency (~ 0.25 bird/day).
Efforts to maintain the snare lines. Captured more*
Partridges, Peafowls, and Pheasants.
Decoy ‘baiting’ rooster: Effective & flexible method (~ 1.8
bird/day). Capture more territorial males.
Sample size: 212 birds -‐ 172 roosters, 23 hens, 17 chicks -‐
(and > 100 Phasianid birds) of blood and swap samples.
Introduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
Research Question:What are major processes influencing the
(broad & fine-‐scale) spatial neutral variation of Red Junglefowl?
Hypothesis: The ground-‐dwelling Red Junglefowl have
substantial inter-‐population variation due to (broad-‐scale)
habitat fragmentation and (fine-‐scale) movement.
Central Highland coffee just in Starbuck’s stores this weekend!
Introduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
Reddish hackles
Golden-‐yellow hackles
Introduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
Methods:
• Amplified Fragment Length Polymorphism AFLP
(Eco+CAT/Eco+GA and Eco+GG/Eco+GC).
• Statistics of population genetics by AFLPsurv.
• Variance-‐based analyses -‐ Principal Component
Analysis PCA and sPCA.
• Distance-‐based -‐ Correlogram.
• Genetic models -‐ Bayesian clustering Geneland
R-‐package: Voronoi tessellation procedures,
spatially explicit, forward MCMC (1,000
iterations in ‘super’ computer) to estimate K
number of genetically distinct populations.
Sites n PLP He PA FST
BDP 5 0.296 0.1242 1 -‐-‐
NCA 6 0.386 0.1380 3 -‐-‐
TKU 9 0.432 0.1432 9 -‐-‐
CTN 44 0.445 0.1533 16 0.0713
HBA 56 0.427 0.1492 8 0.1392
LGO 34 0.368 0.1243 9 0.0625
YDN 58 0.458 0.1916 33 0.1559
TOTAL 212 389 / 431 0.1420 -‐-‐ 0.1028
Table 1.1: Statistics of population genetics for 7 field sites
He expected heterozygosity, PLP proportion of polymorphic loci,
PA private alleles, FST genetic differentiation
CTN
YDN
HBA
LGO
BDP
NCA
TKU
Population Structure Spat ial Range MetapopulationIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
PRINCIPAL COMPONENT ANALYSIS
Population Structure Spat ial Range MetapopulationIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
GLOBAL BAYESIAN CLUSTERING
UPGMA dendrogram from 1,000 iterations
9 populational clusters
from 7 sampling sites
Population Structure Spat ial Range MetapopulationIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
GLOBAL BAYESIAN CLUSTERINGModel with all 212 RJFs, UPGMA dendrogram from 1,000 iterations
LGO CTN
YDNHBA
Population Structure Spat ial Range MetapopulationIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
YDNHBA
Strong population structure particularly at local scale -‐congruency between Bayesian clustering genetic models, and:
• Ordination variance-‐based method PCA
• Sampling localities
CTN
YDN
HBA
LGO
CTN YDNHBA LGO
Population St ructure Spat ial Range MetapopulationIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
sPCA eigenvalues
sPCA’s regressed PC scores
Correlogram
[Local] Genetic clustering
Spatial range of ≤ 6km
X coordinate
Ycoordinate
km
X coordinate
Ycoordinate
Population St ructure Spat ial Range Metapopulat ionIntroduction Neutral Variation Spatial Dependence Adaptive Variation Conclus ions
Conclusions of neutral genetic variation:
• Strong population structure of Red Junglefowl at both
broad and fine scales.
• Congruencies in PCA, sPCA, correlogram, Bayesian
clustering in detecting structure and spatial patterns.
• Classicalmetapopulation of Red Junglefowl in
geographical context.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
G
L
Voronoi Posterior cluster membership
Elevation
Research Question:
Are landscape features deciding factors for (fine-‐scale)
genetic variation?
Hypothesis:
Geographic distance, movement, and demography (e.g.
philopatry) are important factors explaining local
genetic variation in Red Junglefowl.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
CCA
QStandardized
Matrixn x n
Regression Q to L
Qfit
Explained Variancesn x n
Qres
Residual Variances
n x n
Variogram
GDissimilarity Coefficients
n x n
LCost Distances
n x n
CA
① ②
③
④
⑤
Methods:Multi-‐Scale Ordination (MSO) of gradient
analysis (CA & CCA), regression, variogram.
① G -‐ AFLP genetic dissimilarity coefficients
4 major field sites & simulated data
② L -‐ Landscape cost distances (least-‐cost)
③ check for CSR
④ Ordination
⑤ Regression
⑥ Variogram
K̂
⑥
K̂
Total Covariances (Genetics to Landscapes)
Residual Variances (Autocorrelation in Genetics)
CA
km
Simulated genotypes(random)
Observed genotypes
FST = 0.071 FST = 0.156FST = 0.063FST = 0.139varia
nces
Ycoordinate
km
CTN YDNLGOHBAIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
CTN YDNHBA LGO
r distance of argument Poisson
X coordinate
Simulated genotypes (structured)
FST = 0.300 FST = 0.300FST = 0.300FST = 0.300
K̂
Point Pattern, Genetic Structure, & Variograms Spat ial Dependence Landscape Classification
change FST
change Spatial Structure FST = 0.300
Spatial Dependence
(CCA)
CTN YDNLGOHBAPoi nt Pattern, Genetic Struc ture, & Variograms Spat ial Dependence Landscape Classification
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Topography(CA)
Inertia 1.06%
Inertia 1.38%
Observed genotypes
(CA)
CTN YDNHBA LGO
Point Pattern, Genetic Struc ture, & Variograms Spat ial Dependence Landscape ClassificationIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
CTN
YDN
HBA
LGO
True altitude (0, 2400m) → (1,100) log transform → (0,2) categorical → 1,2,3,4
Point Pattern, Genetic Struc ture, & Variograms Spat ial Dependence Landscape ClassificationIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Conclusions of spatial dependence:
• Multi-‐scale ordination (MSO) methods can effectively detect spatial genetic covariances
in variogram analysis.
• No spatial dependence of spatial genetic patterns to landscape features.
E.g. Mantel tests have Type I error in matrix permutation of two correlated data sets.
• Classification of landscape patterns is good for model validation.
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Geographic Distance
(A)
Geographic Distance
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Hess & Edwards 2002
Shiina et al 2004
Major Histocompatibility Complex (MHC)
84-‐SNP panel in chicken MHC
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Research Question:
Are 84-‐SNP MHC genes retained at high diversity and high variation compared to neutrality variation (Chapter 1) and to intensively-‐selected chicken lines?
Hypothesis:
The wild Red Junglefowl’ MHC genes are adaptive (under Balancing Selection) and independent to areas.
Methods:
• 84-‐SNP panel with high-‐density SNP detection KASP platform.
• Bayesian haplotype phasing construction.
• Nucleotide analyses and statistics.
• Recombination and Linkage Disequilibrium analyses.
Selection types
Haplotype var iation Recombinant Popula tion structureIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Site CTN HBA LGO YDN
Sample 46 56 39 58
Unique haplotypes/chromosomes 92/92 82/112 48/78 91/116
Haplotype diversity Hd 100% 98.97% 97.86% 99.39%
Gene diversity 76 76 71 75
Average number of difference K 24.5745 23.9067 23.4393 24.1789
Nucleotide diversity π 0.2926 0.2846 0.2790 0.2878
Tajima’s D 2.1236 2.1381 2.0641 2.2986
Segregation site S 76 76 71 75
Recombination parameter 0.0065 0.0020 0.0013 0.0024
Recombination rate (average) 1.3100 1.3000 1.13000 1.3400
ρ̂
ρ
Table 3.2: Haplotype
diversity and statistics
of Red Junglefowl in
four sampling sites
Haplotype var iation Recombinant Popula tion structureIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Line type No. Samples
No. Haplotypes
Nucleotide diversity π
Haplotype diversity Hd
Haplotype percentage
Red Junglefowl -‐ ALL 199 310 77.89%
CTN 46 92 0.29260 100.00% 100.00%HBA 56 82 0.28460 98.97% 73.21%LGO 39 48 0.27900 97.86% 61.54%YDN 58 91 0.28780 99.39% 78.45%Broiler-‐UAB-‐AMC-‐1957 71 8 0.25347 80.42% 5.63%Broiler-‐UAB-‐AMC-‐1978S 64 5 0.19893 63.78% 3.91%Broiler-‐UAB-‐AMC-‐1978D 78 10 0.25537 80.98% 6.41%Broiler-‐UGA-‐ACRB 100 11 0.25796 80.32% 5.50%Broiler-‐UGA-‐ARB 71 4 0.21043 70.95% 2.82%Broiler-‐UAR-‐RB 54 7 0.26625 75.86% 6.48%Standard-‐UAB-‐BPR 76 4 0.24627 73.50% 2.63%Standard-‐UAB-‐SBPR 80 4 0.25612 70.57% 2.50%Standard-‐USK-‐BPR 96 2 0.04281 17.13% 1.04%Standard-‐UAB-‐SRIR 80 4 0.11339 40.58% 2.50%Standard-‐UAB-‐WL 72 3 0.10629 33.41% 2.08%Standard-‐UAB-‐LS 77 3 0.18736 57.92% 1.95%Standard-‐UAB-‐NH 73 4 0.05272 55.93% 2.74%Standard-‐Ill-‐NH 94 3 0.05874 33.23% 1.60%Standard-‐UAB-‐BL 76 1 0.00000 0.00% 0.66%Synthetic-‐Ill-‐PC 92 3 0.13741 52.47% 1.63%Synthetic-‐USK-‐EPI 97 9 0.23621 66.90% 4.64%
Table 3.4:
Comparison of
MHC haplotypes of
Red Junglefowl vs
domestic chickens
~ 99%
~ 3%
~ 80%~ 0.29
~ 56%~ 0.17
Haploty pe v a riation Recombinant Popula tion structureIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
CTN HBA YDNLGO
Recombination: consistently high across populations with significant recombination ‘hotspots’.
0065.0ˆ =ρ3100.1=ρ
0020.0ˆ =ρ3000.1=ρ
0013.0ˆ =ρ
1300.1=ρ
0024.0ˆ =ρ
3400.1=ρ
Haploty pe v a riation Recombinant Popula tion structureIntroduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
D’ < 1, LOD < 2
D’ < 1, LOD >= 2
D’ = 1, LOD < 2
D’ = 1, LOD >= 2
CTN HBA YDNLGO
Linkage Disequilibrium (LD): little LD across the 84-‐SNPs, but evidence of common LD blocks.
CTN
HBA
YDN
LGO
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
CTN HBA YDNLGO
Haplotype Networks
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
CTN HBA LGO YDN
Haplotype Network
(for 4 populations):
too variable, almost
random for structure.
CTN
HBA
YDN
LGO
A
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
LGO
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
YDN
P
P
PP
P
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
Source of Variation Degree of freedom
Sum of Squares
Variance components
Percentage of variation
Among K populations 3 2.79 0.0042 0.83%
Among N individuals within K populations 195 101.18 0.0237 4.66%
Within N individuals 199 94.00 0.4724 94.51%
Total 397 197.98 0.4998 100.00%
FST = 0.0038
(FST = 0.1028 by neutral AFLPs)
Table 3.3: Analysis of Molecular Variance (AMOVA)
Haploty pe v a riation Recombinant Popula tion s tructureIntroduction Neutra l Variation Spatial Dependence Adaptiv e Varia tion Conclus ions
Conclusions of MHC adaptive variation:
Tremendous diversity and variation in the 84-‐SNP MHC haplotypes
• Haplotype diversity ~ 100%.
• Unique haplotypes ~ 80% of total haplotypes (compared to ~ 3% in domestic chickens).
• High recombination rates with a few significant recombination hotspots.
• Little linkage disequilibrium blocks.
• No population structure, 94% variation among individual birds.
Under local adaptation and balancing selection.
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Species movement -‐ Classical metapopulation structure
(FST = 0.1028) by barriers and spatial range.
NEUTRAL VARIATION ADAPTIVE VARIATION
Balancing selection -‐ Local adaptation (FST = 0.0038, 94% variation
between birds, 80% unique haplotypes) to environmental condition.
Landcover Layer H5N1 exposure
Var i ation i n geographical context Agr i cultural d iversity Conservation implication
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
One of the richest genetic resources to serve
as genetic resources in future breeding and
conservation programs.
3% MHC diversity in chickens vs. 80% in Red Junglefowl
Wild maize distribution
Variation in geographical context Agr i cultural d iversity Conservation implication
Introduction Neutra l Variation Spatial Dependence Adaptive Variation Conclus ions
Spatial genetic patterns help to understand other threatened Phasianids in
Southeast Asia and landscape management.
Common Quail Scaly-‐breasted Partridge Germain’s Peacock Pheasant
Variation in geographical context Agri cul tural d iversi ty Conservation implication
Acknowledgements
ü Mark Berres
ü Zach Perry, Triet Tran, Monica Turner, Jun Zhu
ü Sean Schoville, Janet Fulton, Jeb Barzen
ü Staff and trappers of the seven field sites
ü Animal Sciences, Fadl Lab, Kirkpatrick Lab, friends @ Berres Lab, @
HCMC University of Science, @ HCMC Zoo, and @ Southern Institute
of Ecology
ü Hatch Fund, Halpin Fund, Animal Sciences, Rufford Small Grant
Thank you!
Questions, please!