population structure, heritability, and polygenic risk · 10/17/2016 · europeans east asians...
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Population structure, heritability, and polygenic risk
Alicia MartinDaly Lab
October 18, 2016
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Project goals
✓ Call local ancestry in large case/control PTSD cohort of African Americans
• Estimate heritability using local ancestry tracts. Compare/contrast this estimate with SNP-based heritability in this and European cohort (in progress)
• Perform admixture mapping
• Considerations: transferability of polygenic risk scores, cross-population heritability
(Work with Karestan Koenen, Mark Daly, Laramie Duncan, Caroline Nievergelt)
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Data overview
Study PI Analyst NTotal NAAData label
1 GTP (Grady Trauma Project) Kerry Ressler Lynn Almli 4752 3492 gt2y2 Detriot (DNHS) Monica Uddin Guia Guffanti 812 650 dnhy3 Genetics of Substance Dependence Goel Gelernter Pingxing Xie 5451 3100 gsdy4 Marine Resilience Study Caroline Nievergelt /
Dewleen Baker Adam Maihofer 4036 226 mrsy
5 Family Study of Cocaine Dependence Laura Bierut Louis Fox 1271 653 fscy6 COGEND Laura Bierut Louis Fox 2768 711 cogy
7 Nurses Health Study Karestan Koenen Andrew Ratanatharathorn 1378
8 Stein South Africa Dan Stein / Kerry Ressler Lynn Almli 434
9 Ohio National Guard Israel Liberzon Tony King 239 Summary Statistics from imputed data
10 Duke J. Beckham / M. Hauser / A. Ashley-Koch Melanie Garrett 1963
11 National Center for PTSD (Boston) Mark Miller / Mark Logue Mark Logue 652
Total 23,756 8,832
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Local ancestry calling strategy
1. Merge intersecting genotyped SNPs (N=421,607 with MAF > 0.05)
2. Phase aggregated dataset with HAPI-UR 3x and take best combined phase
3. Split jointly phased haplotypes into reference + 50 sets of admixed samples for computational feasibility
4. Aggregate local ancestry calls across all runs
5. Collapse local ancestry output
gt2y dnhy gsdy mrsy fscy cogy YRI CEU+ + + + + + +
AA + reference genos
AA + reference jointly phased haplotypes
Local ancestry
run 1
Local ancestry run 50
Local ancestry
run 2
Local ancestry run 49
+ ++ +...
Combined local ancestry calls
Collapsed bed files, ancestry karyograms, and plink files
1
2
3
4
5
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Heritability estimates
h2 estimate Kinship matrix SE N
h2g REAP 0.018 0.046 7548
h2g GCTA GRM 0.02 0.048 7248
h2γlocal ancestry GRM ? ?
h2
Zaitlen, N., et al. (2014). Nat. Genet. 46, 1356–1362.
h2� =phenotypic variation described by variation in local ancestry
�2� =phenotypic variation explained by variation in local ancestry
�2e =residual phenotypic variance
h2� =
�2�
�2� + �2
e
FSTC =weighted allele frequency di↵erences
between ancestral populations at causal loci
✓ =genome-wide ancestry proportions
h2� =2FSTC✓(1� ✓)h2
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1000 Genomes phase 3 populations
Auton, A., et al. (2015). Nature 526, 68–74.
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Substantial global genetic diversity in 1000 Genomes
CEUGBR IBS TS
IFIN KH
VCHSCHBJPT
CDX
MSLYRIESNLWK
GWD
GIH PJ
LITU BE
BSTU
ACB
PURCLMMXLPEL
ASW
K=5
K=6
K=7
EuropeansEast Asians Africans
SouthAsians
AdmixedAmericas
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Varying admixture proportions across populations in the Americas
0.00.20.40.60.81.0
NAT
CEU
YRI
0.00.20.40.60.81.0
ACB
ASW
0.00.20.40.60.81.0
PUR
CLM
MXL
PEL
Referencepanel
AfricanAmerican
Hispanic/Latino
African AmericansACB = African Caribbean in BarbadosASW = African Ancestry in SW US
Hispanic/LatinosCLM = ColombiansMXL= Mexicans
PUR = Puerto RicansPEL = Peruvians
NAT = Mao et al, (2007). AJHG. 80, 1171–1178.
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Admixed samples in the Americas
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Admixture tracts inform subcontinental-level ancestral populations
RFMix: Maples, B.K., et al (2013). AJHG. 93, 278–288.
HG01893 (Peruvian)
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Ancestry-specific PCA provides insight into subcontinental admixture origins
−5
−4
−3
−2
−1
0
1
−1.0 −0.5 0.0 0.5 1.0PC1
PC2
ReferenceAFREURNAT
AdmixedACBASWCLMMXLPELPUR
ASPCA: Moreno-Estrada, A., et al. (2013). PLoS Genetics. 9, e1003925.
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African Americans have northern European tracts, Hispanics have southern European tracts
ASPCA: Moreno-Estrada, A., et al. (2013). PLoS Genetics. 9, e1003925.
−3
−2
−1
0
1
−2 −1 0 1 2PC1
−PC2
ReferenceFINCEUGBRIBSTSI
AdmixedACBASWCLMMXLPELPUR
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African Americans have African tracts closest to Nigerian reference panel
−2
−1
0
1
−1 0 1 2−PC1
PC2
ReferenceESNGWDLWKMSLYRI
AdmixedACBASW
ASPCA: Moreno-Estrada, A., et al. (2013). PLoS Genetics. 9, e1003925.
GWD
LWKMSL YRI
ESN
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Africans have more genetic variation than out-of-Africa populations
1000 Genomes Project Consortium. (2015). A global reference for human genetic variation. Nature 526, 68–74.
AFR
AMR
EAS
EUR
SAS
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Biased genetic discoveries
African
LatinoEast Asian
Middle Eastern
European
OceanicSouth Asian
Global population
East Asian
European
PGC GWAS(SCZ, BIP, MDD, ADHD)
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Europeans (and Hispanic/Latinos) are overrepresented in disease databases
1000 Genomes Project Consortium. (2015). A global reference for human genetic variation. Nature 526, 68–74.
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Computing polygenic risk scores from summary statistics
• LD clumping for all variants with MAF ≥ 0.01:
• Apply p-value threshold (p=0.01)
• Thin for LD within window (R2=0.5, window=250kb)
(P+T in LDpred paper)
X =mX
i=1
gi�i
!
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Polygenic risk score for height reflects adaptive event in Europeans… and bias
Wood, A.R., et al. (2014). Nature Genetics 46, 1173–1186.
0
2000
4000
6000
0.0e+00 2.5e−04 5.0e−04 7.5e−04 1.0e−03Polygenic Risk Score
Den
sity Region
N.EuropeS.Europe
European height score
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Polygenic risk score for height reflects adaptive event in Europeans… and bias
Wood, A.R., et al. (2014). Nature Genetics 46, 1173–1186.
0
2000
4000
6000
0.0e+00 2.5e−04 5.0e−04 7.5e−04 1.0e−03Polygenic Risk Score
Den
sity Region
N.EuropeS.Europe
European height score
0
2500
5000
7500
10000
0.0e+00 2.5e−04 5.0e−04 7.5e−04 1.0e−03Polygenic Score
Den
sity
Superpopulation
AFRAMREASEURSAS
Global height score
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Polygenic risk score for Type II diabetes highlights role of demography
European: Gaulton, K.J., et al. (2015). Nat. Genet. 47, 1415–1425.Multi-ethnic: Mahajan, A., et al. (2014). Nat. Genet. 46, 234–244.
0
25
50
75
100
0.54 0.56 0.58 0.60Polygenic Score
Den
sity
Superpopulation
AFRAMREASEURSAS
Global T2D (Multi−ethnic) score
0
5
10
15
20
25
0.50 0.55 0.60 0.65Polygenic Score
Den
sity
Superpopulation
AFRAMREASEURSAS
Global T2D (EUR) score
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Coalescent model for simulation framework
Demographic model: Gravel, S., et al. (2011). Proc. Natl. Acad. Sci. U. S. A. 108, 11983–11988.msprime: Kelleher, J., Etheridge, A.M., and Mcvean, G. (2015). PLoS Comput Biol 1–22.
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Simulation steps
• Simulate for chr20 (μ=2e-8 mutations/(bp*generation)) genotypes with HapMap recombination map for 200k each: Africans, East Asians, Europeans
• Assign “true” causal effect sizes to m evenly spaced variants as:
• As before, define X as:
• Normalize:
• Compute true PRS as (such that total variance is h2):
X =mX
i=1
gi�i
� ⇠ N(0,h2
m)
ZX =X � µX
�X
G =ph2 ⇤ ZX
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Simulation steps
• Compute the total liability for each individual (epsilon is standard normal noise), such that:
• Assuming a 5% prevalence, assign 10,000 European individuals at the most extreme end of the liability threshold “case” status. Randomly assign different 10,000 European individuals “control” status.
• Run a simulated GWAS, computing Fisher’s exact test for all sites with MAF ≥ 0.01.
• Clump SNPs into LD blocks for all sites with p≤1e-2, R2≥0.5 in Europeans, and window size of 250kb.
• Compute inferred PRS from summary stats and ⍴ with true PRS• Evaluate over 50 simulations for m = 200,500,1000 and
h2=0.33,0.50,0.67
T =ph2 ⇤ ZX +
p1� h2 ⇤ Z✏
h2 =�2g
�2g + �2
✏
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True vs inferred PRS with same causal variants, different effect sizes are inconsistent
h2=0.67, m=1000
G H I
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Best performance in European study population
h2=0.67, m=1000, 50 replicates
●
●
●
●
●
●
●
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200 500 1000
0.00
0.25
0.50
0.75
1.00
AFR
EAS
EUR
ALL
AFR
EAS
EUR
ALL
AFR
EAS
EUR
ALL
# Causal variants
Pear
son'
s co
rrela
tion
Superpopulation
AFREASEURALL
h2 = 0.67
●
●
●
●
●
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●
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200 500 1000
0.00
0.25
0.50
0.75
1.00AF
R
EAS
EUR
ALL
AFR
EAS
EUR
ALL
AFR
EAS
EUR
ALL
# Causal variants
Pear
son'
s co
rrela
tion
Superpopulation
AFREASEURALL
h2 = 0.67
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http://biorxiv.org/content/early/2016/08/23/070797