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Lecture 12: Population structure
Statistical Genomics Lecture 12: Population structure Zhiwu Zhang Washington State University
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Outline Inflation of P value population subdivision
Population structure Principal component
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QTNs 0n CHR 1-5, leave 6-10 empty
myGD=read.table(file=" myGM=read.table(file=" setwd("~/Dropbox/Current/ZZLab/WSUCourse/CROPS545/Demo") source("G2P.R") source("GWASbyCor.R") X=myGD[,-1] index1to5=myGM[,2]<6 X1to5 = X[,index1to5] set.seed(99164) mySim=G2P(X= X1to5,h2=.75,alpha=1,NQTN=10,distribution="norm") p= GWASbyCor(X=X,y=mySim$y)
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False positives color.vector <- rep(c("deepskyblue","orange","forestgreen","indianred3"),10) m=nrow(myGM) plot(t(-log10(p))~seq(1:m),col=color.vector[myGM[,2]]) abline(v=mySim$QTN.position, lty = 2, lwd=2, col = "black")
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LD across chromosomes left=X[, index1to5] right=X[, !index1to5]
qtn=left[,mySim$QTN.position] r=cor(qtn,X[, !index1to5]) hist(r)
![LD across chromosomes left=X[, index1to5] right=X[, !index1to5]](http://slideplayer.com./slide/13532550/82/images/5/LD+across+chromosomes+left%3DX%5B%2C+index1to5%5D+right%3DX%5B%2C+%21index1to5%5D.jpg "qtn=left[,mySim$QTN.position] r=cor(qtn,X[, !index1to5]) hist(r)")
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Linkage equilibrium Random mating Control Case A G AG TG AC TC T G T C
Disease AG TG AC TC Random mating Control Case T G T C
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Association study Marker Control Case 6 2 X2=4(2*2/4)=4, df=1, P=4.5%
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Linkage disequilibrium (LD)
Disease T G T C Random mating Geography Breeding and family All A as control and half T as case AG TG TC Control Case
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TROPICAL- SUBTROPICAL
CM37 K148 R4 Mo46 OH7B Ky228 Hi27 DE-3 NC360 NC344 K4 Mo47 A682 MO17 Mt42 CMV3 CO106 B97 Mo45 Yu796-NS NC362 NC262 CI91B W401 NC364 NC342 NC258 CI187-2 NC222 MS153 CI3A A556 B77 W117HT B103 Tzi16 Tzi25 B105 DE811 DE1 NC290A B164 SD40 A641 A214N NC250 STIFF STALK DE-2 B57 NC236 CM7 C123 I205 N7A N28HT H100 H84 NON STIFF STALK ND246 CO109 H105W C103 A632 A635 B64 CO125 B79 B68 A634 H91 B14A B84 Hy Ky21 A661 WD CM174 CM105 B104 B76 CI21E A554 B75 Os420 MS71 38-11 NC260 B37 Mo44 NC328 R229 Mo1W R168 A679 A680 N192 B109 NC368 NC294 NC326 B73Htrhm B73 NC292 NC324 Pa875 W64A NC312 NC308 NC314 NC330 NC322 CH9 H49 NC306 NC372 A619 SD44 WF9 NC268 B46 B10 NC310 T8 Pa880 A239 Pa762 OH43 Ky226 VA26 C49A A188 C49 Oh43E Va102 Va14 Va35 Va59 A654 W153R Oh40B Va17 A659 CI-7 Va22 R177 H95 W182B W22 Va99 H99 PA91 CI90C M14 33-16 Va85 CH701-30 VaW6 NC33 L317 NC232 4226 MoG R109B B115 CI66 K55 I137TN CI44 CI31A NC230 81-1 M162W CI64 MEF K64 IL677A E2558W Ia5125 N6 SWEET CML52 T234 L578 SC357 IL14H IA2132 P39 CML14 CML69 IL101 CML38 B52 CML103 Tzi11 CML287 CML108 NC366 EP1 F2 SC213R F7 CML9 GT112 CO255 CML61 CML254 CML5 NC238 CML264 CML314 T232 GA209 CML258 Q6199 CI28A Mp339 CML10 CML341 B2 CML11 CML45 CML261 CML331 CML332 MS1334 U267Y Sg1533 SG18 Mo24W HP301 IDS28 F2834T D940Y M37W CML277 IDS69 IDS91 SA24 CML322 CML321 CML238 CML247 TROPICAL- SUBTROPICAL Ki2021 Ki14 Ki11 A6 F44 F6 4722 CML157Q Ki44 POPCORN I-29 Ki43 Oh603 CML328 Ki21 Ki2007 CML228 NC300 NC340 NC356 A272 CML92 Tx303 CML323 Ki3 NC302 NC338 NC358 CML77 CML218 NC320 NC332 NC334 NC318 SC55 A441-5 TZI18 NC354 CML154Q TZI10 NC370 CML220 NC264 Tzi9 Mo18W Ab28A NC350 TX601 CML333 CML158Q CML349 NC304 CML91 MIXED CML311 TZI8 Based on 89 SSR loci 0.1 CML281 NC296A NC346 parvi-03 NC336 NC296 NC352 NC298 NC348 ssp. parviglumis Flint-Garcia et al. (2005) Plant J. 44: 1054 parvi-30 parvi-49 parvi-14 parvi-36
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Jonathan K. Pritchard, Matthew Stephens and Peter Donnelly
Jonathan K. Pritchard, Matthew Stephens and Peter Donnelly. Inference of Population Structure Using Multilocus Genotype Data. Genetics, 2000. Population structure
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Population structure of maize
Taxa Q1 Q2 Q3 33-16 0.014 0.972 38-11 0.003 0.993 0.004 4226 0.071 0.917 0.012 4722 0.035 0.854 0.111 A188 0.013 0.982 0.005 B73 0.999 0.001 1.10E-16 B73HTRHM B75 0.005 0.993 0.002 WD 0.014 0.97 0.016 WF9 0.005 0.994 0.001 YU796NS 0.189 0.785 0.026
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Information extraction
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Principal components
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Principal Component Analysis (PCA)
X-Y: Correlated PCs Uncorrelated Var(PC1)>Var(PC2) Y PC2 PC1 X
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Eigen value and eigen vector
AV=λV Covariance matrix (symmetric ) eigen vector eigen value)
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Eigen value and eigen vector
data n individual by p features Covariance or correlation p by p X→A → λ V Y=XV Principal Component
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PCA in R pca=prcomp(X[,1:10]) str(pca) List of 5
$ sdev : num [1:10] $ rotation: num [1:10, 1:10] ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:10] "PZB " "PZA " "PZA " "PZA " ... .. ..$ : chr [1:10] "PC1" "PC2" "PC3" "PC4" ... $ center : Named num [1:10] ..- attr(*, "names")= chr [1:10] "PZB " "PZA " "PZA " "PZA " ... $ scale : logi FALSE $ x : num [1:281, 1:10] .. ..$ : NULL - attr(*, "class")= chr "prcomp"
![PCA in R pca=prcomp(X[,1:10]) str(pca) List of 5](http://slideplayer.com./slide/13532550/82/images/17/PCA+in+R+pca%3Dprcomp%28X%5B%2C1%3A10%5D%29+str%28pca%29+List+of+5.jpg "$ sdev : num [1:10] $ rotation: num [1:10, 1:10] attr(*, dimnames )=List of $ : chr [1:10] PZB PZA PZA PZA $ : chr [1:10] PC1 PC2 PC3 PC4 ... $ center : Named num [1:10] attr(*, names )= chr [1:10] PZB PZA PZA PZA $ scale : logi FALSE. $ x : num [1:281, 1:10] $ : NULL. - attr(*, class )= chr prcomp")
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PCA in R PCA=prcomp(X) str(PCA) List of 5
$ sdev : num [1:281] $ rotation: num [1:3093, 1:281] ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:3093] "PZB " "PZA " "PZA " "PZA " ... .. ..$ : chr [1:281] "PC1" "PC2" "PC3" "PC4" ... $ center : Named num [1:3093] ..- attr(*, "names")= chr [1:3093] "PZB " "PZA " "PZA " "PZA " ... $ scale : logi FALSE $ x : num [1:281, 1:281] .. ..$ : NULL - attr(*, "class")= chr "prcomp"
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Extraction Eigen value: $sdev squaed Eigen vector: $rotation
Principal component: $x PCA$x[1:10,1:5]
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Contribution pcavar=PCA$sdev^2 proportion=pcavar/sum(pcavar)
par(mfrow=c(1,3),mar = c(3,4,1,1)) barplot(PCA$sdev[1:10]) barplot(pcavar[1:10]) plot(proportion[1:10],type="b")
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plot(PCA$x[,1],PCA$x[,2],col="red")
Visualization plot(PCA$x[,1],PCA$x[,2],col="red")
![plot(PCA$x[,1],PCA$x[,2],col= red )](http://slideplayer.com./slide/13532550/82/images/21/plot%28PCA%24x%5B%2C1%5D%2CPCA%24x%5B%2C2%5D%2Ccol%3D+red+%29.jpg "Visualization. plot(PCA$x[,1],PCA$x[,2],col= red )")
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Association with phenotypes
PC1 r=0.2 par(mfrow=c(2,1),mar = c(3,4,1,1)) plot(mySim$y,PCA$x[,1]) cor(mySim$y,PCA$x[,1]) plot(mySim$y,PCA$x[,2]) cor(mySim$y,PCA$x[,2]) r=-0.32 PC2
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With QTNs Without QTNs Association r=-0.16 r=-0.21 PC1 PC2 r=0.31
pca1to5=prcomp(X[,index1to5]) pca6to10=prcomp(X[,!index1to5]) par(mfrow=c(2,2), mar = c(3,4,1,1)) plot(mySim$y, pca1to5$x[,1]) cor(mySim$y, pca1to5$x[,1]) plot(mySim$y, pca6to10$x[,1]) cor(mySim$y, pca6to10$x[,1]) plot(mySim$y, pca1to5$x[,2]) cor(mySim$y, pca1to5$x[,2]) plot(mySim$y, pca6to10$x[,2]) cor(mySim$y, pca6to10$x[,2]) PC2 r=0.31 r=-0.33
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This partially explains the inflation
plot(-log10(p.uni[order.uni]),-log10(p.obs[order.obs])) abline(a = 0, b = 1, col = "red")
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Highlight Inflation of P value population subdivision
Population structure Principal component
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