L6-population-structure

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Last updated: 2020-01-30

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Knit directory: hgen471/

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File	Version	Author	Date	Message
Rmd	e73a329	Hae Kyung Im	2020-01-27	L6 examples
html	e73a329	Hae Kyung Im	2020-01-27	L6 examples

---

library(tidyverse)

── Attaching packages ─────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.2.1     ✔ purrr   0.3.3
✔ tibble  2.1.3     ✔ dplyr   0.8.3
✔ tidyr   1.0.0     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0

── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

work.dir ="~/Downloads/hapmap/"

## qqunif function
source("https://gist.githubusercontent.com/hakyim/38431b74c6c0bf90c12f/raw/21fbae9a48dc475f42fa60f0ef5509d071dea873/qqunif")

*What’s the population composition?**

popinfo = read_tsv(paste0(work.dir,"relationships_w_pops_051208.txt"))

Parsed with column specification:
cols(
  FID = col_character(),
  IID = col_character(),
  dad = col_character(),
  mom = col_character(),
  sex = col_double(),
  pheno = col_double(),
  population = col_character()
)

popinfo %>% count(population)

# A tibble: 11 x 2
   population     n
   <chr>      <int>
 1 ASW           90
 2 CEU          180
 3 CHB           90
 4 CHD          100
 5 GIH          100
 6 JPT           91
 7 LWK          100
 8 MEX           90
 9 MKK          180
10 TSI          100
11 YRI          180

samdata = read_tsv(paste0(work.dir,"phase3_corrected.psam"),guess_max = 2500) 

Parsed with column specification:
cols(
  `#IID` = col_character(),
  PAT = col_character(),
  MAT = col_character(),
  SEX = col_double(),
  SuperPop = col_character(),
  Population = col_character()
)

superpop = samdata %>% select(SuperPop,Population) %>% unique()

superpop = rbind(superpop, data.frame(SuperPop=c("EAS","HIS","AFR"),Population=c("CHD","MEX","MKK")))

Effect of population structure in Hardy Weinberg

## what happens if we calculate HWE with this mixed population?
system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --hardy --out {work.dir}output/allhwe"))
allhwe = read.table(glue::glue("{work.dir}output/allhwe.hwe"),header=TRUE,as.is=TRUE)
hist(allhwe$P)

Past versions of “hardy weinberg all populations-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqunif(allhwe$P,main='HWE HapMap3 All Pop')

Past versions of “hardy weinberg all populations-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

What if we calculate with single population?

pop = "CHB"
pop = "CEU"
pop = "YRI"

for(pop in c("CHB","CEU","YRI"))
{
  ## what if we calculate with single population?
  popinfo %>% filter(population==pop) %>%
    write_tsv(path=glue::glue("{work.dir}{pop}.fam") )
  system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --hardy --keep {work.dir}{pop}.fam --out {work.dir}output/hwe-{pop}"))
  pophwe = read.table(glue::glue("{work.dir}output/hwe-{pop}.hwe"),header=TRUE,as.is=TRUE)
  hist(pophwe$P,main=glue::glue("HWE {pop} and founders only"))
  qqunif(pophwe$P,main=glue::glue("HWE {pop} and founders only"))
}

Past versions of “hardy weinberg single population-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

Past versions of “hardy weinberg single population-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

## not much difference when --nonfounders option is added

What if we add non founders? Some of the samples in HapMap were recruited from families.

## what if we add nonfounders?
system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --hardy --keep {work.dir}CEU.fam --nonfounders --out {work.dir}output/CEUhwe_nf"))
CEUhwe_nf = read.table(glue::glue("{work.dir}output/CEUhwe_nf.hwe"),header=TRUE,as.is=TRUE)
hist(CEUhwe_nf$P,main="HWE CEU + non founders")

Past versions of “hardy weinberg including non founders-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqunif(CEUhwe_nf$P,main="HWE CEU + non founders")

Past versions of “hardy weinberg including non founders-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqplot(-log10(CEUhwe_nf$P),-log10(CEUhwe_nf$P),main="all vs founders only" );abline(0,1) 

Past versions of “hardy weinberg including non founders-3.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

GWAS on a growth phenotype in HapMap samples

## read igrowth
igrowth = read_tsv("https://raw.githubusercontent.com/hakyimlab/igrowth/master/rawgrowth.txt")

Parsed with column specification:
cols(
  IID = col_character(),
  sex = col_double(),
  pop = col_character(),
  experim = col_double(),
  meas.by = col_double(),
  serum = col_character(),
  growth = col_double()
)

## fix FID from igrowth file
igrowth = popinfo %>% select(-pheno) %>% inner_join(igrowth %>% select(IID,growth), by=c("IID"="IID"))
write_tsv(igrowth,path=glue::glue("{work.dir}igrowth.pheno"))
igrowth %>% ggplot(aes(population,growth)) + geom_violin(aes(fill=population)) + geom_boxplot(width=0.2,col='black',fill='gray',alpha=.8) + theme_bw(base_size = 15)

Warning: Removed 130 rows containing non-finite values (stat_ydensity).

Warning: Removed 130 rows containing non-finite values (stat_boxplot).

Past versions of “intrinsic growth phenotype GWAS-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

summary( lm(growth~population,data=igrowth) )

Call:
lm(formula = growth ~ population, data = igrowth)

Residuals:
   Min     1Q Median     3Q    Max 
-58821 -18093  -2242  15896  98760 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    73080.8      938.2  77.894  < 2e-16 ***
populationCEU  -2190.1     1175.4  -1.863   0.0625 .  
populationCHB   9053.1     2043.9   4.429 9.73e-06 ***
populationJPT   3476.8     2034.8   1.709   0.0876 .  
populationYRI  -7985.2     1137.2  -7.022 2.61e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 24160 on 3591 degrees of freedom
  (130 observations deleted due to missingness)
Multiple R-squared:  0.0345,    Adjusted R-squared:  0.03342 
F-statistic: 32.08 on 4 and 3591 DF,  p-value: < 2.2e-16

system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --linear --pheno {work.dir}igrowth.pheno --pheno-name growth --maf 0.05 --out {work.dir}output/igrowth"))
igrowth.assoc = read.table(glue::glue("{work.dir}output/igrowth.assoc.linear"),header=T,as.is=T)
hist(igrowth.assoc$P)

Past versions of “intrinsic growth phenotype GWAS-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqunif(igrowth.assoc$P)

Past versions of “intrinsic growth phenotype GWAS-3.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

## install.packages("qqman")
library(qqman)

For example usage please run: vignette('qqman')

Citation appreciated but not required:

Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).

manhattan(igrowth.assoc, chr="CHR", bp="BP", snp="SNP", p="P" )

Past versions of “intrinsic growth phenotype GWAS-4.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

Simulate phenotype

set.seed(10) ## to get the same simulated values each time
simpheno = popinfo %>% mutate(pheno=rnorm(nrow(popinfo)))
write_tsv(simpheno, path=glue::glue("{work.dir}sim.pheno"))
## run association with plink
system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --linear --pheno {work.dir}sim.pheno --pheno-name pheno --maf 0.05 --out {work.dir}output/simpheno") )
simpheno.assoc = read.table(glue::glue("{work.dir}output/simpheno.assoc.linear"),header=T,as.is=T)
hist(simpheno.assoc$P)

Past versions of “simulated phenotype-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqunif(simpheno.assoc$P)

Past versions of “simulated phenotype-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

manhattan(simpheno.assoc, chr="CHR", bp="BP", snp="SNP", p="P" )

Past versions of “simulated phenotype-3.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

PCA calculation using plink

## generate PCs using plink
system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --pca --out {work.dir}output/pca"))
## read plink calculated PCs
pcplink = read.table(glue::glue("{work.dir}output/pca.eigenvec"),header=F, as.is=T)
names(pcplink) = c("FID","IID",paste0("PC", c(1:(ncol(pcplink)-2))) )
pcplink = popinfo %>% left_join(superpop,by=c("population"="Population")) %>% inner_join(pcplink, by=c("FID"="FID", "IID"="IID"))

## plot PC1 vs PC2
pcplink %>% ggplot(aes(PC1,PC2,col=population,shape=SuperPop)) + geom_point(size=3,alpha=.7) + theme_bw(base_size = 15)

Past versions of “PCA calculation-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

manhattan(simpheno.assoc, chr="CHR", bp="BP", snp="SNP", p="P" ,main="Simulated Phenotype")

Past versions of “PCA calculation-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

runnig igrowth GWAS using PCs

system(glue::glue("~/bin/plink --bfile {work.dir}hapmapch22 --linear --pheno {work.dir}igrowth.pheno --pheno-name growth --covar {work.dir}output/pca.eigenvec --covar-number 1-4 --hide-covar --maf 0.05 --out {work.dir}output/igrowth-adjPC"))
igrowth.adjusted.assoc = read.table(glue::glue("{work.dir}output/igrowth-adjPC.assoc.linear"),header=T,as.is=T)
##indadd = igrowth.adjusted.assoc$TEST=="ADD"
titulo = "igrowh association adjusted for PCs"
hist(igrowth.adjusted.assoc$P,main=titulo)

Past versions of “igrowth GWAS with PC-1.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

qqunif(igrowth.adjusted.assoc$P,main=titulo)

Past versions of “igrowth GWAS with PC-2.png”

Version	Author	Date
e73a329	Hae Kyung Im	2020-01-27

calculate Zscores

genomic.control = function(pvec)
{
 z = qnorm(pvec / 2)
 lambda = round(median(z^2) / 0.4549, 3)
 lambda
}

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] qqman_0.1.4     forcats_0.4.0   stringr_1.4.0   dplyr_0.8.3    
 [5] purrr_0.3.3     readr_1.3.1     tidyr_1.0.0     tibble_2.1.3   
 [9] ggplot2_3.2.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 xfun_0.11        haven_2.2.0      lattice_0.20-38 
 [5] colorspace_1.4-1 vctrs_0.2.0      generics_0.0.2   htmltools_0.4.0 
 [9] yaml_2.2.0       utf8_1.1.4       rlang_0.4.1      later_1.0.0     
[13] pillar_1.4.2     withr_2.1.2      glue_1.3.1       calibrate_1.7.5 
[17] modelr_0.1.5     readxl_1.3.1     lifecycle_0.1.0  munsell_0.5.0   
[21] gtable_0.3.0     workflowr_1.5.0  cellranger_1.1.0 rvest_0.3.5     
[25] evaluate_0.14    labeling_0.3     knitr_1.26       httpuv_1.5.2    
[29] curl_4.2         fansi_0.4.0      broom_0.5.2      Rcpp_1.0.3      
[33] promises_1.1.0   backports_1.1.5  scales_1.1.0     jsonlite_1.6    
[37] farver_2.0.1     fs_1.3.1         hms_0.5.2        digest_0.6.22   
[41] stringi_1.4.3    grid_3.6.1       rprojroot_1.3-2  cli_1.1.0       
[45] tools_3.6.1      magrittr_1.5     lazyeval_0.2.2   crayon_1.3.4    
[49] whisker_0.4      pkgconfig_2.0.3  zeallot_0.1.0    MASS_7.3-51.4   
[53] xml2_1.2.2       lubridate_1.7.4  assertthat_0.2.1 rmarkdown_1.17  
[57] httr_1.4.1       rstudioapi_0.10  R6_2.4.1         nlme_3.1-142    
[61] git2r_0.26.1     compiler_3.6.1