Last updated: 2020-01-27

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library(tidyverse)
Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
── Attaching packages ─────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1     ✔ purrr   0.3.2
✔ tibble  2.1.2     ✔ dplyr   0.8.1
✔ tidyr   0.8.3     ✔ 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)

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

What if we calculate with single population?

pop = "CHB"
pop = "CEU"
pop = "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"))

## 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")

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

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

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).

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)

qqunif(igrowth.assoc$P)

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" )

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)

qqunif(simpheno.assoc$P)

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

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)

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

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 --maf 0.05 --out {work.dir}output/igrowth-adjPC"))
igrowth.assoc = read.table(glue::glue("{work.dir}output/igrowth-adjPC.assoc.linear"),header=T,as.is=T)
indadd = igrowth.assoc$TEST=="ADD"
titulo = "igrowh association adjusted for PCs"
hist(igrowth.assoc$P[indadd],main=titulo)

qqunif(igrowth.assoc$P[indadd],main=titulo)


sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

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_0.8.1    
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.2   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 xfun_0.7         haven_2.1.0      lattice_0.20-38 
 [5] colorspace_1.4-1 generics_0.0.2   vctrs_0.1.0      htmltools_0.4.0 
 [9] yaml_2.2.0       utf8_1.1.4       rlang_0.4.1      pillar_1.4.1    
[13] glue_1.3.1       withr_2.1.2      calibrate_1.7.5  modelr_0.1.4    
[17] readxl_1.3.1     plyr_1.8.4       munsell_0.5.0    gtable_0.3.0    
[21] workflowr_1.3.0  cellranger_1.1.0 rvest_0.3.4      evaluate_0.14   
[25] labeling_0.3     knitr_1.23       curl_3.3         fansi_0.4.0     
[29] broom_0.5.2      Rcpp_1.0.2       scales_1.0.0     backports_1.1.4 
[33] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.19   
[37] stringi_1.4.3    grid_3.6.0       rprojroot_1.3-2  cli_1.1.0       
[41] tools_3.6.0      magrittr_1.5     lazyeval_0.2.2   crayon_1.3.4    
[45] pkgconfig_2.0.2  zeallot_0.1.0    MASS_7.3-51.4    xml2_1.2.0      
[49] lubridate_1.7.4  assertthat_0.2.1 rmarkdown_1.13   httr_1.4.0      
[53] rstudioapi_0.10  R6_2.4.0         nlme_3.1-139     git2r_0.25.2    
[57] compiler_3.6.0