Stratification in polygenic scores (UK-like structure)
Last updated: 2020-12-22
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Introduction
Here, I’ll be plotting the spatial distribution of polygenic scores based on effect sizes estimated from a GWAS in a population with a deme arrangement that looks like a map of Britain.
I did the analyses with two different sampling distributions:
- Where individuals are sampled uniformly across all demes.
- Where individuals are sampled according to the observed sampling distribution in the UK Biobank.
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(data.table)
Attaching package: 'data.table'The following objects are masked from 'package:dplyr':
between, first, lastlibrary(rprojroot)
library(patchwork)
library(sf)Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1library(sp)
#specify root of the directory
F = is_rstudio_project$make_fix_file()
options(dplyr.summarise.inform=FALSE) PRS when individuals sampled uniformly across demes.
Load ‘pop’ file, which contains deme identity, longitude, and latitude information for each individual.
#load some shared reference files
#load file containing population information for each individual and their longitude/latitude info
pop.test=fread(F("data/gwas/ukb/popfiles/ukb_ss500_d35.uniform.pop"))
#pop.test$FID=pop.test$IID=paste("tsk_",seq(1,17999,2),sep="")
#load the genetic value
gvalue_df = fread(F("data/gwas/ukb/test/genos_ukb_l1e7_ss500_m0.08_uniform_chr1_20.rmdup.train.all.gvalue.sscore.gz"))
colnames(gvalue_df) = c("rep","IID","dosage","gvalue")
gvalue_df = gvalue_df[,c('rep','IID','gvalue')]
gvalue_df2 = merge(gvalue_df,pop.test,by="IID")
gvalue_df.mean = gvalue_df2%>%
group_by(deme,longitude,latitude)%>%
summarise(gvalue=mean(gvalue))Load the true genetic values for each individual.
#load the genetic value
gvalue_df = fread(F("data/gwas/ukb/test/genos_ukb_l1e7_ss500_m0.08_uniform_chr1_20.rmdup.train.all.gvalue.sscore.gz"))
colnames(gvalue_df) = c("rep","IID","dosage","gvalue")
gvalue_df = gvalue_df[,c('rep','IID','gvalue')]
gvalue_df2 = merge(gvalue_df,pop.test,by="IID")
gvalue_df.mean = gvalue_df2%>%
group_by(deme,longitude,latitude)%>%
summarise(gvalue=mean(gvalue)/sd(gvalue))Read a map of Britain and plot the true mean genetic values for each deme.
#read uk map
nc<-st_read(F("data/ukmap/NUTS_Level_2_January_2015_Full_Clipped_Boundaries_in_England_and_Wales//NUTS_Level_2_January_2015_Full_Clipped_Boundaries_in_England_and_Wales.shp"))Reading layer `NUTS_Level_2_January_2015_Full_Clipped_Boundaries_in_England_and_Wales' from data source `/Users/Azaidi/Documents/gwas_bias2/popstruct_scripts/data/ukmap/NUTS_Level_2_January_2015_Full_Clipped_Boundaries_in_England_and_Wales/NUTS_Level_2_January_2015_Full_Clipped_Boundaries_in_England_and_Wales.shp' using driver `ESRI Shapefile'
Simple feature collection with 35 features and 5 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 82672 ymin: 5337.901 xmax: 655604.7 ymax: 657534.1
projected CRS: OSGB 1936 / British National Grid#transform to same coordinate system as UKB data (OSGB1936)
nc2<-st_transform(nc,27700)
nc2.simple = st_simplify(nc2,preserveTopology = TRUE,dTolerance = 1000)
nc2.simple = merge(nc2.simple,gvalue_df.mean,by.x="nuts215cd",by.y="deme")
nc3.simple = st_union(nc2.simple)
ggplot()+
geom_sf(data=nc3.simple,
color="black",size=2)+
geom_sf(data=nc2.simple,
aes(fill=gvalue),
color="transparent")+
scale_fill_viridis_c()+
theme_void()+
theme(plot.margin = margin(0, 0, 0, 0, "cm"))
| Version | Author | Date |
|---|---|---|
| 5bce003 | Arslan-Zaidi | 2020-12-22 |
Write function that will do the following:
1: First,load the predicted polygenic scores based on effect sizes that are: (i) causal and have a pvalue of 5e-04 or smaller (named ‘causal’) (ii) the topmost significant SNP (‘lead SNP’) within each 100Kb window around the causal variant with the condition that the lead SNP has a pvalue of 5e-04 or smaller.
2: Load each individual’s true genetic value and subtract it out of the polygenic score. This is important because there might be some structure in the genetic value itself because of the simulated demography and we are only interested in any residual structure due to residual stratification.
For visualization, plot the mean polygenic score per deme. The mean averages out stochastic noise in the polygenic score, making systematic patterns more apparent.
Plot.
fmake_plots= function(pheno,sampling = "uniform",plot_title){
#lead the polygenic scores
#prs1=fread(here(paste("gwas/complex_dem/test/prs/complexdem_prs_",pheno,".all.c.sscore.gz",sep="")))
prs2=fread(F(paste("data/gwas/ukb/test/prs/ukbdem_prs_",pheno,".all.",sampling,".c.p.sscore.gz",sep="")))
prs3=fread(F(paste("data/gwas/ukb/test/prs/ukbdem_prs_",pheno,".all.",sampling,".nc.sscore.gz",sep="")))
colnames(prs2)=colnames(prs3)=c("rep","IID","dosage_sum","pcs0","cm","re")
#prs1$ascertainment = "all_causal"
prs2$ascertainment = "causal_p"
prs3$ascertainment = "lead_snp"
#rbind polygenic scores and add spatial information
prs_df=rbind(prs2,prs3)
prs_df=merge(prs_df,pop.test,by="IID")
#add genetic value to prs dataframe
prs_df = merge(prs_df, gvalue_df, by=c("rep","IID"))
#melt to long format
mprs_df=melt(prs_df%>%
select(-c(dosage_sum)),
id.vars=c("rep","IID","gvalue","ascertainment","deme","longitude","latitude"),
variable.name="correction",
value.name="prs")
#remove cmre for now
mprs_df = mprs_df%>%
filter(correction!="cmre")
#remove variation due to simulated genetic value
#also calculate the correlation between prs and longitude/latitude
mprs.adj = mprs_df%>%
group_by(rep,correction,ascertainment)%>%
mutate(prs.adjusted = prs-gvalue,
prs.adjusted = (prs.adjusted - mean(prs.adjusted))/sd(prs.adjusted))%>%
ungroup()
#calculate mean prs adjusted for each deme
mprs.sum = mprs.adj%>%
group_by(correction,ascertainment,deme,longitude,latitude)%>%
summarize(mean.prs = mean(prs.adjusted))%>%
ungroup()
#calculate mean of rlat and rlong across reps
# mprs.r = mprs.adj %>%
# group_by(correction,ascertainment)%>%
# summarize(rlat = mean(rlat),
# rlong = mean(rlong))%>%
# ungroup()
labels_prs=c(
causal_p="Causal",
lead_snp="Lead SNP",
pcs0="Uncorrected",
cm="Common\nPCA",
re="Rare\nPCA",
cmre="Common +\nrare"
)
prs_midpoint = mean(mprs.sum$mean.prs)
mprs.sum = merge(nc2.simple,mprs.sum,by.x="nuts215cd",by.y="deme")
if(pheno=="smooth"){
plt_prs_phe=ggplot() +
geom_sf(data=nc3.simple,
color="black",size=0.5)+
theme_bw()+
geom_sf(data = mprs.sum,
aes(fill = mean.prs),
color="transparent",
show.legend = T)+
facet_grid(correction ~ ascertainment,
labeller=as_labeller(labels_prs)) +
scale_fill_gradient2(high = "#d7191c",
mid = "#ffffbf",
low = "#2c7bb6",
midpoint = prs_midpoint)+
labs(title=plot_title, fill="Mean\nPRS")+
theme_void()+
theme(plot.title=element_text(size=11),
strip.text = element_text(size=9),
panel.grid = element_line(color="transparent"),
legend.text=element_text(size=9,angle=90,hjust=1),
legend.title = element_text(size=10),
legend.key.size = unit(5, "mm"),
legend.position="bottom",
legend.background = element_blank(),
plot.background = element_blank(),
strip.background = element_blank())
}
if(pheno == "sharp"){
plt_prs_phe = ggplot() +
geom_sf(data=nc3.simple,
color="black",size=0.5)+
theme_bw()+
geom_sf(data = mprs.sum,
aes(fill = mean.prs),
color="transparent",
show.legend = T)+
facet_grid(correction ~ ascertainment,
labeller=as_labeller(labels_prs)) +
scale_fill_gradient2(high = "#fdae61",
mid = "#ffffbf",
low = "#2c7bb6",
midpoint = prs_midpoint)+
labs(title=plot_title, fill="Mean\nPRS")+
theme_void()+
theme(plot.title=element_text(size=11),
strip.text = element_text(size=9),
panel.grid = element_line(color="transparent"),
legend.text=element_text(size=9,angle=90,hjust=1),
legend.title = element_text(size=10),
legend.key.size = unit(5, "mm"),
legend.position="bottom",
legend.background = element_blank(),
plot.background = element_blank(),
strip.background = element_blank()) +
annotate(geom="text",
x=462513, y=202620, label = "*", vjust = 0.7)
}
return(plt_prs_phe)
}Residual stratification
plts_smooth1 = fmake_plots("smooth","uniform","Smooth(N-S)")
plts_sharp1 = fmake_plots("sharp","uniform","Sharp")
plts_smooth1 + plts_sharp1
| Version | Author | Date |
|---|---|---|
| 5bce003 | Arslan-Zaidi | 2020-12-22 |
PRS when individuals are sampled non-uniformly across demes
Now repeat the analysis when individuals are sampled non-uniformly across space.
#load some shared reference files
#load file containing population information for each individual and their longitude/latitude info
pop.test=fread(F("data/gwas/ukb/popfiles/ukb_ss500_d35.weighted.pop"))
#pop.test$FID=pop.test$IID=paste("tsk_",seq(1,17999,2),sep="")Load the true genetic values for each individual.
#load the genetic value
gvalue_df = fread(F("data/gwas/ukb/test/genos_ukb_l1e7_ss500_m0.08_weighted_chr1_20.rmdup.train.all.gvalue.sscore.gz"))
colnames(gvalue_df) = c("rep","IID","dosage","gvalue")
gvalue_df = gvalue_df[,c('rep','IID','gvalue')]
gvalue_df2 = merge(gvalue_df,pop.test,by="IID")
gvalue_df.mean = gvalue_df2%>%
group_by(deme,longitude,latitude)%>%
summarise(gvalue=mean(gvalue)/sd(gvalue))Plot!
plts_smooth1 = fmake_plots("smooth","weighted","Smooth(N-S)")
plts_sharp1 = fmake_plots("sharp","weighted","Sharp")
plts_smooth1 + plts_sharp1
| Version | Author | Date |
|---|---|---|
| 5bce003 | Arslan-Zaidi | 2020-12-22 |
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] sp_1.4-4 sf_0.9-6 patchwork_1.0.1 rprojroot_1.3-2
[5] data.table_1.13.2 dplyr_1.0.2 ggplot2_3.3.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 purrr_0.3.4 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.3.4 generics_0.1.0 htmltools_0.5.0
[9] viridisLite_0.3.0 yaml_2.2.1 rlang_0.4.8 R.oo_1.24.0
[13] e1071_1.7-4 later_1.1.0.1 pillar_1.4.6 glue_1.4.2
[17] withr_2.3.0 DBI_1.1.0 R.utils_2.10.1 lifecycle_0.2.0
[21] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 R.methodsS3_1.8.1
[25] evaluate_0.14 labeling_0.4.2 knitr_1.30 httpuv_1.5.4
[29] class_7.3-17 Rcpp_1.0.5 KernSmooth_2.23-17 promises_1.1.1
[33] scales_1.1.1 backports_1.1.10 classInt_0.4-3 farver_2.0.3
[37] fs_1.5.0 digest_0.6.27 stringi_1.5.3 grid_4.0.3
[41] tools_4.0.3 magrittr_1.5 tibble_3.0.4 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 rmarkdown_2.5
[49] rstudioapi_0.11 R6_2.5.0 units_0.6-7 git2r_0.27.1
[53] compiler_4.0.3