Fine-mapping Height CABLES1 – SuSiE check
Yuxin Zou
11/18/2019
Last updated: 2019-11-18
Checks: 7 0
Knit directory: finemap-uk-biobank/
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Modified: analysis/_site.yml
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Modified: scripts/plots.R
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Modified: scripts/prepare.susieinput.R
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a056f42 | zouyuxin | 2019-11-18 | wflow_publish(“analysis/finemap_height_CABLES1_check.Rmd”) |
We perform some check for the SuSiE result on region around CABLES1
Load pacakges:
library(readr)
library(dplyr)
library(gridExtra)
library(susieR)Load plotting functions:
knitr::read_chunk("scripts/plots.R")library(ggplot2)
#' plot gene name annotations
#' @param dat a matrix of gene names with 'start' and 'end' base-pair position
#' @param xrange range of x axis, base-pair position
plot_geneName = function(dat, xrange, chr){
ngene = 2:nrow(dat)
line = 1
dat$lines = NA
dat$lines[1] = 1
gene.end = dat[1, 'end']
while(length(ngene) != 0){
id = which(dat[ngene, 'start'] > gene.end + 0.02)[1]
if(!is.na(id)){
dat$lines[ngene[id]] = line
gene.end = dat[ngene[id],'end']
ngene = ngene[-id]
}else{
line = line + 1
dat$lines[ngene[1]] = line
gene.end = dat[ngene[1],'end']
ngene = ngene[-1]
}
}
dat$start = pmax(dat$start, xrange[1])
dat$end = pmin(dat$end, xrange[2])
dat$mean = rowMeans(dat[,c('start', 'end')])
pl = ggplot(dat, aes(xmin = xrange[1], xmax = xrange[2])) + xlim(xrange[1], xrange[2]) + ylim(min(-dat$lines-0.6), -0.8) +
geom_rect(aes(xmin = start, xmax = end, ymin = -lines-0.05, ymax = -lines+0.05), fill='blue') +
geom_text(aes(x = mean, y=-lines-0.4, label=geneName), size=4) +
xlab(paste0('base-pair position (Mb) on chromosome ', chr)) + ylab('Gene') +
theme_bw() + theme(axis.text.x=element_blank(),
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.title = element_text(size=15),
plot.title=element_text(size=11),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
pl
}
discrete_gradient_pal <- function(colours, bins = 5) {
ramp <- scales::colour_ramp(colours)
function(x) {
if (length(x) == 0) return(character())
i <- floor(x * bins)
i <- ifelse(i > bins-1, bins-1, i)
ramp(i/(bins-1))
}
}
scale_colour_discrete_gradient <- function(..., colours, bins = 5, na.value = "grey50", guide = "colourbar", aesthetics = "colour", colors) {
colours <- if (missing(colours))
colors
else colours
continuous_scale(
aesthetics,
"discrete_gradient",
discrete_gradient_pal(colours, bins),
na.value = na.value,
guide = guide,
...
)
}
#' Locuszoom plot
#' @param z a vector of z scores with SNP names
#' @param pos base-pair positions
#' @param gene.pos.map a matrix of gene names with 'start' and 'end' base-pair position
#' @param z.ref.name the reference SNP
#' @param ld correlations between teh reference SNP and the rests
#' @param title title of the plot
#' @param title.size the size of the title
#' @param true the true value
#' @param y.height height of -log10(p) plot and height of the gene name annotation plot
#' @param y.lim range of y axis
locus.zoom = function(z, pos, chr, gene.pos.map=NULL, z.ref.name=NULL, ld=NULL,
title = NULL, title.size = 10, true = NULL,
y.height=c(5,1.5), y.lim=NULL, y.type='logp',xrange=NULL){
if(is.null(xrange)){
xrange = c(min(pos), max(pos))
}
tmp = data.frame(POS = pos, p = -(pnorm(-abs(z), log.p = T) + log(2))/log(10), z = z)
if(!is.null(ld) && !is.null(z.ref.name)){
tmp$ref = names(z) == z.ref.name
tmp$r2 = ld^2
if(y.type == 'logp'){
pl_zoom = ggplot(tmp, aes(x = POS, y = p, shape = ref, size=ref, color=r2)) + geom_point() +
ylab("-log10(p value)") + ggtitle(title) + xlim(xrange[1], xrange[2]) +
scale_color_gradientn(colors = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
values = seq(0,1,0.2), breaks=seq(0,1,0.2)) +
# scale_colour_discrete_gradient(
# colours = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
# limits = c(0, 1.01),
# breaks = c(0,0.2,0.4,0.6,0.8,1),
# guide = guide_colourbar(nbin = 100, raster = FALSE, frame.colour = "black", ticks.colour = NA)
# ) +
scale_shape_manual(values=c(20, 18), guide=FALSE) + scale_size_manual(values=c(2,5), guide=FALSE) +
theme_bw() + theme(axis.title.x=element_blank(),
axis.title.y = element_text(size=15),axis.text = element_text(size=15),
plot.title = element_text(size=title.size))
}else if(y.type == 'z'){
pl_zoom = ggplot(tmp, aes(x = POS, y = z, shape = ref, size=ref, color=r2)) + geom_point() +
ylab("z score") + ggtitle(title) + xlim(xrange[1], xrange[2]) +
scale_color_gradientn(colors = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
values = seq(0,1,0.2), breaks=seq(0,1,0.2)) +
# scale_colour_discrete_gradient(
# colours = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
# limits = c(0, 1.01),
# breaks = c(0,0.2,0.4,0.6,0.8,1),
# guide = guide_colourbar(nbin = 100, raster = FALSE, frame.colour = "black", ticks.colour = NA)
# ) +
scale_shape_manual(values=c(20, 18), guide=FALSE) + scale_size_manual(values=c(2,5), guide=FALSE) +
theme_bw() + theme(axis.title.x=element_blank(),
axis.title.y = element_text(size=15),axis.text = element_text(size=15),
plot.title = element_text(size=title.size))
}
}else{
if(y.type == 'logp'){
pl_zoom = ggplot(tmp, aes(x = POS, y = p)) + geom_point(color = 'darkblue') +
ylab("-log10(p value)") + ggtitle(title) + xlim(xrange[1], xrange[2]) +
theme_bw() + theme(axis.title.x=element_blank(),
plot.title = element_text(size=title.size))
}else if(y.type == 'z'){
pl_zoom = ggplot(tmp, aes(x = POS, y = z)) + geom_point(color = 'darkblue') +
ylab("z scores") + ggtitle(title) + xlim(xrange[1], xrange[2]) +
theme_bw() + theme(axis.title.x=element_blank(),
plot.title = element_text(size=title.size))
}
}
if(!is.null(y.lim)){
pl_zoom = pl_zoom + ylim(y.lim[1], y.lim[2])
}
# pl_zoom = pl_zoom + geom_hline(yintercept=-log10(5e-08), linetype='dashed', color = 'red')
if(!is.null(true)){
tmp.true = data.frame(POS = which(true!=0), p = tmp$p[which(true!=0)],
ref = (names(z) == z.ref.name)[which(true!=0)],
label = paste0('SNP',1:length(which(true!=0))))
pl_zoom = pl_zoom + geom_point(data=tmp.true, aes(x=POS, y=p),
color='red', show.legend = FALSE, shape=1, stroke = 1) +
geom_text(data=tmp.true, aes(x = POS-30, y=p+1, label=label), size=3, color='red')
}
if(!is.null(gene.pos.map)){
pl_gene = plot_geneName(gene.pos.map, xrange = xrange, chr=chr)
g = egg::ggarrange(pl_zoom, pl_gene, nrow=2, heights = y.height, draw=FALSE)
}else{
g = pl_zoom
}
g
}
#' SuSiE plot with Locuszoom plot
#' @param z a vector of z scores with SNP names
#' @param model the fitted SuSiE model
#' @param pos base-pair positions
#' @param gene.pos.map a matrix of gene names with 'start' and 'end' base-pair position
#' @param z.ref.name the reference SNP
#' @param ld correlations between teh reference SNP and the rests
#' @param title title of the plot
#' @param title.size the size of the title
#' @param true the true value
#' @param plot.locuszoom whether to plot locuszoom plot
#' @param y.lim range of y axis
#' @param y.susie the y axis of the SuSiE plot, 'PIP' or 'p' or 'z', 'p' refers to -log10(p)
susie_plot_locuszoom = function(z, model, pos, chr, gene.pos.map = NULL, z.ref.name, ld,
title = NULL, title.size = 10, true = NULL,
plot.locuszoom = TRUE, y.lim=NULL, y.susie='PIP', xrange=NULL){
if(is.null(xrange)){
xrange = c(min(pos), max(pos))
}
if(plot.locuszoom){
if(y.susie == 'z'){
y.type = 'z'
}else{
y.type = 'logp'
}
pl_zoom = locus.zoom(z, pos = pos, chr = chr, ld=ld, z.ref.name = z.ref.name, title = title, title.size = title.size, y.lim=y.lim, y.type=y.type, xrange=xrange)
}
pip = model$pip
tmp = data.frame(POS = pos, PIP = pip, p = -(pnorm(-abs(z), log.p = T) + log(2))/log(10), z = z)
if(y.susie == 'PIP'){
pl_susie = ggplot(tmp, aes(x = POS, y = PIP)) + geom_point(show.legend = FALSE, size=3) +
xlim(xrange[1], xrange[2]) +
theme_bw() + theme(axis.title.x=element_blank(), axis.text.x=element_blank(),axis.text = element_text(size=15),
axis.title.y = element_text(size=15))
if(!plot.locuszoom){
pl_susie = pl_susie + ggtitle(title) + theme(plot.title = element_text(size=title.size))
}
}else if(y.susie == 'p'){
pl_susie = ggplot(tmp, aes(x = POS, y = p)) + geom_point(show.legend = FALSE, size=3) +
ylab("-log10(p value)") + xlim(xrange[1], xrange[2]) +
theme_bw() + theme(axis.title.x=element_blank(), axis.text.x=element_blank(),axis.text = element_text(size=15),
axis.title.y = element_text(size=15))
if(!plot.locuszoom){
pl_susie = pl_susie + ggtitle(title) + theme(plot.title = element_text(size=title.size))
# pl_susie = pl_susie + geom_hline(yintercept=-log10(5e-08), linetype='dashed', color = 'red')
}
}else if(y.susie == 'z'){
pl_susie = ggplot(tmp, aes(x = POS, y = z)) + geom_point(show.legend = FALSE, size=3) +
ylab("z scores") + xlim(xrange[1], xrange[2]) +
theme_bw() + theme(axis.title.x=element_blank(), axis.text.x=element_blank(),axis.text = element_text(size=15),
axis.title.y = element_text(size=15))
if(!plot.locuszoom){
pl_susie = pl_susie + ggtitle(title) + theme(plot.title = element_text(size=title.size))
}
}
if(!is.null(true)){
tmp.true = data.frame(POS = pos[which(true!=0)], PIP = pip[which(true!=0)])
pl_susie = pl_susie + geom_point(data=tmp.true, aes(x=POS, y=PIP),
color='red', size=3, show.legend = FALSE)
}
model.cs = model$sets$cs
if(!is.null(model.cs)){
tmp$CS = numeric(length(z))
for(i in 1:length(model.cs)){
tmp$CS[model.cs[[i]]] = gsub('L', 'CS', names(model.cs)[i])
}
tmp.cs = tmp[unlist(model.cs),]
tmp.cs$CS = factor(tmp.cs$CS)
levels(tmp.cs$CS) = paste0('CS', 1:length(model.cs))
colors = c('red', 'cyan', 'green', 'orange', 'dodgerblue', 'violet', 'gold',
'#FF00FF', 'forestgreen', '#7A68A6')
if(y.susie == 'PIP'){
pl_susie = pl_susie + geom_point(data=tmp.cs, aes(x=POS, y=PIP, color=CS),
size=3, shape=1, stroke = 2) +
scale_color_manual(values=colors)
}else if(y.susie == 'p'){
pl_susie = pl_susie + geom_point(data=tmp.cs, aes(x=POS, y=p, color=CS),
shape=1, size=3, stroke=1.5) +
scale_color_manual(values=colors)
}else if(y.susie == 'z'){
pl_susie = pl_susie + geom_point(data=tmp.cs, aes(x=POS, y=z, color=CS),
shape=1, size=3, stroke=1.5) +
scale_color_manual(values=colors)
}
}
if(!is.null(gene.pos.map)){
pl_gene = plot_geneName(gene.pos.map, xrange = xrange, chr=chr)
if(plot.locuszoom){
g = egg::ggarrange(pl_zoom, pl_susie, pl_gene, nrow=3, heights = c(4,4,1.5), draw=FALSE)
}else{
g = egg::ggarrange(pl_susie, pl_gene, nrow=2, heights = c(5.5,1.5), draw=FALSE)
}
}else{
if(plot.locuszoom){
g = egg::ggarrange(pl_zoom, pl_susie, nrow=2, heights = c(4,4), draw=FALSE)
}else{
g = pl_susie
}
}
g
}locus.zoom.cs = function(z, cs, pos, chr, gene.pos.map=NULL, z.ref.name=NULL, ld=NULL, title = NULL, title.size = 10, xrange = NULL, y.lab='-log10(p value)', y.type = 'logp'){
tmp = data.frame(POS = pos, log10p = -(pnorm(-abs(z), log.p = T) + log(2))/log(10), z = z)
tmp$ref = names(z) == z.ref.name
tmp$r2 = ld^2
tmp$CS = rep(4, length(z))
tmp$CS[cs] = 16
tmp$CS = as.factor(tmp$CS)
if(y.type == 'logp'){
pl_zoom = ggplot(tmp, aes(x = POS, y = log10p, shape = CS, color = r2, size=CS)) +
geom_point() + ylab(y.lab)
}else if(y.type == 'z'){
pl_zoom = ggplot(tmp, aes(x = POS, y = z, shape = CS, color = r2, size=CS)) +
geom_point() + ylab(y.lab)
}
pl_zoom = pl_zoom + scale_color_gradientn(colors = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
values = seq(0,1,0.2), breaks=seq(0,1,0.2)) +
# scale_colour_discrete_gradient(
# colours = c("darkblue", "deepskyblue", "lightgreen", "orange", "red"),
# limits = c(0, 1.01),
# breaks = c(0,0.2,0.4,0.6,0.8,1),
# guide = guide_colourbar(nbin = 100, raster = FALSE, frame.colour = "black", ticks.colour = NA)) +
scale_shape_manual(values = c(4, 19), guide=FALSE) +
scale_size_manual(values=c(1.5,4), guide=FALSE) +
ggtitle(title) +
theme_bw() + theme(axis.title.x=element_blank(), axis.text=element_text(size=15),
axis.title.y=element_text(size=12),
plot.title = element_text(size=title.size))
tmp.sub = tmp[cs,]
if(y.type == 'log10p'){
pl_zoom = pl_zoom + geom_point(data = tmp.sub, aes(x=POS, y=log10p), shape=1, size=4, color='black', stroke=0.1)
}else if(y.type == 'z'){
pl_zoom = pl_zoom + geom_point(data = tmp.sub, aes(x=POS, y=z), shape=1, size=4, color='black', stroke=0.1)
}
if(!is.null(xrange)){
pl_zoom = pl_zoom + xlim(xrange[1], xrange[2])
}
pl_gene = plot_geneName(gene.pos.map, xrange = xrange, chr=chr)
g = egg::ggarrange(pl_zoom, pl_gene, nrow=2, heights = c(5.5,1.5), draw=FALSE)
g
}Get summary statistics:
ss.dat = readRDS('data/height.CABLES1.XtX.Xty.rds')
betas = as.vector(ss.dat$Xty/diag(ss.dat$XtX))
rss = c(ss.dat$yty) - betas * as.vector(ss.dat$Xty)
se = sqrt(rss/((ss.dat$n-1)*diag(ss.dat$XtX)))
z = betas/se
pval = 2*pnorm(-abs(z))
R = as.matrix(t(ss.dat$XtX * (1/sqrt(diag(ss.dat$XtX)))) * (1/ sqrt(diag(ss.dat$XtX))))
names(z) = rownames(R)Get gene data:
genes <- read_delim("data/seq_gene.md.gz",delim = "\t",quote = "")
class(genes) <- "data.frame"
genes <- subset(genes,
group_label == "GRCh37.p5-Primary Assembly" &
feature_type == "GENE")
start.pos <- min(ss.dat$pos$POS)
stop.pos <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
chromosome == 18 &
((chr_start > start.pos & chr_start < stop.pos) |
(chr_stop > start.pos & chr_start < stop.pos)) & feature_type == 'GENE')
gene.pos.map = plot.genes %>% select(feature_name, chr_start, chr_stop)
colnames(gene.pos.map) = c('geneName', 'start', 'end')
gene.pos.map = as.data.frame(gene.pos.map)
gene.pos.map = gene.pos.map %>% mutate(start = start/1e6, end = end/1e6)
gene.pos.map = gene.pos.map[-c(1),]SuSiE bhat result:
mod_bhat = susie_bhat(bhat = betas, shat = se, R = R, n = ss.dat$n, var_y = as.numeric(ss.dat$yty/(ss.dat$n-1)), track_fit=TRUE, standardize=FALSE)
z.max = which.max(abs(z))
p1 = susie_plot_locuszoom(z, mod_bhat, pos = ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max))
p2 = susie_plot_locuszoom(z, mod_bhat, pos = ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), y.susie ='p')
p3 = susie_plot_locuszoom(z, mod_bhat, pos = ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), y.susie ='z')
grid.arrange(p1, p2, p3, ncol=3)
For 1Mb region about CABLES1, SuSiE found 2 CSs. The SNP with the strongest marginal p value in CS1 is rs7235010 (p = 1.3394e-113). For CS2, the SNP with the strongest marginal p value is rs12327253 (p = 0.1015).
The correlation between rs2871960 and rs11919556 is 0.3082546. The average correlation between SNPs in CS1 and CS2 is
round(mean(abs(R[mod_bhat$sets$cs$L1, mod_bhat$sets$cs$L2])), 4)[1] 0.2764Zoom in plot:
p1 = susie_plot_locuszoom(z, mod_bhat, pos = ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z), ld = R[z.max,], title='CABLES1 Credible Sets', plot.locuszoom = FALSE, y.susie = 'p', xrange=c(20.5, 21), title.size = 20)Warning: Removed 1129 rows containing missing values (geom_point).Warning: Removed 4 rows containing missing values (geom_rect).Warning: Removed 4 rows containing missing values (geom_text).p1
# pdf('GitHub/finemap-uk-biobank/CABLES1_CSs.pdf', height = 5, width=8)
# p1
# dev.off()CS 1
- Removing effect of top SNP in CS 2
library(data.table)
library(readr)
library(Matrix)
geno.file = 'height.CABLES1.raw.gz'
cat("Reading genotype data.\n")
geno <- fread(geno.file,sep = "\t",header = TRUE,stringsAsFactors = FALSE)
class(geno) <- "data.frame"
# Extract the genotypes.
X <- as(as.matrix(geno[-(1:6)]),'dgCMatrix')
pheno.file <- "/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/height.csv.gz"
out.pheno.file <- "pheno.height.txt"
out.covar.file <- "covar.removeCS2.height.txt"
# LOAD PHENOTYPE and COVARIATES DATA
# -------------------
# Read the phenotype data from the CSV file.
cat("Reading phenotype data.\n")
pheno <- suppressMessages(read_csv(pheno.file))
class(pheno) <- "data.frame"
pheno$sex = factor(pheno$sex)
pheno$assessment_centre = factor(pheno$assessment_centre)
pheno$genotype_measurement_batch = factor(pheno$genotype_measurement_batch)
pheno$age2 = pheno$age^2
# match individual order with genotype file
ind = fread('height.CABLES1.psam')
match.idx = match(ind$IID, pheno$id)
pheno = pheno[match.idx,]
Z = model.matrix(~ sex + age + age2 + assessment_centre + genotype_measurement_batch +
pc_genetic1 + pc_genetic2 + pc_genetic3 + pc_genetic4 + pc_genetic5 +
pc_genetic6 + pc_genetic7 + pc_genetic8 + pc_genetic9 + pc_genetic10 +
pc_genetic11 + pc_genetic12 + pc_genetic13 + pc_genetic14 + pc_genetic15 +
pc_genetic16 + pc_genetic17 + pc_genetic18 + pc_genetic19 + pc_genetic20 + X[,1281], data = pheno)
# Remove intercept
Z = Z[,-1]
colnames(Z)[150] = 'X'
Z = scale(Z, center=T, scale=F)
# standardize quantitative columns
cols = which(colnames(Z) %in% c("age","pc_genetic1","pc_genetic2","pc_genetic3","pc_genetic4",
"pc_genetic5","pc_genetic6","pc_genetic7","pc_genetic8","pc_genetic9",
"pc_genetic10","pc_genetic11","pc_genetic12","pc_genetic13","pc_genetic14",
"pc_genetic15","pc_genetic16","pc_genetic17","pc_genetic18","pc_genetic19","pc_genetic20"))
Z[,cols] = scale(Z[,cols])
Z[,'age2'] = Z[,'age']^2
# Compute XtX and Xty
y = pheno$height
names(y) = pheno$id
# Center y
y = y - mean(y)
# Center X
X = scale(X, center=T, scale=FALSE)
xtxdiag = colSums(X^2)
A <- crossprod(Z) # Z'Z
# chol decomposition for (Z'Z)^(-1)
R = chol(solve(A)) # R'R = (Z'Z)^(-1)
W = R %*% crossprod(Z, X) # RZ'X
S = R %*% crossprod(Z, y) # RZ'y
# Load LD matrix from raw genotype
ld.matrix = as.matrix(fread(paste0('height.CABLES1.matrix')))
# X'X
XtX = sqrt(xtxdiag) * t(ld.matrix*sqrt(xtxdiag)) - crossprod(W) # W'W = X'ZR'RZ'X = X'Z(Z'Z)^{-1}Z'X
rownames(XtX) = colnames(XtX) = colnames(X)
# X'y
Xty = as.vector(y %*% X)
Xty = Xty - crossprod(W, S) # W'S = X'ZR'RZ'y = X'Z(Z'Z)^{-1}Z'y
## SNP info
maf <- read.delim('height.CABLES1.afreq')
pos <- fread('height.CABLES1.pvar')
pos$maf = pmin(maf$ALT_FREQS, 1-maf$ALT_FREQS)
saveRDS(list(XtX = XtX, Xty = Xty, yty = sum(y^2) - crossprod(S), n = length(y), pos=pos),
paste0('height.CABLES1.removeCS2.XtX.Xty.rds'))After removing the effect of rs12327253 (p = 0.1015),
ss.dat = readRDS('output/height.CABLES1.removeCS2.XtX.Xty.rds')
betas = as.vector(ss.dat$Xty/diag(ss.dat$XtX))
rss = c(ss.dat$yty) - betas * ss.dat$Xty
se = as.vector(sqrt(rss/((ss.dat$n-1)*diag(ss.dat$XtX))))
z = betas/se
pval = 2*pnorm(-abs(z))
R = as.matrix(t(ss.dat$XtX * (1/sqrt(diag(ss.dat$XtX)))) * (1/ sqrt(diag(ss.dat$XtX))))
names(z) = rownames(R)
z.max = which.max(abs(z))
p1 = locus.zoom.cs(z, cs = mod_bhat$sets$cs$L1, pos=ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z)[z.max], ld = R[z.max,], xrange=c(20.5,21), title='CABLES1 CS1', y.lab = '-log10(p value condition on CS2)')
p2 = locus.zoom.cs(z, cs = mod_bhat$sets$cs$L1, pos=ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z)[z.max], ld = R[z.max,], xrange=c(20.5,21), title='CABLES1 CS1', y.lab = 'z scores condition on CS2)', y.type = 'z')
grid.arrange(p1, p2, ncol=2)
- Remove effect of all SNPs based on weights in other CSs
ss.dat = readRDS('data/height.CABLES1.XtX.Xty.rds')
XtXr = mod_bhat$XtXr - ss.dat$XtX %*% (mod_bhat$alpha[1,] * mod_bhat$mu[1,])
Xtr = ss.dat$Xty - XtXr
betas = Xtr/diag(ss.dat$XtX)
b_2 = colSums(mod_bhat$alpha[-1,]*mod_bhat$mu[-1,])/mod_bhat$X_column_scale_factors
rss = c(ss.dat$yty - 2*sum(ss.dat$Xty * b_2) + sum(XtXr * b_2)) - betas * Xtr
se = as.vector(sqrt(rss/((ss.dat$n-1)*diag(ss.dat$XtX))))
z.CS1 = betas/se
names(z.CS1) = colnames(R)
z.cs1.max = which.max(abs(z.CS1))
p2 = locus.zoom.cs(z.CS1, mod_bhat$sets$cs$L1, pos=ss.dat$pos$POS/1e6, chr = 18, gene.pos.map = gene.pos.map, z.ref.name = names(z.cs1.max), ld = R[z.cs1.max,], xrange=c(20.5,21), title='CABLES1 Credible Set 1', y.lab = '-log10(p value condition on other CSs)', title.size = 20)Warning: Removed 1129 rows containing missing values (geom_point).Warning: Removed 4 rows containing missing values (geom_rect).Warning: Removed 4 rows containing missing values (geom_text).p2
# pdf('GitHub/finemap-uk-biobank/CABLES1_CS1.pdf', height=5, width=8)
# p2
# dev.off()tmp = data.frame(POS = ss.dat$pos$POS/1e6, logPO = log(mod_bhat$alpha[1,]/(1-mod_bhat$alpha[1,])))
tmp$CS = rep(4, length(z))
tmp$CS[mod_bhat$sets$cs$L1] = 16
tmp$CS = as.factor(tmp$CS)
pl_zoom = ggplot(tmp, aes(x = POS, y = logPO, shape = CS, size=CS)) +
geom_point() + ylab('log posterior odd') +
scale_shape_manual(values = c(4, 19), guide=FALSE) +
scale_size_manual(values=c(1.5,4), guide=FALSE) +
ggtitle('CABLES1 Credible Set 1') +
theme_bw() + theme(axis.title.x=element_blank(), plot.title = element_text(size=12))
tmp.sub = tmp[mod_bhat$sets$cs$L1,]
pl_zoom = pl_zoom + geom_point(data = tmp.sub, aes(x=POS, y=logPO), shape=1, size=4, color='black', stroke=0.1)
pl_zoom = pl_zoom + xlim(20.5, 21)
pl_gene = plot_geneName(gene.pos.map, xrange = c(20.5, 21), chr=3)
g = egg::ggarrange(pl_zoom, pl_gene, nrow=2, heights = c(5,1), draw=FALSE)Warning: Removed 1129 rows containing missing values (geom_point).Warning: Removed 4 rows containing missing values (geom_rect).Warning: Removed 4 rows containing missing values (geom_text).g
CS 2
- Removing effect of top SNP in CS 1
library(data.table)
library(readr)
library(Matrix)
geno.file = 'height.CABLES1.raw.gz'
cat("Reading genotype data.\n")
geno <- fread(geno.file,sep = "\t",header = TRUE,stringsAsFactors = FALSE)
class(geno) <- "data.frame"
# Extract the genotypes.
X <- as(as.matrix(geno[-(1:6)]),'dgCMatrix')
pheno.file <- "/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/height.csv.gz"
out.pheno.file <- "pheno.height.txt"
out.covar.file <- "covar.removeCS1.height.txt"
# LOAD PHENOTYPE and COVARIATES DATA
# -------------------
# Read the phenotype data from the CSV file.
cat("Reading phenotype data.\n")
pheno <- suppressMessages(read_csv(pheno.file))
class(pheno) <- "data.frame"
pheno$sex = factor(pheno$sex)
pheno$assessment_centre = factor(pheno$assessment_centre)
pheno$genotype_measurement_batch = factor(pheno$genotype_measurement_batch)
pheno$age2 = pheno$age^2
# match individual order with genotype file
ind = fread('height.CABLES1.psam')
match.idx = match(ind$IID, pheno$id)
pheno = pheno[match.idx,]
Z = model.matrix(~ sex + age + age2 + assessment_centre + genotype_measurement_batch +
pc_genetic1 + pc_genetic2 + pc_genetic3 + pc_genetic4 + pc_genetic5 +
pc_genetic6 + pc_genetic7 + pc_genetic8 + pc_genetic9 + pc_genetic10 +
pc_genetic11 + pc_genetic12 + pc_genetic13 + pc_genetic14 + pc_genetic15 +
pc_genetic16 + pc_genetic17 + pc_genetic18 + pc_genetic19 + pc_genetic20 + X[,1293], data = pheno)
# Remove intercept
Z = Z[,-1]
colnames(Z)[150] = 'X'
Z = scale(Z, center=T, scale = F)
# standardize quantitative columns
cols = which(colnames(Z) %in% c("age","pc_genetic1","pc_genetic2","pc_genetic3","pc_genetic4",
"pc_genetic5","pc_genetic6","pc_genetic7","pc_genetic8","pc_genetic9",
"pc_genetic10","pc_genetic11","pc_genetic12","pc_genetic13","pc_genetic14",
"pc_genetic15","pc_genetic16","pc_genetic17","pc_genetic18","pc_genetic19","pc_genetic20"))
Z[,cols] = scale(Z[,cols])
Z[,'age2'] = Z[,'age']^2
# Compute XtX and Xty
y = pheno$height
names(y) = pheno$id
# Center y
y = y - mean(y)
# Center X
X = scale(X, center=TRUE, scale = FALSE)
xtxdiag = colSums(X^2)
A <- crossprod(Z) # Z'Z
# chol decomposition for (Z'Z)^(-1)
R = chol(solve(A)) # R'R = (Z'Z)^(-1)
W = R %*% crossprod(Z, X) # RZ'X
S = R %*% crossprod(Z, y) # RZ'y
# Load LD matrix from raw genotype
ld.matrix = as.matrix(fread('height.CABLES1.matrix'))
# X'X
XtX = sqrt(xtxdiag) * t(ld.matrix*sqrt(xtxdiag)) - crossprod(W) # W'W = X'ZR'RZ'X = X'Z(Z'Z)^{-1}Z'X
rownames(XtX) = colnames(XtX) = colnames(X)
# X'y
Xty = as.vector(y %*% X)
Xty = Xty - crossprod(W, S) # W'S = X'ZR'RZ'y = X'Z(Z'Z)^{-1}Z'y
saveRDS(list(XtX = XtX, Xty = Xty, yty = sum(y^2) - crossprod(S), n = length(y), pos=pos),
paste0('height.CABLES1.removeCS1.XtX.Xty.rds'))After removing the effect of rs7235010 (p = 1.3394e-113), the conditional p value for rs57772091 becomes 1.9404e-08!
ss.dat = readRDS('output/height.CABLES1.removeCS1.XtX.Xty.rds')
betas = as.vector(ss.dat$Xty/diag(ss.dat$XtX))
rss = c(ss.dat$yty) - betas * ss.dat$Xty
se = as.vector(sqrt(rss/((ss.dat$n-1)*diag(ss.dat$XtX))))
z = betas/se
pval = 2*pnorm(-abs(z))
R = as.matrix(t(ss.dat$XtX * (1/sqrt(diag(ss.dat$XtX)))) * (1/ sqrt(diag(ss.dat$XtX))))
names(z) = rownames(R)
z.max = which.max(abs(z))
p1 = locus.zoom.cs(z, cs = mod_bhat$sets$cs$L2, pos=ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z)[z.max], ld = R[z.max,], xrange=c(20.5,21), title='CABLES1 CS2', y.lab = '-log10(p value condition on CS1)')
p2 = locus.zoom.cs(z, cs = mod_bhat$sets$cs$L2, pos=ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z)[z.max], ld = R[z.max,], xrange=c(20.5,21), title='CABLES1 CS2', y.lab = 'z scores condition on CS1)', y.type = 'z')
grid.arrange(p1, p2, ncol=2)
- Remove effect of all SNPs based on weights in other CSs
ss.dat = readRDS('data/height.CABLES1.XtX.Xty.rds')
XtXr = mod_bhat$XtXr - ss.dat$XtX %*% (mod_bhat$alpha[2,] * mod_bhat$mu[2,])
Xtr = ss.dat$Xty - XtXr
betas = Xtr/diag(ss.dat$XtX)
b_2 = colSums(mod_bhat$alpha[-2,]*mod_bhat$mu[-2,])/mod_bhat$X_column_scale_factors
rss = c(ss.dat$yty - 2*sum(ss.dat$Xty * b_2) + sum(XtXr * b_2)) - betas * Xtr
se = as.vector(sqrt(rss/((ss.dat$n-1)*diag(ss.dat$XtX))))
z.CS2 = betas/se
names(z.CS2) = colnames(R)
z.cs2.max = which.max(abs(z.CS2))
p3 = locus.zoom.cs(z.CS2, mod_bhat$sets$cs$L2, pos=ss.dat$pos$POS/1e6, chr=18, gene.pos.map = gene.pos.map, z.ref.name = names(z.cs2.max), ld = R[z.cs2.max,], xrange=c(20.5, 21), title='CABLES1 Credible Set 2', y.lab = '-log10(p value condition on other CSs)', title.size = 20)Warning: Removed 1129 rows containing missing values (geom_point).Warning: Removed 4 rows containing missing values (geom_rect).Warning: Removed 4 rows containing missing values (geom_text).p3
# pdf('GitHub/finemap-uk-biobank/CABLES1_CS2.pdf', height=5, width=8)
# p3
# dev.off()tmp = data.frame(POS = ss.dat$pos$POS/1e6, logPO = log(mod_bhat$alpha[2,]/(1-mod_bhat$alpha[2,])))
tmp$CS = rep(4, length(z))
tmp$CS[mod_bhat$sets$cs$L2] = 16
tmp$CS = as.factor(tmp$CS)
pl_zoom = ggplot(tmp, aes(x = POS, y = logPO, shape = CS, size=CS)) +
geom_point() + ylab('log posterior odd') +
scale_shape_manual(values = c(4, 19), guide=FALSE) +
scale_size_manual(values=c(1.5,4), guide=FALSE) +
ggtitle('CABLES1 Credible Set 2') +
theme_bw() + theme(axis.title.x=element_blank(), plot.title = element_text(size=12))
tmp.sub = tmp[mod_bhat$sets$cs$L2,]
pl_zoom = pl_zoom + geom_point(data = tmp.sub, aes(x=POS, y=logPO), shape=1, size=4, color='black', stroke=0.1)
pl_zoom = pl_zoom + xlim(20.5, 21)
pl_gene = plot_geneName(gene.pos.map, xrange = c(20.25, 21), chr=3)
g = egg::ggarrange(pl_zoom, pl_gene, nrow=2, heights = c(5,1), draw=FALSE)Warning: Removed 1129 rows containing missing values (geom_point).Warning: Removed 4 rows containing missing values (geom_rect).Warning: Removed 4 rows containing missing values (geom_text).g
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.1.1 susieR_0.8.1.0545 gridExtra_2.3 dplyr_0.8.0.1
[5] readr_1.3.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 plyr_1.8.4 pillar_1.3.1 compiler_3.5.1
[5] later_0.7.5 git2r_0.26.1 workflowr_1.5.0 tools_3.5.1
[9] digest_0.6.18 evaluate_0.12 tibble_2.0.1 gtable_0.2.0
[13] lattice_0.20-38 egg_0.4.5 pkgconfig_2.0.2 rlang_0.3.1
[17] Matrix_1.2-15 yaml_2.2.0 withr_2.1.2 stringr_1.3.1
[21] knitr_1.20 fs_1.3.1 hms_0.4.2 rprojroot_1.3-2
[25] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[29] rmarkdown_1.10 purrr_0.3.2 magrittr_1.5 whisker_0.3-2
[33] backports_1.1.2 scales_1.0.0 promises_1.0.1 htmltools_0.3.6
[37] assertthat_0.2.1 colorspace_1.4-0 httpuv_1.4.5 labeling_0.3
[41] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 crayon_1.3.4