Last updated: 2019-11-17

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Knit directory: finemap-uk-biobank/

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Load packages

library(dplyr)
library(ggplot2)
library(susieR)
library(data.table)
library(Matrix)
library(gridExtra)
library(readr)
library(cowplot)
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
}

We extract the 16 GWAS regions chosen from GWAS for height. We select 20 top hits and exclude 4 hits on chr6. The region is +- 500kb around the top hit. We exclude SNPs with MAF < 0.01, or inputation score less than 0.9.

We choose this region based on result from http://big.stats.ox.ac.uk/pheno/biobank_50.

Region CHR FROM (Mb) TO (Mb)
GDF5 20 33.525756 34.525756
ZBTB38 3 140.621814 141.621814
HHIP 4 145.066477 146.066477
LCORL 4 17.497066 18.497066
EFEMP1 2 55.606928 56.606928
KDM2A 11 66.524534 67.524534
HMGA2 12 65.871880 66.871880
CABLES1 18 20.224810 21.224810
ADAMTS10 19 8.170147 9.170147
GNA12 7 2.302522 3.302522
ADAMTS17 15 100.192953 101.192953
CDK6 7 91.750140 92.750140
MTMR11 1 149.406413 150.406413
ADAMTSL3 15 84.080582 85.080582
DLEU1 13 50.606788 51.606788
ACAN 15 88.897827 89.897827

The code to get phenotype height is get_height. The code to compute summary statistics is in prepare.

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")

GDF5

Get summary statistics:

region.name = 'GDF5'
file = dir("data/", pattern=paste0("height.",region.name))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2331 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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)

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

ZBTB38

Get summary statistics:

region.name = 'ZBTB38'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2701 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(5,10,11,16),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

HHIP

Get summary statistics:

region.name = 'HHIP'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "1743 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(2, 4),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

LCORL

Get summary statistics:

region.name = 'LCORL'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2846 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(3, 10),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

EFEMP1

Get summary statistics:

region.name = 'EFEMP1'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "3710 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(10),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

KDM2A

Get summary statistics:

region.name = 'KDM2A'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "1758 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(2, 16, 44, 45),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

HMGA2

Get summary statistics:

region.name = 'HMGA2'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2109 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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, 5, 11),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

CABLES1

Get summary statistics:

region.name = 'CABLES1'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2166 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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 result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

ADAMTS10

Get summary statistics:

region.name = 'ADAMTS10'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "3161 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(2,8,10,24),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

GNA12

Get summary statistics:

region.name = 'GNA12'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "4206 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(3,4,6,15),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

ADAMTS17

Get summary statistics:

region.name = 'ADAMTS17'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "4859 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(4,5,12),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

CDK6

Get summary statistics:

region.name = 'CDK6'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "1816 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(2,7,13),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

MTMR11

Get summary statistics:

region.name = 'MTMR11'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "1080 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(1,3,4,8,17,30),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

ADAMTSL3

Get summary statistics:

region.name = 'ADAMTSL3'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2259 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(3,4,6:12,14,16),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

DLEU1

Get summary statistics:

region.name = 'DLEU1'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2500 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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)

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)

ACAN

Get summary statistics:

region.name = 'ACAN'
file = dir("data/", pattern=paste0("height.",region.name,'.XtX'))
ss.dat = readRDS(paste0('data/', file))
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)
print(paste0(length(z), ' SNPs included'))
[1] "2846 SNPs included"

Comparing with Neale and GeneATLAS results:

map = fread(paste0('data/region-variants-', region.name, '.csv.gz'))
z.table = data.frame('id' = gsub('_.*$', '', names(z)), 'z' = z)
map = merge(map, z.table, by='id', all = TRUE)
p1 <- ggplot(map,aes(x = NBETA/NSE,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "GeneATLAS",y = "Our result",title = "t-statistics")
p2 <- ggplot(map,aes(x = neale_tstat,y = z)) +
  geom_point(shape = 20,size = 2,na.rm = TRUE) +
  geom_abline(intercept = 0,slope = 1,color = "dodgerblue",
              linetype = "dotted") +
  xlim(c(-50,50)) +
  ylim(c(-50,50)) +
  theme_cowplot(font_size = 12) +
  labs(x = "Neale lab",y = "Our result",title = "t-statistics")
gridExtra::grid.arrange(p1, p2, ncol=2)

start.pos <- min(ss.dat$pos$POS)
stop.pos  <- max(ss.dat$pos$POS)
plot.genes <- subset(genes,
                     chromosome == unique(ss.dat$pos$`#CHROM`) &
                     ((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(13,20),]

SuSiE result:

mod_rss = susie_rss(z = z, R = R, lambda=1e-04)
Warning in susie_rss(z = z, R = R, lambda = 1e-04): This method is under
active development, so it should not be considered stable.
Warning in susie_rss(z = z, R = R, lambda = 1e-04): The maximum number of
non-zero effects is greater than 1, this feature is experimental.
z.max = which.max(abs(z))
pip_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'))
p_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'p')
z_rss = susie_plot_locuszoom(z, mod_rss, pos = ss.dat$pos$POS/1e6, chr = unique(ss.dat$pos$`#CHROM`), gene.pos.map = gene.pos.map, ld = R[z.max,], z.ref.name = names(z.max), title = paste0(region.name,' SuSiE RSS lambda=1e-4'), y.susie = 'z')
grid.arrange(pip_rss, p_rss, z_rss, ncol=3)


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] cowplot_0.9.4     readr_1.3.1       gridExtra_2.3     Matrix_1.2-15    
[5] data.table_1.12.0 susieR_0.8.1.0545 ggplot2_3.1.1     dplyr_0.8.0.1    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1        pillar_1.3.1      compiler_3.5.1   
 [4] later_0.7.5       git2r_0.26.1      plyr_1.8.4       
 [7] workflowr_1.5.0   R.methodsS3_1.7.1 R.utils_2.7.0    
[10] tools_3.5.1       digest_0.6.18     evaluate_0.12    
[13] tibble_2.0.1      gtable_0.2.0      lattice_0.20-38  
[16] egg_0.4.5         pkgconfig_2.0.2   rlang_0.3.1      
[19] yaml_2.2.0        withr_2.1.2       stringr_1.3.1    
[22] knitr_1.20        hms_0.4.2         fs_1.3.1         
[25] rprojroot_1.3-2   grid_3.5.1        tidyselect_0.2.5 
[28] glue_1.3.0        R6_2.3.0          rmarkdown_1.10   
[31] purrr_0.3.2       magrittr_1.5      whisker_0.3-2    
[34] backports_1.1.2   scales_1.0.0      promises_1.0.1   
[37] htmltools_0.3.6   assertthat_0.2.1  colorspace_1.4-0 
[40] httpuv_1.4.5      labeling_0.3      stringi_1.2.4    
[43] lazyeval_0.2.1    munsell_0.5.0     crayon_1.3.4     
[46] R.oo_1.22.0