Examine methylation and H3K27ac fine-mapping results from the ROSMAP data

Last updated: 2025-03-20

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Knit directory: fsusie-experiments/analysis/

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File	Version	Author	Date	Message
Rmd	1ad355a	Peter Carbonetto	2025-03-20	wflow_publish("rosmap_overview.Rmd", view = FALSE, verbose = TRUE)
html	c84e5c0	Peter Carbonetto	2025-03-20	Rebuilt the rosmap_overview analysis with the new results.
Rmd	1c8eeb7	Peter Carbonetto	2025-03-20	wflow_publish("rosmap_overview.Rmd", view = FALSE)
Rmd	acfadd1	Peter Carbonetto	2025-03-20	Made a few improvements to the code and text of the rosmap_analysis.
Rmd	c102af9	Peter Carbonetto	2025-03-20	Added a scatterplot comparing number of CSs per TAD (susie vs. fsusie).
Rmd	12f2fd3	Peter Carbonetto	2025-03-20	Added some histograms on TAD CS sizes.
Rmd	c69e187	Peter Carbonetto	2025-03-20	Created plot showing TAD sizes from the methylation fine-mapping results.
Rmd	2a5c706	Peter Carbonetto	2025-03-20	Added code to the rosmap_overview analysis to load the methylation SNP results.
Rmd	c3a01c7	Peter Carbonetto	2025-03-20	Added link for downloading data to rosmap_overview analysis.
html	5c446c0	Peter Carbonetto	2025-03-20	First build of the rosmap_overview analysis.
Rmd	7532908	Peter Carbonetto	2025-03-20	workflowr::wflow_publish("rosmap_overview.Rmd", verbose = TRUE)
Rmd	bc6d0a1	Peter Carbonetto	2025-03-20	Started working on rosmap_overview analysis.

ADD SOME TEXT HERE GIVING AN OVERVIEW OF THIS ANALYSIS.

Note: If you would like to run this analysis on your computer, you will first need to download the fine-mapping outputs. They can be downloaded from here. Once you have downloaded the files, copy them to the “outputs” subdirectory.

Load some packges used in the code below:

library(data.table)
library(ggplot2)
library(cowplot)

Methylation fine-mapping

First I define a helper function for loading the enrichment results:

# The "n" argument specifies the number of "meta data" columns.
# Columns after that are treated as the enrichment results. These
# columns contain only binary data (0 or 1) indicating whether or not
# the genomic feature (genetic variant or molecular trait location)
# is assigned that specific annotation.
read_enrichment_results <- function (filename, n) {
  out <- fread(filename,sep = "\t",stringsAsFactors = FALSE,header = TRUE)
  class(out) <- "data.frame"
  out <- transform(out,chr = factor(chr))
  cols <- seq(n + 1,ncol(out))
  for (i in cols)
    out[[i]] <- factor(out[[i]])
  return(out)
}

Next I load methylation SNP results generated by SuSiE-topPC and fSuSiE:

methyl_snps_susie_file <-
  "../outputs/ROSMAP_mQTL_cs_snp_toppc1_annotation.tsv.gz"
methyl_snps_fsusie_file <- "../outputs/ROSMAP_mQTL_cs_snp_annotation.tsv.gz"
methyl_snps_susie  <- read_enrichment_results(methyl_snps_susie_file,n = 6)
methyl_snps_fsusie <- read_enrichment_results(methyl_snps_fsusie_file,n = 7)
methyl_snps_susie$region <-
  sapply(strsplit(methyl_snps_susie$cs,":",fixed = TRUE),"[[",2)
methyl_snps_susie  <- transform(methyl_snps_susie,
                                region = factor(region),
                                cs     = factor(cs),
                                pc     = factor(pc))
methyl_snps_fsusie <- transform(methyl_snps_fsusie,
                                cs     = factor(cs),
                                region = factor(region),
                                study  = factor(study))

This is the number of fine-mapping regions (TADs) that contained at least one CS in each of the analyses:

nlevels(methyl_snps_susie$region)
nlevels(methyl_snps_fsusie$region)
# [1] 1236
# [1] 1327

This is a function we will use below to get the sizes of the TADs (in Mb):

get_tad_sizes <- function (tads) {
  tads <- strsplit(tads,"_",fixed = TRUE)
  pos0 <- as.numeric(sapply(tads,"[[",2))
  pos1 <- as.numeric(sapply(tads,"[[",3))
  return((pos1 - pos0)/1e6)
}

This plot summarizes the sizes of the TADs that were analyzed by SuSiE-topPC and fSuSiE:

plot_tad_sizes <- function (tads) {
  tad_size <- get_tad_sizes(tads)
  pdat <- data.frame(tad_size = tad_size)
  return(ggplot(pdat,aes(x = tad_size)) +
         geom_histogram(color = "white",fill = "darkblue",bins = 48) +
         labs(x = "size (Mb)",y = "number of TADs") +
         theme_cowplot(font_size = 10))
}
tads <- levels(methyl_snps_fsusie$region)
plot_tad_sizes(tads) +
  scale_x_continuous(limits = c(2,9),breaks = 1:10) +
  scale_y_continuous(breaks = seq(0,100,10))

Version	Author	Date
c84e5c0	Peter Carbonetto	2025-03-20

Some more useful statistics on the TAD sizes:

tad_size <- get_tad_sizes(tads)
range(tad_size)
mean(tad_size)
median(tad_size)
sum(tad_size > 9)
# [1]  2.320952 34.727189
# [1] 4.54465
# [1] 4.154266
# [1] 19

These histograms summarize the number of CSs per TAD:

get_cs_vs_tad_size <- function (dat) {
  tads <- levels(dat$region)
  out <- data.frame(tad      = tads,
                    tad_size = get_tad_sizes(tads),
                    num_cs   = tapply(dat$cs,dat$region,
                                      function (x) length(unique(x))))
  rownames(out) <- NULL
  return(out)
}
pdat1 <- get_cs_vs_tad_size(methyl_snps_susie)
pdat2 <- get_cs_vs_tad_size(methyl_snps_fsusie)
pdat1 <- transform(pdat1,num_cs = factor(num_cs,1:20))
pdat2 <- transform(pdat2,num_cs = factor(num_cs,1:20))
p1 <- ggplot(pdat1,aes(x = num_cs)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue") +
  scale_x_discrete(drop = FALSE) +
  labs(x = "number of CSs",y = "number of TADs",title = "SuSiE-topPC") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
p2 <- ggplot(pdat2,aes(x = num_cs)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue") +
  scale_x_discrete(drop = FALSE) +
  labs(x = "number of CSs",y = "number of TADs",title = "fSuSiE") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
plot_grid(p1,p2,nrow = 1,ncol = 2)

Version	Author	Date
c84e5c0	Peter Carbonetto	2025-03-20

Compare discovery of causal SNPs (number of CSs) in the SuSiE-topPC and fSuSiE analyses:

dat1 <- get_cs_vs_tad_size(methyl_snps_susie)
dat2 <- get_cs_vs_tad_size(methyl_snps_fsusie)
dat1 <- dat1[c(1,3)]
dat2 <- dat2[c(1,3)]
names(dat1) <- c("tad","num_cs_susie")
names(dat2) <- c("tad","num_cs_fsusie")
dat <- merge(dat1,dat2,all = TRUE)
rows <- which(is.na(dat$num_cs_susie))
dat[rows,"num_cs_susie"] <- 0
pdat <- melt(with(dat,table(num_cs_susie,num_cs_fsusie)))
rows <- which(pdat$value == 0)
pdat[rows,"value"] <- NA
ggplot(pdat,aes(x = num_cs_susie,y = num_cs_fsusie,size = value)) +
  geom_point(color = "white",fill = "darkblue",shape = 21) +
  geom_abline(intercept = 0,slope = 1,color = "black",linetype = "dotted") +
  scale_x_continuous(breaks = seq(0,20,2),limits = c(0,20)) +
  scale_y_continuous(breaks = seq(0,20,2),limits = c(0,20)) +
  scale_size(breaks = c(1,10,100)) +
  labs(x = "SuSiE-topPC",y = "fSuSiE",size = "number of TADs") +
  theme_cowplot(font_size = 10)

Version	Author	Date
c84e5c0	Peter Carbonetto	2025-03-20

Compare the sizes of the CSs in the SuSiE-topPC and fSuSiE analyses:

bins <- c(0,1,2,5,10,20,Inf)
cs_size_susie  <- as.numeric(table(methyl_snps_susie$cs))
cs_size_fsusie <- as.numeric(table(methyl_snps_fsusie$cs))
cs_size_susie  <- cut(cs_size_susie,bins)
cs_size_fsusie <- cut(cs_size_fsusie,bins)
levels(cs_size_susie) <- bins[-1]
levels(cs_size_fsusie) <- bins[-1]
p1 <- ggplot(data.frame(cs_size = cs_size_susie),aes(x = cs_size)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue",
                 width = 0.65) +
  labs(x = "CS size",y = "number of CSs",title = "SuSiE-topPC") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
p2 <- ggplot(data.frame(cs_size = cs_size_fsusie),aes(x = cs_size)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue",
                 width = 0.65) +
  labs(x = "CS size",y = "number of CSs",title = "fSuSiE") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
plot_grid(p1,p2,nrow = 1,ncol = 2)

Version	Author	Date
c84e5c0	Peter Carbonetto	2025-03-20

H3K27ac fine-mapping

Load the H3K27ac SNP results generated by SuSiE-topPC and fSuSiE:

ha_snps_susie_file <- "../outputs/ROSMAP_haQTL_cs_snp_toppc1_annotation.tsv.gz"
ha_snps_fsusie_file <- "../outputs/ROSMAP_haQTL_cs_snp_annotation.tsv.gz"
ha_snps_susie  <- read_enrichment_results(ha_snps_susie_file,n = 6)
ha_snps_fsusie <- read_enrichment_results(ha_snps_fsusie_file,n = 7)
ha_snps_susie$region <-
  sapply(strsplit(ha_snps_susie$cs,":",fixed = TRUE),"[[",2)
ha_snps_susie  <- transform(ha_snps_susie,
                            region = factor(region),
                            cs     = factor(cs),
                            pc     = factor(pc))
ha_snps_fsusie <- transform(ha_snps_fsusie,
                            cs     = factor(cs),
                            region = factor(region),
                            study  = factor(study))

There is no need to look at the TAD sizes because the same TADs were analyzed for both molecular traits, methylation and H3K27ac.