In kevinlkx/mapgen: Multi-function software that performs enrichment, functionally-informed genetic fine-mapping and gene mapping

knitr::opts_chunk$set(echo = TRUE, eval = TRUE)

Input data

• Fine-mapping summary statistics.
• Gene annotations.
• Functional annotation data, e.g.: chromatin loops, open chromatin regions, histone modification data, etc.

Please have the following Bioconductor packages installed: GenomicFeatures, rtracklayer, Gviz, GenomicInteractions, AnnotationDbi, org.Hs.eg.db.

Load packages

library(GenomicFeatures) #  Making and manipulating annotations
library(rtracklayer) # Import annotation data
library(Gviz) # R package used to visualize track plots
library(GenomicInteractions) # visualize HiC loops
library(AnnotationDbi) # match gene ID to gene symbol
library(org.Hs.eg.db) # match gene ID to gene symbol
library(mapgen)

If you are in Xin He lab at U Chicago, you can access the example data on RCC:

trackdata.dir <- "/project2/xinhe/shared_data/mapgen/example_data/trackplot"

Fine-mapping summary statistics

Here we use the fine-mapping summary statistics from the AFib study.

finemapstats <- readRDS(system.file("extdata", "AF.finemapping.sumstats.rds", package = "mapgen"))
finemapstats <- process_finemapping_sumstats(finemapstats, 
                                             snp = 'snp', 
                                             chr = 'chr', 
                                             pos = 'pos', 
                                             pip = 'susie_pip', 
                                             pval = 'pval', 
                                             zscore = 'zscore', 
                                             cs = 'cs', 
                                             locus = 'locus',  
                                             pip.thresh = 0)

Gene annotations

You can download gene annotations (GTF file) from GENCODE, and make gene annotations using the make_genomic_annots() function with the GTF file.

gtf_file <- '/project2/xinhe/shared_data/gencode/gencode.v19.annotation.gtf.gz'
genomic.annots <- make_genomic_annots(gtf_file)

We included gene annotations (hg19) in the package, downloaded from GENCODE release 19.

genomic.annots <- readRDS(system.file("extdata", "genomic.annots.hg19.rds", package = "mapgen"))
gene.annots <- genomic.annots$genes

TxDb database

We can build a TxDb database (".sqlite") using the GTF file, and use \code{loadDb()} to load the TxDb database.

gtf_file <- '/project2/xinhe/shared_data/gencode/gencode.v19.annotation.gtf.gz'
txdb <- makeTxDbFromGFF(gtf_file, format = "gtf")
saveDb(txdb, "gencode.v19.annotation.gtf.sqlite")

If you are in Xin He lab at UChicago, you can access the gene annotations and TxDb database from RCC.

gtf_file <- '/project2/xinhe/shared_data/gencode/gencode.v19.annotation.gtf.gz'
txdb <- loadDb("/project2/xinhe/shared_data/gencode/gencode.v19.annotation.gtf.sqlite")

Genomic annotations

Load promoter-capture HiC (PC-HiC) data from cardiomyocytes (CMs).

pcHiC <- readRDS(system.file("extdata", "pcHiC.CM.gr.rds", package = "mapgen"))
pcHiC <- pcHiC[pcHiC$gene_name %in% gene.annots$gene_name, ] # restrict to protein coding genes

Load ABC scores from heart ventricle (from Nasser et al. Nature 2021).

ABC <- data.table::fread(system.file("extdata", "heart_ventricle-ENCODE_ABC.tsv.gz", package = "mapgen"))
ABC <- process_ABC(ABC, full.element = TRUE)
ABC <- ABC[ABC$gene_name %in% gene.annots$gene_name, ] # restrict to protein coding genes
ABC$score <- ABC$score * 100 # scale to visualize the ABC scores
head(ABC, 3)

Load H3K27ac and DHS .bed files.

H3K27ac_peaks <- rtracklayer::import(file.path(trackdata.dir, "H3K27ac.heart.concat.hg19.bed.gz"))
DHS_peaks <- rtracklayer::import(file.path(trackdata.dir, "FetalHeart_E083.DNase.hg19.narrowPeak.bed.gz"))

Load ATAC-seq counts data (the data should be in wig, bigWig/bw, bedGraph, or bam format)

CM_counts <- rtracklayer::import(file.path(trackdata.dir, "Cardiomyocyte.atac.hg19.bedGraph.gz"))
Endo_counts <- rtracklayer::import(file.path(trackdata.dir, "Endothelial.atac.hg19.bedGraph.gz")) 
Fibro_counts <- rtracklayer::import(file.path(trackdata.dir, "Fibroblast.atac.hg19.bedGraph.gz"))

LD reference panel

If you want to visualize r^2 between SNPs, we need a reference panel in a bigSNP object. (we will implement an option that takes LD matrix as input)

If you don't provide the bigSNP object, the SNPs in the GWAS track will be plotted in the same color.

library(bigsnpr) # loading reference genotype for LD calculation

If you are in the He lab at UChicago, you can load the bigSNP object from the 1000 Genomes (1KG) European population.

bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')

Make track plots

Plot HCN4 locus in the genomic region "chr15:73610000-73700000"

Highlight SNP "rs7172038"

counts <- list("CM" = CM_counts, "Endo" = Endo_counts, "Fibro" = Fibro_counts)
peaks <- list("H3K27ac" = H3K27ac_peaks, "DHS" = DHS_peaks)
loops <- list("ABC" = ABC)

track_plot(finemapstats,
           region = "chr15:73610000-73700000",
           gene.annots,
           bigSNP = bigSNP,
           txdb = txdb,
           counts = counts,
           peaks = peaks,
           loops = loops,
           genome = "hg19",
           filter_loop_genes = "HCN4",
           highlight_snps = "topSNP", 
           counts.color = c("red", "green", "purple"),
           peaks.color = c("navy", "blue"),
           loops.color = "gray", 
           genelabel.side = "above",
           verbose = TRUE)

kevinlkx/mapgen documentation built on March 31, 2024, 11:03 p.m.