Last updated: 2020-07-19
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Gandal et al analyzed autism spectrum disorder, schizophrenia, and bipolar disorder across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes to produce a quantitative, genome-wide resource. They performed TWAS based on 2,188 postmortem frontal and temporal cerebral cortex samples from 1,695 adults. RNA-sequencing reads were aligned to the GRCh37.p13 (hg19) reference genome. We generated a model using elastic-net weights released by Gandal et al. More info on the study: https://science.sciencemag.org/content/362/6420/eaat8127. The TWAS is available at http://resource.psychencode.org
Define variables in the terminal:
export PRE=/Users/sabrinami/Github/psychencode
export CODE=$PRE/code
export DATA=$PRE/data
export OUTPUT=$PRE/output
export MODEL=$PRE/modelsNow in R:
PRE="/Users/sabrinami/Github/psychencode"
DATA=glue::glue("{PRE}/data")
OUTPUT=glue::glue("{PRE}/output")
MODEL=glue::glue("{PRE}/models")TWAS data can be downloaded at http://resource.psychencode.org Or download from terminal by running
cd $DATA
wget "http://resource.psychencode.org/Datasets/Derived/PEC_TWAS_weights.tar.gz"And unzip
tar -xvf PEC_TWAS_weights.tar.gzWhen you open the PEC_TWAS_weights directory, there will be ~15,000 binary files. Each file contain information for a single gene. When loaded, an .RDat file contains snps (snp info), wgt.matrix (weights), and cv.performance (cross validation) tables. In the snps table, the first column is chromosome, the fourth is position, the fifth is effect allele, and the sixth is reference allele. In the wgt.matrix table, the rownames are the snp ids, and the columns are the weights derived from each method for each snp. In R, set the directory to PEC_TWAS_weights, then load the file:
setwd("/Users/sabrinami/Github/psychencode/data/PEC_TWAS_weights")
load("ENSG00000000457.wgt.RDat")Now, the snps, wgt.matrix, and cv.performance are defined as variables.
Run in R:
suppressPackageStartupMessages(library(RSQLite))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(data.table))make_df will load a file and store its data as a dataframe. This is only for a single gene, so later will be repeated for all genes. The input is the name of the .RDat file, and it returns returns dataframe with gene, position, chromosome, ref allele, eff allele, and non-zero enet weights.
make_df <- function(file) {
load(file)
weights <- data.frame(wgt.matrix)
snps <- data.frame(snps)
rownames(weights) <- c()
weights$gene <- substr(file, 1, nchar(file) - 9)
weights$chromosome <- snps$V1
weights$position <- snps$V4
weights$ref_allele <- snps$V5
weights$eff_allele <- snps$V6
weights %>% filter(enet != 0) %>% select(gene, chromosome, position, ref_allele, eff_allele, enet)
}First, combine .RDat file names in a vector
files <- list.files(pattern = "\\.RDat")The goal is to write tab delimited file with gene, chr, pos, ref, eff, and enet data for all genes in directory. Convert the first file in the vector to dataframe, then write it to a text file. And repeat for the remaining files, then append to the same text file.
pre_weights = glue::glue("{OUTPUT}/pre_weights.txt")
write.table(make_df(files[1]), pre_weights, sep = "\t", quote = FALSE, row.names = FALSE)
for(i in 2:length(files)) {
write.table(make_df(files[i]), pre_weights, append = TRUE, sep = "\t", quote = FALSE, col.names = FALSE, row.names = FALSE)
}Following Yanyu’s recommendation, rsids were added to pre_weights.txt using her python script and a hg19 lookup table. Her script is here: https://github.com/liangyy/misc-tools/tree/master/annotate_snp_by_position The lookup table, dbSNP150_list.txt, contains chromosome, position, ref, alt, rsid, and dbSNPBuildID. So the rsid for each snp is generated by matching the chromosome and position from psychencode models to lookup table. The output, weights_out.txt, will have gene, chr, pos, ref, eff, and rsid as new_id. In the terminal, run:
python3 $CODE/annotate_snp_by_position.py \
--input $OUTPUT/pre_weights.txt --chr_col 2 --pos_col 3 \
--lookup_table $DATA/dbSNP150_list.txt.gz --lookup_chr_col 1 --lookup_start_col 2 --lookup_end_col 2 --lookup_newid_col 5 --if_input_has_header 1 \
--out_txtgz $OUTPUT/weights_out.txt.gzRead weights_out.txt in R, and define varID from chromosome, position, reference and effect alleles. (The RNA-seq was in hg19, so varID is defined in b37)
weights <- fread("weights_out.txt.gz")
weights$varID <- paste(paste("chr", weights$chromosome, sep = ""), weights$position, weights$ref_allele, weights$eff_allele, "b37", sep = "_")
weights <- weights %>% select(gene, new_id, varID, ref_allele, eff_allele, enet) %>% rename(weight = enet, rsid = new_id)Generate number of snps for each gene from the weights table. For now, include blank columns to match PrediXcan format (gene, genename, n.snps.in.model, pred.perf.R2, pred.perf.pval, pred.perf.qval)
extra <- weights %>% group_by(gene) %>% summarise(n.snps.in.model = n())
extra$genename <- NA
extra$pred.perf.R2 <- NA
extra$pred.perf.pval <- NA
extra$pred.perf.qval <- NA
extra <- extra[c(1, 3, 2, 4, 5, 6)]Create database connection, and write the weights and extra tables to database.
model_db = glue::glue("{MODEL}/psychencode_model/psychencode.db")
conn <- dbConnect(RSQLite::SQLite(), model_db)
dbWriteTable(conn, "weights", weights)
dbWriteTable(conn, "extra", extra)To double check, confirm there is a weights and extra table, and show their contents.
dbListTables(conn)
dbGetQuery(conn, 'SELECT * FROM weights') %>% head
dbGetQuery(conn, 'SELECT * FROM extra') %>% headLastly, disconnect from database connection
dbDisconnect(conn)
sessionInfo()R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.6.2 Rcpp_1.0.3 rprojroot_1.3-2 digest_0.6.23
[5] later_1.0.0 R6_2.4.1 backports_1.1.5 git2r_0.27.1
[9] magrittr_1.5 evaluate_0.14 highr_0.8 stringi_1.4.5
[13] rlang_0.4.2 fs_1.3.1 promises_1.1.0 whisker_0.4
[17] rmarkdown_2.1 tools_3.6.2 stringr_1.4.0 glue_1.3.1
[21] httpuv_1.5.3.1 xfun_0.12 yaml_2.2.0 compiler_3.6.2
[25] htmltools_0.4.0 knitr_1.27