Summary statistics for expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders

This dataset contains summary statistics for eQTL (Expression Quantitative Trait Loci) analyses for 120 human fetal brains from the second trimester of gestation (12 to 19<br>post-conception weeks). Expression matrices, covariates, and summary statistics are provided for all tested eQTL and for top eQTL for all genes.<br>The data are contained within a single <b>.zip</b> archive file. Individual data files are of openly accessible <b>.txt</b> text file format containing p- or q- values by SNP, and <b>.bed </b>Browser Extensible Data format files, containing annotation track data such as chromosomal coordinates. Data files of multiple GB in size are stored in individual <b>.gz</b> gzip compressed files.<br>The related study investigates genetic influences on gene expression in the human fetal brain and their relationship with a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. This dataset represents the first eQTL dataset derived exclusively from the human fetal brain, and is based on initial deep RNA sequencing and genotyping.<br>The detailed breakdown of the files in this dataset is provided below and in <b>README.md.</b><br>Gene Level Analyses: <b>- expression_gene.bed.gz </b> · normalised, variance-stabilising transformed count data (29,875 genes) · columns: chr, gene_start, gene_end, gene_id, samples... - <b>all_eqtls_gene.txt.gz</b>· nominal p-values for all SNPs within 1 MB of each gene· columns: gene_id, variant_id, tss_distance, ma_samples, ma_count, maf, pval_nominal, slope, slope_se - <b>top_eqtls_gene.txt.gz</b>· q-values for most significant eQTL for each gene (includes nominal p-value thresholds that can be used to filter significant SNPs)· columns: chr, snp_start, snp_end, gene_id, num_var, beta_shape1, beta_shape2, true_df, pval_true_df, variant_id, tss_distance, minor_allele_samples, minor_allele_count, maf, ref_factor, pval_nominal, slope, slope_se, pval_perm, pval_beta, qval, pval_nominal_threshold <br>Transcript Level Analyses: - <b>expression_transcript.bed.gz </b>· normalised, variance-stabilising transformed count data (144,448 transcripts)· columns: chr, transcript_start, transcript_end, transcript_id, samples... - <b>all_eqtls_transcript.txt.gz</b>· nominal p-values for all SNPs within 1 MB of each transcript· columns: transcript_id, variant_id, tss_distance, ma_samples, ma_count, maf, pval_nominal, slope, slope_se - <b>top_eqtls_transcript.txt.gz</b>· q-values for most significant eQTL for each transcript (includes nominal p-value thresholds that can be used to filter significant SNPs)· columns: columns: chr, snp_start, snp_end, transcript_id, num_var, beta_shape1, beta_shape2, true_df, pval_true_df, variant_id, tss_distance, minor_allele_samples, minor_allele_count, maf, ref_factor, pval_nominal, slope, slope_se, pval_perm, pval_beta, qval, pval_nominal_threshold<br>Covariates (Used For Both Gene Level and Transcript-Level Analyses) - <b>covariates.txt</b>· columns: Sample, Sex, PCW, RIN, ReadLength, PC1, PC2, PC3, PEER1, PEER2, PEER3, PEER4, PEER5, PEER6, PEER7, PEER8, PEER9, PEER10<br>