DOI: 10.5281/zenodo.8250770
title: isoMIGA: Expression and Splicing QTL Summary Statistics in Human Microglia

description: https://github.com/RajLabMSSM/isoMiGA Full associations and top association summary statistics for QTLs mapped in a multi-ethnic meta-analysis of human microglia, as part of the isoform-centric Microglia Genomic Atlas (isoMiGA) project. Sample size = 555 samples from 391 unique donors 6 cohorts (Raj MFG, Raj STG, Raj, SVZ, Raj, THA, Roussos, Gaffney) meta-analysed using the linear mixed model random-effects meta-analysis software mmQTL (PMID: 35058635). Each file has the following naming convention: {REFERENCE}_{PHENOTYPE}_{ASSOC}.tsv.gz <strong>References</strong> The following two transcript references were used for mapping phenotypes: 1. GENCODE - GENCODE v38 comprehensive transcripts (https://www.gencodegenes.org/human/release_38.html) 2. Union - a union of all GENCODE v38 transcripts with an additional 35,879 novel transcripts identified from long-read RNA-seq in 30 human microglia samples. GTF to be uploaded in Zenodo. <strong>Phenotypes</strong> The following phenotypes were tested for genetic association: expression: total gene expression of each gene following voom normalization of read counts. transcript: transcript usage - each transcript expression normalized by TPM divided by the total TPM for that gene leafcutter: junction usage using the Leafcutter framework to count and cluster intron-splicing junction reads. Each phenotype is the relative usage of the intron against the total count of the introns within that cluster. SUPPA_A3: alternate 3' splice site usage, as identified by SUPPA2 from transcript TPMs SUPPA_A5: alternate 5' splice site usage SUPPA_AF: alternate first exon usage SUPPA_AL: alternate last exon usage SUPPA_RI: intron retention usage SUPPA_SE: exon skipping usage All phenotype matrices were scaled and centred and then quantile normalized. <strong>Associations</strong> Top associations (top_assoc.tsv.gz) list the SNP-feature pair with the lowest adjusted P-value (qval) for that feature. Full associations (full_assoc.tsv.gz) list all tested SNP-feature pairs. <strong>Data dictionary</strong> The columns of the two association files only differ by the presence of the qval column in the top associations. feature: the phenotype being tested variant_id: the genetic variant being tested chr: chromosome pos: position (hg38) ref: reference allele alt: alternate allele Allele: the effect allele that the beta is relative to beta_tissue_0: Gaffney cohort beta sd_tissue_0: Gaffney cohort standard error z_tissue_0: Gafney cohort Z-score beta_tissue_1: Roussos cohort beta sd_tissue_1: Roussos cohort standard error z_tissue_1: Roussos cohort Z-core beta_tissue_2: Raj MFG cohort beta sd_tissue_2: Raj MFG cohort standard error z_tissue_2: Raj MFG cohort Z-score beta_tissue_3: Raj STG cohort beta sd_tissue_3: Raj STG cohort standard error z_tissue_3: Raj STG cohort Z-score beta_tissue_4: Raj SVZ cohort beta sd_tissue_4: Raj SVZ cohort standard error z_tissue_4: Raj SVZ cohort Z-score beta_tissue_5: Raj THA cohort beta sd_tissue_5: Raj THA cohort standard error z_tissue_5: Raj THA cohort Z-score fixed_beta: Fixed effect meta-analysis estimate of the beta fixed_sd: Fixed effect meta-analysis standard error of the beta fixed_z: Fixed effect meta-analysis Z-score Random_Z: Random effect meta-analysis Z-score Fixed_P: Fixed effect meta-analysis P-value Random_P: Random effect meta-analysis P-value Fixed_bonf: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni) Random_bonf: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni) Fixed_FDR: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) Random_FDR: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) qval: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) and for the number of features tested in the dataset (Storey's q value) <br>