Impact of cell-type and context-dependent regulatory variants on human immune traits

Abstract

The effects of trait-associated variants are often studied in a single relevant cell-type or context. However, for many complex traits, multiple cell-types are involved. This applies particularly to immune-related traits, for which many immune cell-types and contexts play a role. Here, we studied the impact of immune gene regulatory variants on complex traits to better understand genetic risk mediated through immune cell-types. We identified 26,271 expression quantitative trait loci (QTLs) and 23,121 splicing QTLs in 18 immune cell-types, and analyzed their overlap with trait-associated loci from 72 genome-wide association studies (GWAS). We showed that effects on RNA expression and splicing in immune cells colocalize with an average of 40.4% and 27.7% GWAS loci for immune-related and non-immune traits, respectively. Notably, we found that a large number of loci (mean: 14%) colocalize with splicing QTLs but not expression QTLs. The 60% GWAS loci without colocalization harbor genes that have lower expression levels, are less tolerant to loss-of-function mutations, and more enhancerrich than genes at colocalized loci. To further investigate the 60% GWAS loci not explained by our regulatory QTLs, we collected H3K27ac CUT&Tag data from rheumatoid arthritis (RA) and healthy controls. We found several unexplained GWAS hits lying within regions with higher H3K27ac activity in RA patients. We also observed that enrichment of RA GWAS heritability is greater in H3K27ac regions in immune cell-types from RA patients compared to healthy controls. Our study paves the way for future QTL studies to elucidate the mechanisms of as yet unexplained GWAS loci.

eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

“…Still, these genetic effects mediate only 11% of disease heritability (4), suggesting that many regulatory effects cannot be detected in bulk tissues at a steady-state (5). In contrast, profiling specialised disease-relevant cell types such as induced pluripotent stem cells (6), peripheral immune cells (7), microglia (8,9) or dopaminergic neurons (10) often identifies additional colocalisations that are missing in GTEx. While several databases have been developed to collect eQTL summary statistics from individual studies (11)(12)(13)(14)(15)(16)(17), these efforts have relied on the heterogeneous set of files provided by the original authors.…”

Predicting RNA splicing from DNA sequence using Pangolin

“…To evaluate Pangolin’s ability to predict the effects of common variants in their extant biological contexts, we used Pangolin to distinguish SNPs that are putatively causal for splicing differences, as determined using a splicing QTL (sQTL) analysis, from other nearby SNPs. We used a previously analyzed set of sQTLs generated using RNA-seq data from whole blood samples from 922 genotyped individuals in the Depression Genes and Networks (DGN) cohort (Mu et al, 2021). Briefly, Leafcutter was used to calculate intron excision ratios, which were used as the splicing phenotype, and sQTLs were called using fastQTL after accounting for principal component covariates.…”

“…RNA-seq data are available (ArrayExpress E-MTAB-6798 (mouse), E-MTAB-6811 (rat), E-MTAB-6813 (rhesus macaque), E-MTAB-6814 (human), E-MTAB-8231 (rhesus macaque, chimpanzee, human used in the splice site evolution analysis)), MFASS data are available (https://github.com/KosuriLab/MFASS), FAS exon 6 data are available (Supplementary Data 1 of Julien et al, 2016), DGN data are available (Mu et al, 2021), BRCA1 data are available (Supplementary Table 1 of Findlay et al, 2018), ClinVar variants with Pangolin annotations are available (Supplementary Data 1), Pangolin is available on GitHub (https://github.com/tkzeng/Pangolin).…”

“…The authors declare that they have no competing interests. FAS exon 6 data are available (Supplementary Data 1 of Julien et al, 2016), DGN data are available (Mu et al, 2021), BRCA1 data are available (Supplementary Table 1 of Findlay et al, 2018), ClinVar variants with Pangolin annotations are available (Supplementary Data 1), Pangolin is available on GitHub (https://github.com/tkzeng/Pangolin).…”

Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

“…Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data [1,2], frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues [3][4][5].…”