Fine-mapping causal tissues and genes at disease-associated loci

Abstract

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue’s contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (averageN= 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g.,TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g.,SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5—primarily for autoimmune disease and blood cell traits, including the well-established role ofCTLA4in CD8+T cells for All autoimmune disease. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

“…Spurious enrichments can result from correlations of epigenetic features with true causal cell types via shared regulatory pathways or linkage disequilibrium with causal variants active in distinct cell types. We are aware of a few methods that leverage expression quantitative trait loci (eQTL) to fine-map causal cell-types [53][54][55] but were cautious to employ them as they are limited by the systematic differences between the discoverability of GWAS and eQTL signals 56 and the availability of bone-cell eQTLs, which is restricted to a dataset of primary osteoblasts from surgical explants 57 and a dataset of RANKLstimulated osteoclast-like cells 58 . Instead, we chose to interpret our S-LDSC results principally at the lower-resolution of tissues, which should be less susceptible to spurious correlations between closely related cell types.…”

Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

“…Compared to existing methods, CT-FM and CT-FM-SNP propose several conceptual advances. First, it fine-maps cell types of diseases and risk variants from cREs rather than gene expression QTLs datasets 14,20 , allowing to capture more disease h 2 . Indeed, the h 2 explained by the cREs identified by CT-FM is nearly four times higher than the h 2 explained by fine-mapped eQTLs 22 or mediated by gene expression 23 from all GTeX tissues.…”

“…Methods accounting for gene co-regulation in expression quantitative trait loci (eQTLs) datasets have already been proposed to fine-map causal tissues of human diseases 14 and their risk variants 20 . However, current eQTLs datasets have been generated on bulk tissues that do not capture cell-type-specific effects and often have limited overlap with GWAS results [21][22][23][24] , limiting insights from these methods.…”