Estimating colocalization probability from limited summary statistics

Abstract

Background Colocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics. Results We evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets. Conclusions We find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc.

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

“…Several investigators have shied away from recommending the use of eQTL information to prioritize target tissues in GWAS, 42 , 50 citing the tissue-sharing of cis -eQTLs in a large fraction of trait associations as one reason. 51 Recent work by us 52 and others 4 , 53 , 54 has demonstrated the existence of tissue-specific eQTLs and shown potential for leveraging those eQTLs to understand broad patterns of tissue enrichment for human complex traits. For example, Majumdar et al.…”

“… 54 Similarly, other groups have shown enrichments of complex traits for biologically relevant tissues by using colocalization or mediation approaches on eQTL data. 4 , 53 , 55 However, as a result of the inability of existing methods to fully evaluate allelic heterogeneity and LD, the extent of tissue specificity of eQTLs has not been previously fully explored or harnessed. Specifically, it remains an open question whether the observed tissue-specific enrichments are driven by a small number of genes known to possess eQTLs only in specific tissues or by pervasive patterns of tissue specificity confounded by allelic heterogeneity and LD.…”

“…A potential solution to this problem would be to first impute missing variants via a reference LD matrix and use the imputed variants as input to CAFEH. 53 Last, although CAFEH represents a significant advance in evaluating allelic heterogeneity compared to prior methods, it is still difficult distinguish the presence of multiple signals among variants in nearly perfect or very high LD with each other. In that case, one can expect CAFEH to assign all variants in a high LD block almost equal posterior inclusion probability in a single causal component.…”

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

“…Because the GWAS Catalog only reports the lead variant for each locus, and this variant is not necessarily identical to the causal variant for the association, we performed an LD expansion from each top SNP to identify additional possible causal variants. Broad ancestry as reported in the GWAS Catalog was mapped to a 1000 Genomes superpopulation following methods we described recently [23]. For each associated SNP in the GWAS Catalog, an LD expansion was performed to identify SNPs within 1 Mb with LD r 2 ≥ 0.5 in the corresponding 1000 Genomes super-population.…”

“…GWAS Catalog Experimental Factor Ontology (EFO) terms and disease terms curated from the literature were mapped to the 2020 MeSH thesaurus vocabulary using the approach outlined previously [26]. To allow for inexact matches in MeSH terms (e.g., hypertension and systolic blood pressure), we use two similarity metrics: Lin-Resnik average similarity with a cutoff value of 0.75 [26,27] and odds ratio of MeSH term co-occurrence in the same PubMed article with a cutoff of 20 [23]. We count a match between an article identified in our systematic review and a GWAS study if any GWAS Catalog association satisfies the following criteria: (1) The reported variant in the GWAS Catalog has LD R 2 ≥ 0.5 to at least one curated variant, and (2) the reported trait in the GWAS Catalog has similarity to a main or manually curated disease from the PubMed abstract, meeting or exceeding the cutoff value.…”