Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

Abstract

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.

The GTEx Consortium atlas of genetic regulatory effects across human tissues

“…i.e., multiple independent causal eQTLs per gene (4,14,15). We used two approaches to characterize this: 1) stepwise regression to identify conditionally independent cis-eQTLs, where the threshold for significance was defined by the single cis-eQTL mapping (10), and 2) a Bayesian approach where the posterior probability of linked variants was used to control the local FDR (11,16). Both methods showed concordant results of widespread allelic heterogeneity, with up to 50% of eGenes having more than one independent cis-eQTL in the tissues with the largest sample sizes (figs.…”

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

“…3A). After evaluating the performance of coloc and enloc (16,17), we chose enloc as our primary approach, due to its use of hierarchical models to estimate colocalization priors (16) and its ability to account for multiple causal variants. The coloc assumption of a single causal variant drastically reduces performance especially in large QTL datasets such as GTEx with widespread allelic heterogeneity (g. S27).…”

“…For a given variant associated with multiple traits such as gene expression (eQTL) and complex disease (trait-associated variant), extensive LD makes it challenging to identify the underlying true causal mechanisms. Thus, we conducted colocalization analysis using two independent approaches: coloc (17) and enloc (16)), to estimate whether a gene's 56 expression or a splicing event shares a causal variant with a trait.…”

“…One of the primary tools for functional interpretation of GWAS associations has been integrative analysis of molecular QTLs. Colocalization approaches that seek to establish shared causal variants (e.g., eCaviar (15), enloc (16), and coloc (17)), enrichment analysis (S-LDSC (18) and QTLEnrich (11)) or mediation and association methods (SMR (12), TWAS (13) and PrediXcan (19)) have provided important insights, but they are often used in isolation, and there have been limited prior assessments of power and error rates associated with each (20). Their applications often fall short of providing a comprehensive, biologically interpretable view across multiple methods, traits, and tissues or oering guidelines that are generalizable to other contexts.…”

Genetic association of molecular traits: A help to identify causative variants in complex diseases

“…Even if the population is the same, sampling fluctuation may affect LD estimates. To address this issue of causality inference, sophisticated statistical methods are being developed 22,[124][125][126][127][128][129][130][131][132]. Such a method applied to 7 autoimmune diseases in 180 000 patients revealed that only 25% of the lead GWAS SNPs were eQTLs, 75% of them being cell-specific.…”