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.
Highlights
Genome-wide association studies (GWAS) have successfully identified many genomic loci that impact complex diseases and complex traits
Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants that impact complex diseases. The roles of such studies in disease etiology remain poorly understood, primarily because a large proportion of the GWAS findings are located in the non-coding region of the genome
Recent advancements in high-throughput sequencing technology enable the systematic investigation of molecular quantitative trait loci (QTLs), which are genetic variants that directly affect
Summary
Genome-wide association studies (GWAS) have successfully identified many genomic loci that impact complex diseases and complex traits. Integrating molecular QTL data into GWAS analyses has shown great potential in unveiling the missing links between trait-associated genetic variants and organismal phenotypes [5,6,7]. We focus on a specific type of integrative analysis that aims to assess the overlapping/colocalization of causal GWAS hits and causal molecular QTLs ( known as quantitative trait nucleotides, or QTNs). We provide the necessary background on the existing computational work and outline the computational procedures to achieve our three inference goals for the integrative analysis. Assuming that the annotation d is observed, our previous work [14] proposes a two-stage empirical Bayes procedure to perform accurate and efficient approximate Bayesian inference in the GWAS setting. For analyzing GWAS data, we divide the genome into K roughly independent LD blocks using the approach described in [15], i.e., γ
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