title: 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.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Xiaoquan
authors.identifier: http://orcid.org/0000-0001-8990-2737
authors.initials: X
authors.lastname: Wen
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.|Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Roger
authors.identifier: http://orcid.org/0000-0002-1262-2275
authors.initials: R
authors.lastname: Pique-Regi
authors.affiliation: Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America.|Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Francesca
authors.identifier: http://orcid.org/0000-0001-8252-9052
authors.initials: F
authors.lastname: Luca
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D016014:Linear Models; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D040641:Quantitative Trait Loci / Q000235:genetics*