Open Access
September 2016 Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control
Xiaoquan Wen
Ann. Appl. Stat. 10(3): 1619-1638 (September 2016). DOI: 10.1214/16-AOAS952


Mapping molecular QTLs has emerged as an important tool for understanding the genetic basis of cell functions. With the increasing availability of functional genomic data, it is natural to incorporate genomic annotations into QTL discovery. Discovering molecular QTLs is typically framed as a multiple hypothesis testing problem and solved using false discovery rate (FDR) control procedures. Currently, most existing statistical approaches rely on obtaining $p$-values for each candidate locus through permutation-based schemes, which are not only inconvenient for incorporating highly informative genomic annotations but also computationally inefficient. In this paper, we discuss a novel statistical approach for integrative QTL discovery based on the theoretical framework of Bayesian FDR control. We use a Bayesian hierarchical model to naturally integrate genomic annotations into molecular QTL mapping and propose an empirical Bayes-based computational procedure to approximate the necessary posterior probabilities to achieve high computational efficiency. Through theoretical arguments and simulation studies, we demonstrate that the proposed approach rigorously controls the desired type I error rate and greatly improves the power of QTL discovery when incorporating informative annotations. Finally, we demonstrate our approach by analyzing the expression-genotype data from 44 human tissues generated by the GTEx project. By integrating the simple annotation of SNP distance to transcription start sites, we discover more genes that harbor expression-associated SNPs in all 44 tissues, with an average increase of 1485 genes per tissue.


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Xiaoquan Wen. "Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control." Ann. Appl. Stat. 10 (3) 1619 - 1638, September 2016.


Received: 1 February 2016; Revised: 1 June 2016; Published: September 2016
First available in Project Euclid: 28 September 2016

zbMATH: 06775280
MathSciNet: MR3553238
Digital Object Identifier: 10.1214/16-AOAS952

Keywords: Bayesian FDR control , genomic annotations , Molecular QTL , QTL mapping

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 3 • September 2016
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