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PLoS Genet. 2019 Dec 13;15(12):e1008481. doi: 10.1371/journal.pgen.1008481. eCollection 2019 Dec.

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

Author information

1
Computer Science Department, University of California Los Angeles, Los Angeles, California, United States of America.
2
Genetic Epidemiology and Statistical Genetics Program, Harvard University, Cambridge, Massachusetts, United States of America.
3
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
4
Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America.
5
New York Genome Center, New York, New York, United States of America.
6
Department of Systems Biology, Columbia University, New York, New York, United States of America.
7
Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California, United States of America.
8
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States of America.
9
Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America.

Abstract

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.

Conflict of interest statement

The authors have declared that no competing interests exist.

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