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Bioinformatics. 2016 Jun 15;32(12):i156-i163. doi: 10.1093/bioinformatics/btw272.

Using genomic annotations increases statistical power to detect eGenes.

Author information

1
Department of Computer Science.
2
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
3
Department of Computer Science Department of Biological Chemistry.
4
Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
5
Department of Computer Science Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.

Abstract

MOTIVATION:

Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.

RESULTS:

We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method.

CONTACT:

buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu.

PMID:
27307612
PMCID:
PMC4908356
DOI:
10.1093/bioinformatics/btw272
[Indexed for MEDLINE]
Free PMC Article

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