Using genomic annotations increases statistical power to detect eGenes

Using genomic annotations increases statistical power to detect eGenes

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 req...

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Main Authors: Duong, Dat, Zou, Jennifer, Hormozdiari, Farhad, Sul, Jae Hoon, Ernst, Jason, Han, Buhm, Eskin, Eleazar
Format: Online
Language:English
Published: Oxford University Press 2016
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908356/
id pubmed-4908356
recordtype oai_dc
spelling pubmed-49083562016-06-17 Using genomic annotations increases statistical power to detect eGenes Duong, Dat Zou, Jennifer Hormozdiari, Farhad Sul, Jae Hoon Ernst, Jason Han, Buhm Eskin, Eleazar Ismb 2016 Proceedings July 8 to July 12, 2016, Orlando, Florida 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. Oxford University Press 2016-06-15 2016-06-11 /pmc/articles/PMC4908356/ /pubmed/27307612 http://dx.doi.org/10.1093/bioinformatics/btw272 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Duong, Dat
Zou, Jennifer
Hormozdiari, Farhad
Sul, Jae Hoon
Ernst, Jason
Han, Buhm
Eskin, Eleazar
spellingShingle Duong, Dat
Zou, Jennifer
Hormozdiari, Farhad
Sul, Jae Hoon
Ernst, Jason
Han, Buhm
Eskin, Eleazar
Using genomic annotations increases statistical power to detect eGenes
author_facet Duong, Dat
Zou, Jennifer
Hormozdiari, Farhad
Sul, Jae Hoon
Ernst, Jason
Han, Buhm
Eskin, Eleazar
author_sort Duong, Dat
title Using genomic annotations increases statistical power to detect eGenes
title_short Using genomic annotations increases statistical power to detect eGenes
title_full Using genomic annotations increases statistical power to detect eGenes
title_fullStr Using genomic annotations increases statistical power to detect eGenes
title_full_unstemmed Using genomic annotations increases statistical power to detect eGenes
title_sort using genomic annotations increases statistical power to detect egenes
description 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.
publisher Oxford University Press
publishDate 2016
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908356/
_version_ 1613594845995073536

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