Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

DSpace/Manakin Repository

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

Citable link to this page

 

 
Title: Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches
Author: Sul, Jae Hoon; Han, Buhm; Ye, Chun; Choi, Ted; Eskin, Eleazar

Note: Order does not necessarily reflect citation order of authors.

Citation: Sul, Jae Hoon, Buhm Han, Chun Ye, Ted Choi, and Eleazar Eskin. 2013. “Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches.” PLoS Genetics 9 (6): e1003491. doi:10.1371/journal.pgen.1003491. http://dx.doi.org/10.1371/journal.pgen.1003491.
Full Text & Related Files:
Abstract: Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.
Published Version: doi:10.1371/journal.pgen.1003491
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681686/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11708688
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters