An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PLoS Computational Biology
Jin Hyun JuJason G Mezey

Abstract

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic dat...Continue Reading

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Citations

Mar 28, 2020·Genetic Epidemiology·X Jessie JengJung-Ying Tzeng
Apr 16, 2020·Trends in Genetics : TIG·Youqiong YeLeng Han

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Methods Mentioned

BETA
PCA
ICE
RNA-seq

Software Mentioned

PEER
MuTHER
R package lrgpr
PANAMA ( Probabilistic ANAlysis of genoMic dAta )
BLAT
CONFETI
LIMIX
ISVA
R
LINEAR

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