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

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 data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Effective SNP ranking improves the performance of eQTL mapping

“…Besides the aforementioned methods, there are other methods that can be applied at the joint modeling stage, giving the users freedom to select the method that best suits their needs, for example, Listgarten et al (2010);Fusi et al (2012); Velu and Reinsel (2013); Gao et al (2014);Ju al. (2017); etc.…”

Comparison of confound adjustment methods in the construction of gene co-expression networks

“…Each dataset underwent the same preliminary preprocessing including TMM normalization, gene-level filtering, and gene outlier removal. We applied six data correction procedures to each dataset: 1) no correction, 2) known covariate adjustment, 3) probabilistic estimation of expression residuals (PEER)(1), 4) confounding factor estimation through independent component analysis (CONFETI)(12), 5) removal of unwanted variation (RUVCorr)(7), or 6) principal component adjustment (PC)(13). RUVCorr, CONFETI, and PC adjustment are three alternative data correction approaches designed to identify and remove hidden confounds while retaining patterns of coexpression in the dataset.…”

“…For each dataset we adjusted for the number of PEER factors selected to optimize cis-eGene discovery in the latest quantitative trait locus study by the GTEx Consortium(3): 15 factors for tissues with < 150 samples, 30 factors for tissues with 150-249 samples, 45 factors for tissues with 250-349 samples, and 60 factors for tissues with ≥ 350 samples. CONFETI: Confounding Factor Estimation Through Independent component analysis (CONFETI) is designed to adjust for non-genetic confounding factors while retaining genetically-regulated coexpression (i.e. broad impact eQTL) in the expression dataset(12). Briefly, factors are derived from the full gene expression dataset using independent component analysis.…”

“…CONFETI: Confounding Factor Estimation Through Independent component analysis (CONFETI) is designed to adjust for non-genetic confounding factors while retaining genetically-regulated coexpression (i.e. broad impact eQTL) in the expression dataset(12). Briefly, factors are derived from the full gene expression dataset using independent component analysis.…”

Genome-Wide Expression Quantitative Trait Loci Analysis Using Mixed Models

“…Spurious eQTLs easily result from microarray expression data because confounding effects are induced by various measurement errors ( Churchill, 2002 ; Akey et al, 2007 ). Mixed model analyses, such as the intersample correlation emended method ( Kang et al, 2008 ), probabilistic analysis of genomic data ( Fusi et al, 2012 ), and confounding factor estimation through independent component analysis ( Ju et al, 2017 ), have been suggested to correct for the confounding effects. These analytical models incorporate random effects with an intersample covariance structure that might explain unknown confounding factors produced by measurement errors.…”