A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

By Oliver Stegle, Leopold Parts, Richard Durbin, John M. Winn. 2010

486 citations recorded by the source corpus.

Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Deep learning for computational biology (2016, neutral): “Better statistical methods have helped to increase the power to detect regulatory QTL (Kang et al , 2008; Stegle et al , 2010; Parts et al , 2011; Rakitsch & Stegle, 2016); however, any mapping approach is intrinsically limited to variation that is present in the training population.”
g:Profiler—a web server for functional interpretation of gene lists (2011 update) (2011, unknown): “Since the publication of g:Profiler in 2007, the tool has found extensive use in a number of projects in stem cell research (37–39), cancer genomics (40,41), machine learning and yeast genomics (42,43), microRNA regulatory networks (44), among others.”
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “1 ) builds on existing approaches for modeling gene expression heterogeneity in bulk data 22,30 , which we here adapt to single-cell transcriptomics.”
Covariate powered cross‐weighted multiple testing (2017, background): “Once they are a matter of fact, it is sometimes possible to remove them by mapping the data to a new set of properly “normalized” and “batch-corrected” variables (Leek and Storey, 2008; Stegle et al., 2010; Wang et al., 2017).”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “66 can be used on a mixed population of cells, to either map them along a differentiation cascade or cluster them into new cell types 55,58 .”
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “It is well established that accounting for covariates and confounding factors can help to increase power in eQTL analyses [11, 25].”
svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “Another extension of the surrogate variable approach in step two has been to model known sources of technical or biological covariation between the measurements for probes, for example in eQTL studies [15, 16].”
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses (2012, methodology): “The protocol is based on the software suite known as PEER, which consists of a collection of Bayesian approaches to infer hidden determinants and their effects from gene expression profiles by using factor analysis methods 1,2 .”