Abstract
Motivation: Expression Quantitative Trait Locus (eQTL) mapping tackles the problem of identifying variation in DNA sequence that have an effect on the transcriptional regulatory network. Major computational efforts are aimed at characterizing the joint effects of several eQTLs acting in concert to govern the expression of the same genes. Yet, progress toward a comprehensive prediction of such joint effects is limited. For example, existing eQTL methods commonly discover interacting loci affecting the expression levels of a module of co-regulated genes. Such “modularization” approaches, however, are focused on epistatic relations and thus have limited utility for the case of additive (non-epistatic) effects.Results: Here we present POEM (Pairwise effect On Expression Modules), a methodology for identifying pairwise eQTL effects on gene modules. POEM is specifically designed to achieve high performance in the case of additive joint effects. We applied POEM to transcription profiles measured in bone marrow-derived dendritic cells across a population of genotyped mice. Our study reveals widespread additive, trans-acting pairwise effects on gene modules, characterizes their organizational principles, and highlights high-order interconnections between modules within the immune signaling network. These analyses elucidate the central role of additive pairwise effect in regulatory circuits, and provide computational tools for future investigations into the interplay between eQTLs.Availability: The software described in this article is available at csgi.tau.ac.il/POEM/.
Highlights
The transcriptional regulatory program that controls the expression of a gene may combine the joint effect of several regulatory mechanisms that act in concert during the cellular response to internal and external signals
Four alternative methods: a residual-based stepwise regression (RBSR) applied to each transcript approach was significantly better than that attained by the independently; a RBSR approach [P < 0.0002 and P < 0.001 partition-based stepwise regression applied to each transcript; paired t-test; Figure 2]
Pairwise eQTL effects provide a model for deciphering regulatory programs that act on gene expression
Summary
The transcriptional regulatory program that controls the expression of a gene may combine the joint effect of several regulatory mechanisms that act in concert during the cellular response to internal and external signals. Transcription profiles can be integrated with genotypic data across a population to identify genomic loci that have an effect on gene expression (Mackay et al, 2009), and it is possible to use these loci as potential regulatory mechanisms. These mechanisms are referred to POEM: Identifying Joint Additive Effects as “expression Quantitative Trait Loci” (eQTLs). In this study we focus on regulatory programs with two eQTLs and refer to such programs as “pairwise effects.”
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