Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis

Abstract

Global transcript expression experiments are commonly used to investigate the biological processes that underlie complex traits. These studies can exhibit complex patterns of pleiotropy when trans-acting genetic factors influence overlapping sets of multiple transcripts. Dissecting these patterns into biological modules with distinct genetic etiology can provide models of how genetic variants affect specific processes that contribute to a trait. Here we identify transcript modules associated with pleiotropic genetic factors and apply genetic interaction analysis to disentangle the regulatory architecture in a mouse intercross study of kidney function. The method, called the combined analysis of pleiotropy and epistasis (CAPE), has been previously used to model genetic networks for multiple physiological traits. It simultaneously models multiple phenotypes to identify direct genetic influences as well as influences mediated through genetic interactions. We first identify candidate trans expression quantitative trait loci (eQTL) and the transcripts potentially affected. We then clustered the transcripts into modules of co-expressed genes, from which we compute summary module phenotypes. Finally, we applied CAPE to map the network of interacting module QTL (modQTL) affecting the gene modules. The resulting network mapped how multiple modQTL both directly and indirectly affect modules associated with metabolic functions and biosynthetic processes. This work demonstrates how the integration of pleiotropic signals in gene expression data can be used to infer a complex hypothesis of how multiple loci interact to co-regulate transcription programs, thereby providing additional constraints to prioritize validation experiments.

Fig. 1

Hypothetical regulatory architecture of transcripts (T1, …, T6) that serve as an endophenotype for an organism-level trait. (A) Simple model in which all transcripts are associated with trans-acting eQTL1 and part of a single underlying biological process affecting the trait. (B) Model with transcripts grouped into two modules that combine to affect the trait. Models (A) and (B) are indistinguishable using single-locus association. (C) Model obtained with co-expression clustering and CAPE analysis, in which the eQTL has been replaced by two multiple module QTL (modQTL). The genetic effects now map to the two modules distinctly, and the modQTL are linked by a directional influence mapping feed-forward regulation from modQTL1 to the red module via modQTL2.

Fig. 2

Overview of analytical strategy.

Fig. 3

Correlation structure of the three module phenotypes selected for CAPE analysis. (A) Pearson correlations and scatter plots of each pair of module phenotypes. (B) The three module phenotypes decomposed into orthogonal eigentraits, showing phenotype composition and global variance fraction for each eigentrait.

Fig. 4

Adjacency matrix of interactions derived with CAPE (FDR q < 0.05). Markers are designated as sources or targets of directed interactions, and marker-to-phenotype influences are in the rightmost columns. Only candidate markers are shown with chromosome locations labeled, and grey dots marking pairs that were not tested due to LD. Standardized effects (effect divided by standard error) are shown to reflect significance.

Fig. 5

Summary interaction network derived with R/cape, with interacting modQTL labeled by chromosome location on white nodes and gene modules on nodes colored by WCGNA assignments. Width of positive (green) and negative (red) edges represent significance in terms of standardized effect size.