Abstract
To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework. The approach can not only identify specific eQTLs that control how genes are clustered and organized toward biological functions, but also enable the investigation of the biological mechanisms that individual eQTLs perturb in a signaling pathway. We applied the new approach to characterize the effects of eQTLs on the structure and organization of gene clusters in Caenorhabditis elegans. This study provides the first characterization, to our knowledge, of the effects of genetic variants on the regulatory network of gene expression. The approach developed can also facilitate the genetic dissection of other dynamic processes, including development, physiology and disease progression in any organisms.
Highlights
An essential step toward constructing the genotype-phenotype map is to understand how DNA polymorphisms affect variation in a phenotype by perturbing transcripts, metabolites and proteins[1]
We used the new model to reanalyze a real example for genetic mapping of gene expression in Caenorhabditis elegans by Kruglyak’s group[5]
The estimated optimal cluster number from the block mixture model is a consequence of the interactions between gene expression differences and genotypic differences
Summary
An essential step toward constructing the genotype-phenotype map is to understand how DNA polymorphisms affect variation in a phenotype by perturbing transcripts, metabolites and proteins[1]. Many approaches have been developed for gene clustering and eQTL mapping, an integrative framework by which to chart a clear picture of genetic mechanisms regulating the function of gene expression has not been constructed. Such integration will facilitate our mechanistic understanding of differentiated gene expression in response to environmental clues, and potentially increase the statistical power of genetic dissection of gene expression. We integrated unsupervised gene expression pattern discovery and interval mapping within the composite likelihood framework and further implemented the two-layer EM algorithm to localize the genomic locations of eQTLs and estimate their genetic effects on gene clustering and function. By reconstructing the regulatory networks among gene clusters, the model provides results that facilitate our mechanistic understanding of how gene expression is mediated in response to developmental and environmental clues
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