ArticleFrontiers in genetics2015
dcVar: a method for identifying common variants that modulate differential correlation structures in gene expression data.
Article in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- The role of systems biology approaches in determining molecular signatures for the development of more effective vaccines.Expert review of vaccines · 2019Review
- An interaction quantitative trait loci tool implicates epistatic functional variants in an apoptosis pathway in smallpox vaccine eQTL data.Genes and immunity · 2016Article
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4 authors.
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Abstract
Recent studies have implicated the role of differential co-expression or correlation structure in gene expression data to help explain phenotypic differences. However, few attempts have been made to characterize the function of variants based on their role in regulating differential co-expression. Here, we describe a statistical methodology that identifies pairs of transcripts that display differential correlation structure conditioned on genotypes of variants that regulate co-expression. Additionally, we present a user-friendly, computationally efficient tool, dcVar, that can be applied to expression quantitative trait loci (eQTL) or RNA-Seq datasets to infer differential co-expression variants (dcVars). We apply dcVar to the HapMap3 eQTL dataset and demonstrate the utility of this methodology at uncovering novel function of variants of interest with examples from a height genome-wide association and cancer drug resistance. We provide evidence that differential correlation structure is a valuable intermediate molecular phenotype for further characterizing the function of variants identified in GWAS and related studies.
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