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Abstract
While both genes and environment contribute to phenotype, deciphering environmental contributions to phenotype is a challenge. Furthermore, elucidating how different phenotypes may share similar environmental etiologies also is challenging. One way to identify environmental influences is through a discordant monozygotic (MZ) twin study design. Here, we assessed differential gene expression in MZ discordant twin pairs (affected vs. non-affected) for seven phenotypes, including chronic fatigue syndrome, obesity, ulcerative colitis, major depressive disorder, intermittent allergic rhinitis, physical activity, and intelligence quotient, comparing the spectrum of genes differentially expressed across seven phenotypes individually. Second, we performed meta-analysis for each gene to identify commonalities and differences in gene expression signatures between the seven phenotypes. In our integrative analyses, we found that there may be a common gene expression signature (with small effect sizes) across the phenotypes; however, differences between phenotypes with respect to differentially expressed genes were more prominently featured. Therefore, defining common environmentally induced pathways in phenotypes remains elusive. We make our work accessible by providing a new database (DiscTwinExprDB:http://apps.chiragjpgroup.org/disctwinexprdb/) for investigators to study non-genotypic influence on gene expression.
Highlights
Is there a consistent gene expression signature of environmental influence? Or, how much does gene expression due to potential environmental influence vary across phenotypes? We hypothesized that integrating gene expression data from multiple phenotypes can allow the elucidation of heterogeneity of discordant gene expression and gene signatures across phenotypes
We present a computational workflow to execute differential expression using discordant twin samples across multiple disparate phenotypes
We first identified genes differentially expressed in seven different phenotypes by finding differences in transcript-level expression values and meta-analyzing those transcript-level differences to get overall gene-level differences using monozygotic (MZ) discordant twin samples
Summary
How much does gene expression due to potential environmental influence vary across phenotypes? We hypothesized that integrating gene expression data from multiple phenotypes can allow the elucidation of heterogeneity of discordant gene expression (how gene expression differences between twins vary) and gene signatures across phenotypes. We claim it is possible to measure cross-phenotype heterogeneity by meta-analyzing across mean expression differences for each gene from discordant twin samples. As of this writing, gene expression data from discordant twin samples have not been utilized to perform such analyses. Our study’s goal was to identify significant differentially expressed genes between samples of affected and non-affected MZ twin pairs and integrate mean expression differences across seven phenotypes. We hypothesized that sex may play a role in gene expression variation; we attempted to identify genes in a sex-specific manner across multiple phenotypes
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