Abstract
Objective: The body mass index (BMI) measured as weight in relation to height is an important monitor for obesity and diabetes, with individual variation under control by genetic and environmental factors. In transcriptome-wide association studies on BMI, the genetic contribution calls for controlling of genetic confounding that affects both BMI and gene expression. We performed a global gene expression profiling of BMI on peripheral blood cells using monozygotic twins for efficient handling of genetic make-ups. Methods: We applied a generalized association method to genome-wide gene expression data on 229 pairs of monozygotic twins (age 56-80 years) for detecting diverse patterns of correlation between BMI and gene expression. Results: We detected seven probes associated with BMI with p<1e-04, among them two probes with p<1e05 (p=2.83e-06 AAK1; p=7.83e-06 LILRA3). In total, the analysis found 1579 probes with nominal p<0.05. Biological pathway analysis of enriched pathways found 50 KEGG and 45 Reactome pathways (FDR<0.05). The identified top functional pathways included immune function, JAK-STAT signalling, insulin signalling and regulation of energy metabolism. Conclusion: This transcriptome-wide association study using monozygotic twins and generalized correlation identified differentially expressed genes and a broad spectrum of enriched biological pathways that may implicate metabolic health.
Highlights
The body mass index (BMI) quantifies the amount of tissue mass including muscle, fat and bone in an individual
Instead of focusing on the genetic polymorphisms which are static across life span, analysis of gene expression profiles can directly depict the dynamic activity of functional genes in regulating the variation of BMI, especially when controlling for genetics
We first removed house-keeping probes by calculating the coefficient of variation (CV) as standard deviation divided by mean of expression measurement for each probe
Summary
The body mass index (BMI) quantifies the amount of tissue mass including muscle, fat and bone in an individual. It is highly associated with cardiovascular disease and diabetes and has profound influences on life quality and mortality [1,2,3]. Many epidemiological and molecular studies have been conducted to find the genetic and non-genetic (environmental) mechanisms underlying individual BMI variation in order to identify potential causes of MetS and eventually to find strategies for mitigating its burden to public health. Multiple genetic variants have been reported to affect BMI in genome-wide association studies, albeit with the proportion of BMI variation accounted for remaining far from the overall genetic contribution estimated in twin. Instead of focusing on the genetic polymorphisms which are static across life span, analysis of gene expression profiles can directly depict the dynamic activity of functional genes in regulating the variation of BMI, especially when controlling for genetics
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