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Genetic analysis of transcriptional profiles for the identification of genes influencing obesity

conference contribution
posted on 2023-05-24, 10:46 authored by Jac CharlesworthJac Charlesworth, Curran, JE, Johnson, MP, Goring, HHH, Dyer, TD, Comuzzie, AG, Cole, SA, Mahaney, MC, Jowett, JBM, MacCluer, JW, Collier, GR, Moses, EK, Blangero, J
The identification of candidate genes for human quantitative traits is typically based upon subjective knowledge of a biological pathway that is extrapolated to a particular phenotype. In this study, we propose an objective approach to candidate gene discovery that utilizes large-scale transcriptional profiling to identify novel cis-acting genes that correlate with a given quantitative trait. Using RNA extracted from lymphocytes, we obtained genome-wide quantitative transcriptional profiles from 1,240 individuals in the San Antonio Family Heart Study. In this data set, we were able to significantly detect ~20,000 transcripts. Using quantitative trait linkage analysis, we identified over 3,000 autosomal cis-acting QTLs for which we have significant evidence for variation at the transcript’s genomic location that influences expression levels. To identify potential novel candidate genes involved in obesity, we examined correlations between expression levels of these cis-acting genes and two obesity-related indicators, the body mass index and fat mass (as measured by bioimpedance). Using high-dimensional endophenotypic search procedures, we identified 383 autosomal genes (many novel) that correlate with these obesity-related phenotypes. Several known candidate genes for obesity, including IGBP3 and CDF (which encodes adipsin) were confirmed. The novel genes identified vary in their general biological actions, from mitochondrial functions to inflammation and growth factors. Because these genes were chosen to have large cis-acting effects on transcription levels, it is likely that some of our findings reflect causal relationships with risk of obesity. Our results point to the utility of large scale family-based transcriptional data bases for identifying human quantitative trait loci.


Publication title

Abstract/Session Information, American Society of Human Genetics, 56th Annual Meeting






Menzies Institute for Medical Research


American Society of Human Genetics

Place of publication

United States

Event title

American Society of Human Genetics, 56th Annual Meeting

Event Venue

New Orleans, Louisiana

Date of Event (Start Date)


Date of Event (End Date)


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  • Restricted

Socio-economic Objectives

Expanding knowledge in the biological sciences

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