Expression-Based Empirical Candidate Genes Influencing Body Mass Index

  • Curran, Joanne E (PI)

    Project: Research project

    Project Details

    Description

    DESCRIPTION (provided by applicant): The escalating obesity epidemic is now one of the most significant threats to human health in the 21st century. In this project, we will employ a novel approach to the empirical identification of novel candidate genes influencing body mass index (BMI), in a large family based study. Using genome-wide transcriptional profiles obtained from lymphocyte samples from 1,240 San Antonio Family Heart Study participants, we identified over 1,400 transcripts that exhibit highly significant evidence for cis-regulation as inferred from quantitative linkage analysis. Each of these cis-regulated transcripts has been examined for association with BMI, with 247 transcripts showing evidence for significant correlations of quantitative gene expression levels and BMI, a major risk factor for obesity. This project will exploit this genome-wide expression-based information to rapidly identify regulatory sequence variants that influence transcriptional levels of these novel candidate genes and to assess their influence on BMI and waist circumference, as obesity risk predictors. Using this unique family-based resource of quantitative genome-wide transcriptional profiles, we will empirically nominate and examine 100 novel candidate genes that exhibit both strong evidence for cis-regulation of expression levels and significant correlations between expression levels and BMI. Our prior linkage-based evidence for cis-acting sequence variation can be exploited as a probabilistic causal anchor that should maximize our chance for finding functional variation within proximal promoters. For each of these objectively chosen genes, we will: 1) resequence approximately two kilobases of putative promoter, upstream of transcription start, in 182 founder individuals to identify promoter variants;2) genotype all detected promoter variation in each of the 100 candidate genes in the 1,240 SAFHS samples for whom we have transcriptional profiles;3) test whether promoter sequence variants are associated with gene expression levels of the appropriate candidate gene;4) test for associations between promoter sequence variants, BMI and waist circumference;5) confirm observed associations with BMI and/or waist circumference in two independent sample populations and 6) perform preliminary functional analyses of promoter variants influencing BMI and/or waist circumference. Obesity has reached pandemic proportions in the United States and throughout the world, with an estimated economic burden of approximately $93 billion per year in the United States alone. The results of this project should increase the pace of discovery of novel genes underlying human variation in BMI;a task that has been somewhat slow and unsuccessful to date. By focusing on genes whose transcripts show evidence for both cis- regulatory variation and a strong relationship with BMI, we should maximize our probability for finding causal genetic variants influencing obesity risk. PUBLIC HEALTH RELEVANCE: The estimated economic burden of overweight and obesity in the United States alone is approximately $93 billion per year, making this disease one of major public health importance. In this project, we will employ a novel strategy that should increase the pace of discovery of genes that influence body mass index and waist circumference, major indicators of obesity. The knowledge gained will help contribute to the understanding of the genetics of obesity through the identification of novel and potentially functional candidate genes, assisting in the development of new preventative measures and/or therapies.
    StatusFinished
    Effective start/end date9/30/097/31/12

    Funding

    • National Institutes of Health: $853,485.00
    • National Institutes of Health: $830,995.00

    ASJC

    • Medicine(all)

    Fingerprint

    Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.