The obesity epidemic in the U.S. continues unabated, and is accompanied by substantial complications, including diabetes, cancer, cardiovascular disease, and death. Although the recent rise in obesity is due to lifestyle changes, susceptibility or resistance to obesity is strongly influenced by genetic factors. Genome-wide association (GWA) studies, led by the investigators, have identified dozens of places (loci) in the human genome where common DNA sequence variation is associated with body mass index (BMI), and other measures of obesity. Although successful, these GWA studies have only been able to assess common variation, and most of these variants are noncoding, making it difficult to be confident about which of the multiple genes in each of the associated loci are relevant. Although loci identified from GWA studies can still highlight relevant biological pathways, the uncertainty about the relevant genes represents a key obstacle to completing the functional and computational follow-up studies that would reveal in detail the biology that has been hinted at by the GWA studies. Accordingly, we will focus our efforts on studying rare variation, and particularly rare coding variation, which can more precisely pinpoint genes that influence obesity. We will also reanalyze existing large (N~200,000) GWA data sets, using new """"""""reference panels"""""""" containing rarer genetic variants to be able to test rarer genetic variation for association with BMI and other measures of obesity. We will test and follow-up in replication samples both coding and noncoding variation;we will correlate noncoding variation with genomic annotations that could provide further insight into mechanism (Aim 1). In addition, we will select 600 genes from within the loci identified by GWA studies and perform focused, targeted sequencing of the coding regions of these genes in well-phenotyped samples and samples drawn from the tails of the BMI distribution (Aim 2). To test coding variants that are of even lower frequency and therefore beyond the reach of GWA studies, we will perform more comprehensive genotyping studies in samples drawn from the tails of the BMI distribution, using an """"""""exome chip"""""""" that comprehensively surveys nonsynonymous variation with frequency >0.5%. We will combine these data with additional available whole exome genotype and sequence data to identify individual variants (and hence genes) that show association with BMI and other measures of obesity (Aim 3). Finally, we will integrate the genotyping and sequencing data with other data sets (gene expression, protein-protein interaction) to identify an additional 600 genes for further targeted sequencing to identify genes with collections of rare variants that influence BMI and other measures of obesity, and extend these studies into samples of non-European ancestries (Aim 4). Successfully pinpointing genes that are associated with obesity would be a critical next step in uncovering key pathways that influence obesity in humans, which in turn could guide efforts at therapy and prevention.

Public Health Relevance

Obesity is a pressing public health problem for which there are few long-term effective and safe treatments, and genetic susceptibility to obesity varies widely across the population. Knowledge of the underlying genes where variation increases or decreases risk of obesity would shed light on new biological root causes that in turn could guide the development of new or improved therapies and intervention. We plan to focus on lower frequency genetic variation that has not been well-studied until now, with the goal of identifying specific genes that influence human obesity.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Clinical and Integrative Diabetes and Obesity Study Section (CIDO)
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Karp, Robert W
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Children's Hospital Boston
United States
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(2016) New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun 7:10495
Verweij, Niek; Mateo Leach, Irene; Isaacs, Aaron et al. (2016) Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram. Hum Mol Genet 25:2093-2103
Zhan, Xiaowei; Hu, Youna; Li, Bingshan et al. (2016) RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics 32:1423-6
Pers, Tune H; Timshel, Pascal; Ripke, Stephan et al. (2016) Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes. Hum Mol Genet 25:1247-54
Lessard, Samuel; Manning, Alisa K; Low-Kam, Cécile et al. (2016) Testing the role of predicted gene knockouts in human anthropometric trait variation. Hum Mol Genet 25:2082-2092
Guo, Michael H; Dauber, Andrew; Lippincott, Margaret F et al. (2016) Determinants of Power in Gene-Based Burden Testing for Monogenic Disorders. Am J Hum Genet 99:527-39
(2016) Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun 7:10023
Felix, Janine F; Bradfield, Jonathan P; Monnereau, Claire et al. (2016) Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet 25:389-403
Franke, Barbara; Stein, Jason L; Ripke, Stephan et al. (2016) Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nat Neurosci 19:420-31
Hinney, A; Kesselmeier, M; Jall, S et al. (2016) Evidence for three genetic loci involved in both anorexia nervosa risk and variation of body mass index. Mol Psychiatry :

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