Obesity continues to grow as a modern-day epidemic. Because obesity is a strong risk factor for numerous other metabolic derangements, diabetes, cardiovascular disease, fatty liver disease, various cancers, as well as a host of other morbidities, there is strong motivation to understand the genetic architecture of adiposity traits. Genomewide association scans (GWAS) aimed at adiposity traits recently have produced many findings, implicating numerous novel genes, owing to cooperation of large cohort and family studies in meta-analyses of tens of thousands of subjects. The international Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), the Rotterdam Study (RS), and the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES- Reykjavik Study) was convened to promote the discovery of new genes involved in multiple complex traits using GWAS analysis. The Adiposity Working Group includes these cohorts plus the Family Heart Study (FamHS), the European Special Population Network consortium (EUROSPAN), and the Old Order Amish (OOA), together representing over 37,000 subjects. Data on ~8,200 African-Americans are available from the FamHS and the Candidate gene Association Resource (CARe) resource, which includes the Jackson Heart Study, the Cleveland Family Study, ARIC, CARDIA and MESA. These sample sizes enable detection of variants influencing as little as ~0.5% of trait variance. We propose to extend the meta-analysis approach of these cohorts to investigate body mass index (BMI, wt/ht2), waist circumference (WC), waist-hip ratio (WHR), obesity (BMI>30 kg/m2) and extreme obesity (BMI>40 kg/m2). We will address 4 major aims that go beyond primary gene discovery. We propose to contrast the genetic architecture for adiposity traits between European-Americans and African-Americans;to investigate a series of g x e interaction hypotheses, including sex, age, and smoking;to identify adiposity loci with pleiotropic effects on lipid and glucose metabolism traits to deconstruct the correlations among these risk factors;and to identify and test pathways with high impact on adiposity traits, investigating whether the predominant pathways differ by sex and race. For these aims, we will work with studies from the GIANT (Genetic Investigation of ANthropometric Traits) Consortium to augment power, together potentially including up to ~125,000 European- American subjects. We have a unique opportunity to investigate a number of issues using extant GWAS scans to elucidate the genetic architecture of obesity and related traits in two ethnic groups. Findings from these studies will be validated with additional genotyping and / or sequencing, as warranted. This work will stimulate the discovery of variants and pathways, and potentially extend our understanding of the genetic basis of obesity risk and suggest potential therapeutic targets.
Obesity continues to grow as a modern-day epidemic. Because obesity is a strong risk factor for numerous conditions such as diabetes, cardiovascular disease, fatty liver disease, various cancers, as well as a host of other problems, there is strong motivation to understand the genetic architecture of adiposity traits. Understanding the biological and environmental factors that predispose individuals towards obesity can help us to identify people at high risk for interventions and suggest new therapies to keep them within healthy weight range. New techniques aimed searching the human genome to find adiposity genes recently have produced many new findings, however, they are only a piece of the puzzle. The data suggest that there are many more genes to be found, and that environmental factors may play a role in how genes are expressed. We propose to extend studies of already-collected data on genome-wide association scans (GWAS), basing our work on 8 studies of European-Americans (EA), totaling over 37,000 subjects, and a large dataset of African-Americans (AA), totaling over 16,700 subjects. We will collaborate with another group of studies for these projects, which means we could potentially be analyzing up to 125,000 subjects. Because of this, we expect that our study has great power for discovery of new genes for adiposity and obesity. Specifically, we will study the differences and similarities of the genes associated with adiposity and obesity in EA and AA;we will search for genes whose effects depend of any of sex, age, or smoking;we will test whether genes that influence obesity also have effects on lipid profiles and glucose metabolism;and finally, we will identify biological pathways that may play a part in the development of obesity and test whether those pathways are similar of different by sex and race. We expect that this work will generate many new discoveries and provide important new information regarding the genetic underpinnings of obesity.
|Liu, Ching-Ti; Buchkovich, Martin L; Winkler, Thomas W et al. (2014) Multi-ethnic fine-mapping of 14 central adiposity loci. Hum Mol Genet 23:4738-44|
|An, Ping; Straka, Robert J; Pollin, Toni I et al. (2014) Genome-wide association studies identified novel loci for non-high-density lipoprotein cholesterol and its postprandial lipemic response. Hum Genet 133:919-30|
|Zhang, Qunyuan; Feitosa, Mary; Borecki, Ingrid B (2014) Estimating and testing pleiotropy of single genetic variant for two quantitative traits. Genet Epidemiol 38:523-30|
|Zhang, Qunyuan; Wang, Lihua; Koboldt, Dan et al. (2014) Adjusting family relatedness in data-driven burden test of rare variants. Genet Epidemiol 38:722-7|
|Liu, Ching-Ti; Young, Kristin L; Brody, Jennifer A et al. (2014) Sequence variation in TMEM18 in association with body mass index: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study. Circ Cardiovasc Genet 7:344-9|
|Hoggart, Clive J; Venturini, Giulia; Mangino, Massimo et al. (2014) Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index. PLoS Genet 10:e1004508|
|Ng, Maggie C Y; Shriner, Daniel; Chen, Brian H et al. (2014) Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet 10:e1004517|
|Kraja, Aldi T; Chasman, Daniel I; North, Kari E et al. (2014) Pleiotropic genes for metabolic syndrome and inflammation. Mol Genet Metab 112:317-38|
|Province, Michael A; Borecki, Ingrid B (2013) A correlated meta-analysis strategy for data mining "OMIC" scans. Pac Symp Biocomput :236-46|
|Liu, Ching-Ti; Monda, Keri L; Taylor, Kira C et al. (2013) Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet 9:e1003681|
Showing the most recent 10 out of 14 publications