The prevalence of obesity continues to rise, along with its metabolic consequences including diabetes, dyslipidemia, hypertension, fatty liver disease, heart disease, and a host of other morbidities. A clear understanding of the genetic architecture of adiposity and its correlated metabolic traits can identify important targets for intervention, either behavioral or pharmaceutical. Significant progress was achieved in the last 4 years of our project in identifying hundreds of common variants associated with adiposity, regional fat distribution, and ectopic fat across 3 major ethnicities;identifying interactions wit physical activity, smoking, gender, and age;identifying pleiotropic loci accounting for the correlated architecture with metabolic traits;and bioinformatic identification of important pathways, tissue specificities, and predicted cellular / organismal functions. In this renewal application, we propose to continue to expand our understanding of the genetic underpinnings of adiposity traits, specifically, body mass index (BMI), measures of centralized obesity (waist- to-hip ratio adjusted for BMI (WHRaBMI) and CT assessed abdominal fat volumes by focusing on rare variation measured by whole exome and whole genome sequencing, carrying out detailed bioinformatic annotation of our findings including predicted functional significance, regulatory function, and pathways using publicly available knowledge databases, and leveraging our collaboration with ENCODE investigators. Finally, we propose to carry out functional mapping and evaluation of our discoveries in humans in a Drosophila model of adiposity and diet-induced diabetes. We will interrogate GWAS-identified genomic regions, by assessing the effect of knock-downs and knock-outs of functional elements (genes, regulatory loci) in those regions on Drosophila adiposity and metabolic phenotypes. This functional mapping will identify genes in the regions of association that influence adiposity traits, providing gene targets for investigation in the human sequence resource. Our basis of operation is within the CHARGE consortium with its outstanding resources and investigators, and with our established collaboration with other consortia, in particular, GIANT. These powerful approaches for discovery, annotation, and screening for functional significance will allow us to expand our knowledge and understanding of the genetic architecture of obesity with the potential to identify pathways / targets amenable to pharmaceutical or behavioral intervention.

Public Health Relevance

The prevalence of obesity continues to rise, along with its metabolic consequences including diabetes, dyslipidemia, hypertension, fatty liver disease, heart disease, and a host of other morbidities. A clear understanding of the genetic architecture of adiposity and its correlated metabolic traits can identify important targets for intervention, either behavioral or pharmaceutical. In this project, we will extend our genetic search from common variants and haplotypes to less frequent / rare variants assessed by chip array or sequencing. The unique information from family studies will be useful in identifying linked regions harboring rare trait variants, search for segregating major genes for obesity, and modeling parent-of-origin effects. All findings will be deeply annotated using a wide variety of knowledge databases, notably those from ENCODE. Finally, we will use an experimental system - Drosophila melanogaster - to carry out functional mapping in associated genomic regions, and to extend our knowledge of the effect of adiposity genes as a function of sex and developmental stage. We anticipate these combined approaches will allow us to continue to expand our knowledge of the genetic architecture of adiposity traits in humans.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
2R01DK089256-05
Application #
8774098
Study Section
Kidney, Nutrition, Obesity and Diabetes (KNOD)
Program Officer
Karp, Robert W
Project Start
2010-09-10
Project End
2017-06-30
Budget Start
2014-09-19
Budget End
2015-06-30
Support Year
5
Fiscal Year
2014
Total Cost
$736,723
Indirect Cost
$159,911
Name
Washington University
Department
Genetics
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
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