The obesity epidemic in the U.S. continues unabated, and is accompanied by substantial complications, including diabetes, cardiovascular disease, and death. Obesity is strongly influenced by sequence variation in as yet unknown susceptibility genes. Whole genome association scans offer a potentially powerful method for identifying common variants with modest effects on obesity, but the deluge of data from these studies will require careful analysis and rigorous follow-up. The first whole genome association studies are now being performed in large samples in which body mass index has been measured, and we will have access to whole genome association data from 3,000 individuals with measures of obesity (body mass index, BMI). We hypothesize that these studies will identify many potential associations between genetic variants and obesity, representing a mix of largely false positive results interspersed with a smaller number of true associations. It will be critical to perform appropriate and rigorous follow-up to distinguish the true causal variants from the false leads. The combination of multiple large well-characterized cohorts with measures of obesity, high throughput genotyping and robust analytic methods will permit us to rapidly follow up the preliminary results from the whole genome association scans. Specifically, we will be able to extract from the mass of data those genetic variants that are truly associated with obesity, even if the effects are modest. This will lay the groundwork for future studies of the phenotypic consequences of these variants on other obesity-related phenotypes and the risks of complications such as diabetes and cardiovascular disease, and for studies of gene-gene and gene-environment interactions. Successful identification of genes that are convincingly associated with obesity would highlight key pathways that influence obesity in humans, guiding efforts at therapy and prevention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK075787-03
Application #
7628614
Study Section
Genetics of Health and Disease Study Section (GHD)
Program Officer
Karp, Robert W
Project Start
2007-06-08
Project End
2012-05-31
Budget Start
2009-06-01
Budget End
2010-05-31
Support Year
3
Fiscal Year
2009
Total Cost
$611,172
Indirect Cost
Name
Children's Hospital Boston
Department
Type
DUNS #
076593722
City
Boston
State
MA
Country
United States
Zip Code
02115
Guo, Michael H; Plummer, Lacey; Chan, Yee-Ming et al. (2018) Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data. Am J Hum Genet 103:522-534
Cesana, Marcella; Guo, Michael H; Cacchiarelli, Davide et al. (2018) A CLK3-HMGA2 Alternative Splicing Axis Impacts Human Hematopoietic Stem Cell Molecular Identity throughout Development. Cell Stem Cell 22:575-588.e7
Winkler, Thomas W; Günther, Felix; Höllerer, Simon et al. (2018) A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications. Nat Commun 9:1946
Astley, Christina M; Todd, Jennifer N; Salem, Rany M et al. (2018) Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity. Clin Chem 64:192-200
Turcot, Valérie (see original citation for additional authors) (2018) Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet 50:26-41
Marouli, Eirini (see original citation for additional authors) (2017) Rare and low-frequency coding variants alter human adult height. Nature 542:186-190
Justice, Anne E (see original citation for additional authors) (2017) Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun 8:14977
Guo, Michael H; Nandakumar, Satish K; Ulirsch, Jacob C et al. (2017) Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms. Proc Natl Acad Sci U S A 114:E327-E336
Guo, Michael; Liu, Zun; Willen, Jessie et al. (2017) Epigenetic profiling of growth plate chondrocytes sheds insight into regulatory genetic variation influencing height. Elife 6:
Winkler, Thomas W; Justice, Anne E; Cupples, L Adrienne et al. (2017) Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation. PLoS One 12:e0181038

Showing the most recent 10 out of 95 publications