Type 2 diabetes (T2D) shows complex inheritance, indicating a causal role for multiple inherited DNA variants. Genome wide association studies (GWAS) have now mapped over 20 novel loci where common variants are associated with risk of T2D. Despite this progress, identified risk alleles explain relatively little of the overall variation in T2D risk. To fully understand the genetic architecture of T2D we need to move from locus to gene to pinpoint specific causal gene(s) responsible for observed associations. We need to address allelic heteroaeneitv. where T2D genes are likely to have multiple different common and rare mutations. We need to explore ethnic variation, where the specific complement of gene mutations contributing to T2D are likely to vary in frequency and effect size across ethnic groups. We hypothesize that: (1) each region identified by GWAS contains at least one causal T2D gene, influenced by at least one common functional variant;(2) in addition to the index variant identified by GWAS, one or more additional common variants in each locus influence T2D;(3) in addition to common variants, each gene may harbor rare mutations that more strongly influence risk of T2D, and (4) the identities, frequencies and effects of these variants vary across multiple ethnic groups representative of the US population. To address these hypotheses we propose three Specific Aims. (1) Bring together multiethnic samples representative of the US population, drawn from the Jackson Heart Study, Framingham Heart Study, Multi-Ethnic Cohort Study, and Diabetes Prevention Program, that together include -29,000 individuals with T2D phenotypes and DNA;(2) Identify and fine-map common variants at each locus in each ethnic group by leveraging our multi-ethnic design and emerging data from the 1000 Genomes Project;and (3) Identify rare causal mutations at each locus by performing deep sequencing of all coding exons in each ethnic group. The co-investigators have extensive experience in complex disease genetics and genomics, next-generation sequencing, statistical genetics, metabolic physiology and epidemiology, and have a long track-record of effective collaboration and leadership that, combined with a large multiethnic, well-phenotyped sample, we hope can contribute to RFA-DK-09-004.

Public Health Relevance

Genetic studies of type 2 diabetes (T2D) have identified new genomic risk regions. We will look in these regions for genes, define variation within genes and variation in different people by bringing together -29,000 individuals from ethnic groups representing the US population, map genes in each region, and identify mutations by detailed DNA analysis, leading to better prevention and treatment of T2D.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01DK085526-06S1
Application #
8880410
Study Section
Special Emphasis Panel (ZDK1)
Program Officer
Akolkar, Beena
Project Start
2009-09-30
Project End
2015-07-31
Budget Start
2013-08-01
Budget End
2015-07-31
Support Year
6
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Broad Institute, Inc.
Department
Type
DUNS #
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Ning, Chao; Kang, Huimin; Zhou, Lei et al. (2017) Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects. Sci Rep 7:590
Ahn, Eunyong; Park, Taesung (2017) Analysis of population-specific pharmacogenomic variants using next-generation sequencing data. Sci Rep 7:8416
Kim, Young Jin; Lee, Juyoung; Kim, Bong-Jo et al. (2017) PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables. Genet Epidemiol 41:41-50
Konigorski, Stefan; Yilmaz, Yildiz E; Pischon, Tobias (2017) Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits. PLoS One 12:e0178504
Joehanes, Roby; Zhang, Xiaoling; Huan, Tianxiao et al. (2017) Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol 18:16
Lee, Selyeong; Won, Sungho; Kim, Young Jin et al. (2017) Rare variant association test with multiple phenotypes. Genet Epidemiol 41:198-209
Manning, Alisa (see original citation for additional authors) (2017) A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk. Diabetes 66:2019-2032
Hwang, Jessica L; Park, Soo-Young; Ye, Honggang et al. (2017) FOXP3 mutations causing early-onset insulin-requiring diabetes but without other features of immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome. Pediatr Diabetes :
Kang, H; Zhou, L; Mrode, R et al. (2017) Incorporating the single-step strategy into a random regression model to enhance genomic prediction of longitudinal traits. Heredity (Edinb) 119:459-467
Cao, Hongyan; Li, Zhi; Yang, Haitao et al. (2017) Longitudinal data analysis for rare variants detection with penalized quadratic inference function. Sci Rep 7:650

Showing the most recent 10 out of 68 publications