With incredible rapidity the genome has become accessible. No fewer than 12 genome wide association studies have been performed for type 2 diabetes leading to the identification of at least 18 variants reproducibly associated in populations of recent European descent. Replication of specific variants in other population groups, however, has not been as consistent though the genes themselves often show associations. Interestingly, these 18 variants/genes do not seem to be related to each other through common metabolic pathways nor are most associated with previously understood connections to glucose homeostasis. Each merits intense investigation to develop deeper and broader understanding of their variation and function. Even with these 18 genes, we remain far short of a comprehensive understanding of the genetic underpinnings of type 2 diabetes in any population. The success in European populations indicates that extension to other ethnic groups will assuredly identify other genes. These efforts will be enhanced through appropriate combinations of data from multiple groups and careful testing within groups. The resources we have developed enabled an initial genome wide association study of type 2 diabetes among Mexican Americans and a second much larger such study with genetic markers currently being typed at the Center for Inherited Disease Research (CIDR) using the Affymetrix Genome-Wide Human SNP Array 6.0. The analyses and follow-up of these data in conjunction with a cadre of investigators bringing similar resources and analytic expertise for other ethnic groups will accelerate identification, replication and functional understanding of the genetic underpinnings of type 2 diabetes. We propose to become part of the NIDDK's """"""""Multiethnic Study of Type 2 Diabetes Genes"""""""" (RFA-DK-09-004) and to:
Aim 1 : Comprehensively assess genetic variation in the vicinity of all SNPs reproducibly associated with type 2 with the risk for type 2 diabetes, its complications and related quantitative phenotypes in Mexican Americans from Starr County, Texas;
Aim 2 : Identify new genetic risk factors for type 2 diabetes in Mexican Americans, and Aim 3: Distinguish causal polymorphisms through deep resequencing and analyses that exploit network theory and evolutionary contexts. These studies will lead to substantial insights into the mechanisms leading to type 2 diabetes and move us much closer to the understanding required to slow its onset or prevent it.

Public Health Relevance

Type 2 diabetes is increasing at unprecedented rates in nearly all populations. Recent large scale studies have identified several genes that play a role in this. Using extensive data that we have developed on type 2 diabetes among Mexican Americans and joining our efforts with other groups will lead to the identification of other genes and, ultimately, strategies to slow the onset of type 2 diabetes and prevent it. Without such, we can only expect a continuing and increasing epidemic.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZDK1-GRB-G (O2))
Program Officer
Akolkar, Beena
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Texas Health Science Center Houston
Schools of Medicine
United States
Zip Code
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
Graff, Mariaelisa; Emery, Leslie S; Justice, Anne E et al. (2017) Genetic architecture of lipid traits in the Hispanic community health study/study of Latinos. Lipids Health Dis 16:200
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
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

Showing the most recent 10 out of 64 publications