The prevalence of type 2 diabetes (T2DM) has been increasing at epidemic proportions worldwide including significantly increased rates among United States (US) minority populations such as Mexican Americans. To date, knowledge about the genetic determinants of T2DM is very limited. However, recent genome-wide association studies (GWASs) of T2DM in populations of European ancestry have localized 19 putative genomic regions that may harbor relevant susceptibility loci. These initial findings reflect association signals related to common variants. The identity of the underlying causal genes and their functional variants still remain unknown. Efforts to replicate the original association findings in ethnically diverse populations have not been universally successful, perhaps due to issues such as allele frequency and linkage disequilibrium (LD) differences. Therefore, an exhaustive resequencing-based search of the genomic regions surrounding these original genetic signals in ethnically diverse populations is required to help identify the underlying causal genes and their likely functional variants. In the proposed project, we will attempt to identify causal variants influencing T2DM based on existing localizations obtained from GWA studies using data/samples from five Mexican American family studies in San Antonio (N = 5,638). To fulfill our objective, we will identify all sequence variants in an approximately 250 kb region around the single nucleotide polymorphism (SNP) of interest by deep resequencing of the selected 16 T2DM candidate gene regions identified from GWASs (Aim 1). Using a highly efficient family-based design, we will then identify the most likely functional variants influencing risk of T2DM in 1,000 effectively sequenced individuals using a novel statistical prioritization method, Bayesian quantitative trait nucleotide (BQTN). The most strongly associated 50 SNPs from Aim 2 will then be typed in a sample of 5,030 adults to confirm their association with T2DM (Aim 3). In addition, we will examine whether the variants found to be significant in adults affect T2DM related traits in ~600 non-diabetic children. To carry out the study, advanced next generation sequencing techniques will be combined with unique computationally intensive statistical genetic techniques to predict those variants most likely to play causal roles in T2DM risk.

Public Health Relevance

Identification of T2DM susceptibility genes in Mexican Americans will have major public health relevance in the US and in developed and developing countries. Identification of genes causally involved in T2DM risk may dramatically speed the quest for novel drug targets and improved pharmacologic interventions. Genetic findings in Mexican Americans may help explain health disparities among US populations.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01DK085524-03
Application #
8141359
Study Section
Special Emphasis Panel (ZDK1-GRB-G (O2))
Program Officer
Akolkar, Beena
Project Start
2009-09-20
Project End
2014-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
3
Fiscal Year
2011
Total Cost
$515,276
Indirect Cost
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
007936834
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Konigorski, Stefan; Wang, Yuan; Cigsar, Candemir et al. (2018) Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations. Genet Epidemiol 42:174-186
Ramstetter, Monica D; Shenoy, Sushila A; Dyer, Thomas D et al. (2018) Inferring Identical-by-Descent Sharing of Sample Ancestors Promotes High-Resolution Relative Detection. Am J Hum Genet 103:30-44
Jun, Goo; Manning, Alisa; Almeida, Marcio et al. (2018) Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees. Proc Natl Acad Sci U S A 115:379-384
Kwon, Minseok; Leem, Sangseob; Yoon, Joon et al. (2018) GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data. BMC Syst Biol 12:19
Latva-Rasku, Aino; Honka, Miikka-Juhani; Stan?áková, Alena et al. (2018) A Partial Loss-of-Function Variant in AKT2 Is Associated With Reduced Insulin-Mediated Glucose Uptake in Multiple Insulin-Sensitive Tissues: A Genotype-Based Callback Positron Emission Tomography Study. Diabetes 67:334-342
Hwang, Jessica L; Park, Soo-Young; Ye, Honggang et al. (2018) FOXP3 mutations causing early-onset insulin-requiring diabetes but without other features of immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome. Pediatr Diabetes 19:388-392
Chien, Li-Chu; Chiu, Yen-Feng (2018) General retrospective mega-analysis framework for rare variant association tests. Genet Epidemiol 42:621-635
Mägi, Reedik; Horikoshi, Momoko; Sofer, Tamar et al. (2017) Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum Mol Genet 26:3639-3650
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
Flannick, Jason (see original citation for additional authors) (2017) Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data 4:170179

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