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.

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
Texas Biomedical Research Institute
San Antonio
United States
Zip Code
Zhou, Hua; Zhou, Jin; Hu, Tao et al. (2016) Genome-wide QTL and eQTL analyses using Mendel. BMC Proc 10:239-244
González Silos, Rosa; Karadag, Özge; Peil, Barbara et al. (2016) Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation. BMC Proc 10:397-404
Wang, Chi; Liu, Jinpeng; Fardo, David W (2016) Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty. BMC Proc 10:411-415
Peralta, Juan Manuel; Almeida, Marcio; Abraham, Lawrence J et al. (2016) Finding potential cis-regulatory loci using allele-specific chromatin accessibility as weights in a kernel-based variance component test. BMC Proc 10:103-108
Conomos, Matthew P; Reiner, Alexander P; Weir, Bruce S et al. (2016) Model-free Estimation of Recent Genetic Relatedness. Am J Hum Genet 98:127-48
Lee, Sungyoung; Choi, Sungkyoung; Kim, Young Jin et al. (2016) Pathway-based approach using hierarchical components of collapsed rare variants. Bioinformatics 32:i586-i594
Thompson, Katherine L; Fardo, David W (2016) Comparing performance of non-tree-based and tree-based association mapping methods. BMC Proc 10:405-410
Engelman, Corinne D; Greenwood, Celia M T; Bailey, Julia N et al. (2016) Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals. BMC Proc 10:67-70
Nicholson, Alexandra M; Finch, NiCole A; Almeida, Marcio et al. (2016) Prosaposin is a regulator of progranulin levels and oligomerization. Nat Commun 7:11992
Zhu, Huanhuan; Wang, Zhenchuan; Wang, Xuexia et al. (2016) A novel statistical method for rare-variant association studies in general pedigrees. BMC Proc 10:193-196

Showing the most recent 10 out of 42 publications