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.
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.
|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|
|Gallaugher, Michael; Canty, Angelo J; Paterson, Andrew D (2016) Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees. BMC Proc 10:91-95|
|Green, Alden; Cook, Kaitlyn; Grinde, Kelsey et al. (2016) A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures. BMC Proc 10:165-170|
|Friedrichs, Stefanie; Malzahn, DÃ¶rthe; Pugh, Elizabeth W et al. (2016) Filtering genetic variants and placing informative priors based on putative biological function. BMC Genet 17 Suppl 2:8|
|Datta, Ananda S; Biswas, Swati (2016) Comparison of haplotype-based statistical tests for disease association with rare and common variants. Brief Bioinform 17:657-71|
|Katsumata, Yuriko; Fardo, David W (2016) On combining family- and population-based sequencing data. BMC Proc 10:175-179|
|Held, Elizabeth; Cape, Joshua; Tintle, Nathan (2016) Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. BMC Proc 10:141-145|
|Lent, Samantha; Deng, Xuan; Cupples, L Adrienne et al. (2016) Imputing rare variants in families using a two-stage approach. BMC Proc 10:209-214|
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