Diabesity Gene Discovery at Chromosome 6q23

描述（由申请人提供）：2型糖尿病（T2DM）及其相关疾病是一个主要的健康问题，并且在大多数人群中发病率迅速上升。我们已经确定了一个与墨西哥裔美国人的“糖尿病”表型密切相关的染色体位置（6q23）。表型包括空腹血浆胰岛素和肥胖。这些发现最近在其他几个人群中得到了独立的重复。我们建议在两个大型人群研究（SAFADS/SAFGDS和VAGES）的大型墨西哥裔美国人队列中定位和鉴定位于6q23的易感基因或基因。这是墨西哥裔美国人dna和糖尿病基因发现表型数据的最大存储库，为基因定位提供了前所未有的能力。全基因组扫描在SAFADS/SAFGDS中已经完成，并将很快在VAGES中完成。我们将从公共和私人SNP数据库中选择单核苷酸多态性（SNP），这些数据库以高密度（每10 kb 1个）覆盖链接区域。snp将以基因为中心定位在连锁峰值区域内，该区域占连锁证据的99%。这些snp将使用高通量程序进行基因分型，所得数据将用于测量连锁不平衡并定义该区域内的单倍型结构。SNP基因分型将以一种新的成本最小化的三阶段策略进行，使用逐步扩大的数据集。我们将计算snp与糖尿病表型的关联，并根据相关snp进行连锁分析，以确定包含导致观察到的连锁的变异的位点。在一个或多个特定的靶基因中，所有共同的以基因为中心的snp将被基因分型，并使用一种新的数量性状核苷酸（QTN）方法对数据进行分析，该方法同时利用来自所有snp的信息来充分解释关联。对最高概率基因的严格验证将利用一系列统计和分子遗传学技术：(i) QTN和条件连锁分析；（ii）基因/mRNA/蛋白质结构、功能和细胞位置的生物信息学分析和预测；（iii）在设计补充的体内临床研究之前，在适当的细胞系中，使用DNA微阵列和定量RT-PCR对正常和变异形式的基因进行表达分析，在各种营养和激素干扰下，阐明病理生理机制。

DESCRIPTION (provided by applicant): Type 2 diabetes mellitus (T2DM) and related disorders are a major health problem and rapidly increasing in prevalence in most human populations. We have identified a chromosomal location (6q23) which is strongly linked to "diabesity" phenotypes in Mexican Americans. The phenotypes include fasting plasma insulin and obesity. These findings have recently been independently replicated in several other populations. We propose to localize and identify the susceptibility gene or genes located at 6q23 in a large Mexican American cohort comprising > 2,000 subjects from two large population studies (SAFADS/SAFGDS and VAGES). This is the largest repository of Mexican American DNAs and phenotypic data for diabetes gene discovery and provides unprecedented power to localize genes. Genome-wide scans are complete in SAFADS/SAFGDS and will soon be completed in VAGES. We will select single nucleotide polymorphisms (SNPs), from public and private SNP databases, which cover the linked region at high density (1 per 10 kb). The SNPs will be located gene-centrically within the linkage peak region responsible for > 99% of the evidence for linkage. These SNPs will be genotyped using high-throughput procedures, and the resulting data will be used to measure linkage disequilibrium and define haplotype structure within the region. SNP genotyping will be performed in a novel cost-minimizing three-phase strategy using progressively larger datasets. We will calculate association of SNPs with diabesity phenotypes and perform linkage analysis conditional on associated SNPs, to define loci which contain variants that account for the observed linkage. All common gene-centric SNPs within a defined target gene or genes will be genotyped and the data analyzed using a novel quantitative trait nucleotide (QTN) approach which utilizes the information from all SNPs simultaneously to fully account for association. Rigorous validation of the highest probability gene(s) will utilize a battery of statistical and molecular genetic techniques: (i) QTN and conditional linkage analyses; (ii) bioinformatic analysis and prediction of gene/mRNA/protein structure, function, and cellular location; and (iii) expression analyses of normal and variant forms of the gene using DNA microarrays and quantitative RT-PCR, in appropriate cell lines, under various nutritional and hormonal perturbations, to elucidate pathophysiological mechanisms, prior to the design of complementary in vivo clinical studies.