Leveraging ancestral diversity to map adiposity loci in Hispanics

描述（由申请人提供）：肥胖是代谢和心血管疾病的主要危险因素，自20世纪80年代以来，其患病率增加了一倍以上，少数群体的负担最大。大规模的全基因组关联研究（GWAS）已经确定了超过70个与肥胖相关性状明确相关的遗传位点，主要是在欧洲血统人群中。到目前为止，尽管西班牙裔/拉丁裔（HL）人群的肥胖患病率增加，但尚未对任何肥胖相关性状进行大规模GWAS。虽然被归类为一个“种族标签”，但HL人群具有令人难以置信的多样性，并且在遗传上高度混合了来自欧洲，非洲和美洲的最近起源。因此，全基因组关联将需要大量的合作努力和使用先进的统计方法（远远超出标准的GWAS分析）来解释和利用其高度的遗传多样性。在这里，我们建议进行第一次大规模的基因组研究，在HL人群中寻找肥胖易感基因座。对于目标1，我们收集了世界上HL人群的GWAS研究，包括> 50，000名HL男性和女性的高密度SNP阵列数据。对来自1000个基因组计划和其他独特美洲印第安人资源的多种族参考组进行全基因组插补，将允许全面检测HL人群中存在的常见和低频变异，并为解决HL亚群中肥胖相关基因座的遗传风险异质性提供丰富的资源。为了阐明目标2中的种族/民族可转移性和精细地图关联信号，我们将利用来自AA和EA人群中肥胖相关性状的大规模GWAS的数据，这些数据是通过我们与AA（n> 50，000）和EA（GIANT consortium，n> 200，000）财团的合作获得的。在目标3中，我们将采用果蝇的功能分析和生物信息学数据挖掘工具来识别和表征靶基因和功能等位基因，并将关联与生物学途径联系起来。我们具有独特的优势和经验，可以建立大规模的合作，研究HL中肥胖的基因组学。我们的建议也是独特和创新的，将GWAS研究带入下一个转化阶段，实验研究旨在进一步表征肥胖特异性遗传效应。我们的研究可能会提高对肥胖基因组病因学的理解，这些知识可用于减少服务不足和研究不足的少数群体的疾病负担。

DESCRIPTION (provided by applicant): Obesity is a leading risk factor for metabolic and cardiovascular diseases and its prevalence has more than doubled since the 1980's, with the greatest burden carried by minority populations. Large-scale genome-wide associations studies (GWAS) have identified >70 genetic loci that are unequivocally associated with obesity-related traits primarily in European descent populations. So far, no large-scale GWAS for any obesity-related traits have been performed in Hispanic /Latinos (HL) populations, despite their increased prevalence of obesity. Although classified under one 'ethnic label', HL populations are incredibly diverse and genetically highly admixed with recent origins from Europe, Africa and the Americas. Hence, genome-wide association will necessitate a large collaborative effort and the use of advanced statistical methods (that go far beyond standard GWAS analyses) to account for and leverage their high degree of genetic diversity. Here, we propose to perform the first large-scale genomic study in search of obesity-susceptibility loci in HL populations. For aim 1, we have assembled the world's GWAS studies in HL populations, including >50,000 HL men and women with high-density SNP array data. Genome-wide imputation to multiethnic reference panels from the 1000 Genomes Project and other unique Amerindian resources will allow for comprehensive testing of common and low frequency variation present in HL populations as well as provide a rich resource for addressing genetic risk heterogeneity at obesity-related loci across HL sub-populations. To elucidate racial/ethnic transferability and fine-map association signals in aim 2, we will leverage data from large-scale GWAS of obesity-related traits in AA and EA populations that are available to us through our work with AA (n>50,000) and EA (GIANT consortium, n>200,000) consortia. In aim 3, we will employ functional analyses in Drosophila and bioinformatic data-mining tools to identify and characterize the target genes and functional alleles, and link associations with biological pathways. We are uniquely positioned and experienced to establish a large-scale collaboration to study the genomics of obesity in HLs. Our proposal is also unique and innovative for taking a GWAS study to the next translational stage, with an experimental research aim for further characterization of obesity specific genetic effects. Our study may improve the understanding of the genomic etiology of obesity, knowledge which may be used to reduce the burden of disease in underserved and understudied minority populations.