Exonic variants and their relation to complex traits in minorities of the WHI

描述(由申请人提供)：在当前的PAGE研究(PAGE 1)中，我们调查了在祖先不同的群体中通过全基因组关联研究(GWAS)确定的许多遗传基因座，并成功地概括和精细地定位了来自欧洲后裔群体的许多GWAS基因座。这些成功支持将基因组搜索扩展到不太频繁和罕见的变异，这些变异没有在GWAs中捕捉到，但代表了基因组中最大比例的遗传变异，并有助于许多复杂性状的遗传性。大多数GWAS是在欧洲人中进行的；然而，需要研究包括低频变异在内的遗传风险因素对不同人群中常见复杂性状的影响，包括对疾病负担较高的未被充分研究的美国少数民族。这个项目的目标是全面调查基因组蛋白质编码区中不常见和罕见的非同义变异，即“外显子组”，以及它们与常见复杂性状的关系，如心血管疾病、癌症、身体成分、血脂、血糖、胰岛素和许多其他多种族人口的结果。具体地说，我们建议使用一个新开发的ExomeChip基因分型平台，并增加专注于祖先多样性和非编码区可能的调控元件的额外内容。该平台性价比高，将提供约350,000个等位基因频率低至0.1%的变异的基因分型数据。这些低频变异中的大多数既没有基因分型，也没有在现有的GWAS阵列上很好地标记。我们将在包括非洲裔美国人(n=7,510)、西班牙裔美国人(n=5,394)和来自妇女健康倡议(WHI)的原住民(n=596)在内的祖先多样化的人群中使用这种外芯片。这些数据将与来自正在进行的WHI研究的23,303名欧洲裔美国人和3,631名非洲裔美国人的ExomeChip基因型和外显子组测序数据结合起来，总共有40,434名WHI参与者。这一资源将使我们能够调查低频和罕见的遗传变异与对公共卫生具有重要意义的复杂疾病之间的关系，以及与WHI中精心策划的中间性状和4800多个表型变量之间的关系。我们将开发新的方法，并将它们应用于这一丰富的资源中，以估计遗传力，并识别多种表型(多效性)和基因-环境相互作用的变异的影响，这些影响是由我们对常见变异的第1页发现所激发的。通过这些努力，我们希望识别多个易感基因座，这些易感基因座可能更好地量化由遗传变异解释的复杂疾病的变异比例，识别人群特定的基因座，并为共同的分子途径提供见解，从而更有效地指导美国不同人群的后续预防和治疗策略。所有的基因和相关表型数据都将通过数据库，如数据库，向公众开放，并作为WHI系统的一部分，为科学界建立一个资源。

DESCRIPTION (provided by applicant): In the current PAGE study (PAGE 1) we investigated many genetic loci identified through genome-wide association studies (GWAS) in ancestrally diverse populations, and successfully generalized and fine-mapped many GWAS loci from studies of European-descent populations. These successes support extending the genomic search to less frequent and rare variants, which have not been captured in GWAS, but represent the largest fraction of genetic variation in the genome and contribute to the heritabilit of many complex traits. Most GWAS have been conducted in Europeans; yet studies investigating the impact of genetic risk factors, including low frequency variants, on common complex traits in diverse populations are needed including understudied US minorities having high burden of disease. The goal of this project is to comprehensively investigate less frequent and rare non-synonymous variants across the protein coding regions of the genome, i.e. the "exome", and their associations with common complex traits, such as cardiovascular disease, cancer, body composition, blood lipids, glucose, insulin, and many other outcomes in a multi-ethnic population. Specifically, we propose to use a newly developed ExomeChip genotyping platform, augmented with additional content focused on ancestral diversity and putative regulatory elements in non-coding regions. This platform is highly cost-efficient and will provide genotyping data on about 350,000 variants with allele frequencies as low as 0.1%. Most of these low frequency variants are neither genotyped nor well tagged on existing GWAS arrays. We will use this ExomeChip in an ancestrally diverse population including African Americans (n=7,510), Hispanics (n=5,394) and Native Americans (n=596) from the Women's Health Initiative (WHI). These data will be combined with ExomeChip genotypes and exome sequencing data from 23,303 European Americans and 3,631 African Americans from ongoing WHI studies, for a total of 40,434 WHI participants. This resource will permit us to investigate relationships between low frequency and rare genetic variants with complex diseases of public health importance as well as with well-curated intermediate traits and over 4,800 phenotypic variables available in the WHI. We will develop new methods and apply them in this rich resource to estimate heritability and to identify effects of variants across multiple phenotypes (pleiotropy) and gene-environment interaction which are motivated by our PAGE 1 findings for common variants. Through these efforts, we expect to identify multiple susceptibility loci that may better quantify the proportion of variation in complex diseases explained by genetic variants, identify population-specific loci and provide insights into shared molecular pathways that will more efficiently direct subsequent prevention and treatment strategies in the diverse US population. All genotype and associated phenotypic data will be made publically available through databases, such as dbGaP and as part of the WHI system, building a resource for the scientific community.