Methods for disease mapping in multi-ethnic populations

项目总结/摘要 全基因组关联研究（GWAS）已经成功地鉴定了影响遗传变异的基因。 常见疾病的风险。GWAS已经确定了数千种相关的变体，在某些情况下， 潜在的因果变异已被精细绘制，提供了关键的生物学见解。大多数研究都是 在欧洲血统的人群中进行，但现在许多研究包括多种族样本。分析 多种族数据的融合具有许多优势，包括检测相关变异的能力增加， 在欧洲是罕见的或不存在的，提高了精细绘图的分辨率，但也面临许多挑战。的 遗传结构在多大程度上是跨种族共享的还不清楚， 跨种族的荟萃分析研究是不确定的，进行精细映射的最佳策略 在多种族数据中，这仍然是一个悬而未决的问题，特别是当允许在一个位点存在多个因果变异时。 这些挑战可能会抑制多种族研究设计，限制发现新关联的机会， 解决少数群体的健康差距问题。在这里，我们建议制定一套完整的方法 和软件，用于多民族人群的疾病绘图，建立在我们的广泛进展的基础上， 过去四年的研究计划。我们的目标是使完全强大的关联和精细映射 在多种族人群中的研究与在单一大陆人群中的研究一样实用。我们的方法 研究将由来自> 900，000个样本（> 700，000个原始基因型/表型）的经验数据驱动 和> 200，000（汇总统计），包括非裔美国人、拉丁美洲人、东亚人和南亚人 样本跨越广泛的定量和疾病表型。我们将为两者开发方法 原始基因型/表型数据和汇总关联统计数据，方法将适用于 包括常见和罕见的变异，包括基于基因的测试。

Project Summary/Abstract Genome-wide association studies (GWAS) have been successful in identifying genetic variants affecting the risk of common diseases. GWAS have identified thousands of associated variants, and in some instances the underlying causal variants have been fine-mapped, providing key biological insights. Most studies have been conducted in populations of European ancestry, but many studies now include multi-ethnic samples. Analysis of multi-ethnic data presents many advantages, including increased power to detect associated variants that are rare or absent in Europeans and increased resolution for fine-mapping, but also many challenges. The extent to which genetic architectures are shared across ethnicities is not well-understood, the implications for meta-analyzing studies across ethnicities are uncertain, and the optimal strategy for performing fine-mapping in multi-ethnic data remains an open question, particularly when allowing for multiple causal variants at a locus. These challenges can inhibit multi-ethnic study designs, limiting opportunities to detect new associations and address health disparities in minority populations. Here, we propose to develop a complete set of methods and software for disease mapping in multi-ethnic populations, building on the extensive progress of our research program over the past four years. Our goal is to make fully powered association and fine-mapping studies as practical in multi-ethnic populations as in studies of a single continental population. Our methods research will be driven by empirical data from >900,000 samples (>700,000 with raw genotypes/phenotypes and >200,000 with summary statistics), including African American, Latino, East Asian and South Asian samples spanning a wide range of quantitative and disease phenotypes. We will develop methods for both raw genotype/phenotype data and summary association statistic data, and the methods will be applicable to both common and rare variation, including gene-based tests.