Methods for disease mapping in multi-ethnic populations

项目摘要/摘要 全基因组关联研究(GWAS)已经成功地识别了影响 常见疾病的风险。Gwas已经确定了数千个相关的变体，在某些情况下， 潜在的因果变异已经被精细地绘制出来，提供了关键的生物学见解。大多数研究都是 是在欧洲血统的人群中进行的，但许多研究现在包括了多种族样本。分析 多种族数据的多样性具有许多优点，包括增强了检测相关变异的能力，这些变异 这在欧洲人中很少见或没有，而且提高了分辨率，用于精细测绘，但也面临许多挑战。这个 基因结构在不同种族之间共享的程度还没有被很好地理解，这意味着 跨种族的荟萃分析研究是不确定的，执行精细映射的最佳策略 在多种族数据中仍然是一个悬而未决的问题，特别是在允许一个基因座有多个因果变量的情况下。 这些挑战可能会抑制多种族研究设计，限制发现新的关联和 解决少数群体的健康差距问题。在这里，我们建议开发一套完整的方法 和多民族人口疾病测绘软件，建立在我们的广泛进步的基础上 过去四年的研究计划。我们的目标是进行全能的联想和精细映射 对多民族人口的研究与对单一大陆人口的研究一样实用。我们的方法 研究将由来自&gt；900,000个样本(&gt；700,000个原始基因型/表型)的经验数据驱动 和&gt；200,000人(摘要统计)，包括非裔美国人、拉丁裔、东亚和南亚人 样本涵盖了数量和疾病表型的广泛范围。我们将为这两种情况开发方法 原始基因型/表型数据和汇总关联统计数据，该方法将适用于 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.