Admixed populations arise when two or more previously isolated populations interbreed. Admixture mapping (AM) methods are used for tracing the ancestral origin of disease-susceptibility genetic loci in the admixed population such as African American and Latinos. AM is different from genome-wide association studies in that ancestry rather than genotypes are tracked in the association process. The power and sample size of AM primarily depend on proportion of admixture and differences in the risk allele frequencies among the ancestral populations. Ensuring sufficient power to detect the effect of ancestry on disease susceptibility is critical for interpretability and reliability of studies using AM approach. However, there is no power and sample size analysis tool existing for AM studies in admixed population. In this study, we developed power analysis of multiancestry AM (PAMAM) to estimate power and sample size for two-way and three-way population admixtures. PAMAM is the first web-based bioinformatics tool developed to calculate power and sample size in admixed population under a variety of genetic and disease phenotype models. It is a valuable resource for investigators to design a cost-efficient study and develop grant application to pursue AM studies. PAMAM is built on JavaScript back-end with HTML front-end. It is accessible through any modern web browser such as Firefox, Internet Explorer, and Google Chrome regardless of operating system. It is a user-friendly tool containing links for support information including user manual and examples, and freely available at https://research.cchmc.org/mershalab/PAMAM/login.html.

中文翻译：

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PAMAM：多祖先混合物映射中的功率分析。

当两个或多个先前孤立的种群杂交时，就会出现混合种群。混合映射（AM）方法用于跟踪混合人群（如非裔美国人和拉丁裔）中疾病易感性基因位点的祖先起源。AM与全基因组关联研究的不同之处在于，在关联过程中跟踪祖先而不是基因型。AM的功效和样本大小主要取决于混合比例以及祖先人群之间风险等位基因频率的差异。确保足够的能力来检测祖先对疾病易感性的影响对于使用AM方法的研究的可解释性和可靠性至关重要。但是，目前尚无用于混合人群中AM研究的功效和样本量分析工具。在这个研究中，我们开发了多祖先AM（PAMAM）的功效分析，以估算两向和三向人群混合物的功效和样本量。PAMAM是第一个基于网络的生物信息学工具，用于在多种遗传和疾病表型模型下计算混合种群的功效和样本量。对于研究人员而言，这是宝贵的资源，可用于设计具有成本效益的研究并开发用于进行AM研究的拨款申请。PAMAM构建在具有HTML前端的JavaScript后端上。不管使用什么操作系统，都可以通过任何现代的Web浏览器（例如Firefox，Internet Explorer和Google Chrome）访问它。它是一种用户友好的工具，包含支持信息的链接，包括用户手册和示例，可从https://research.cchmc.org/mershalab/PAMAM/login.html免费获得。