Detecting natural selection by comparing African-ancestry populations

描述（由申请人提供）：在非洲血统人群中进行的全基因组关联研究（GWAS）尚未在确定与疟疾和结核病等传染病的强有力的新关联方面提供巨大的回报，尽管已知对这些疾病的抗性包括大量的遗传成分。影响传染病风险的遗传变异往往处于自然选择之下，导致经历不同选择压力的密切相关群体之间出现异常群体分化的强烈信号。我们和其他人以前曾应用这种方法来检测疟疾和其他传染病的风险变异选择信号，并表明这种方法提高了识别疾病关联的能力。当群体之间的全基因组遗传差异很小时，这种方法具有最佳功效，因此感兴趣的风险变体的差异位于全基因组分布之外。然而，当分析密切相关的群体时，需要非常大的样本量以最大限度地减少采样噪声。以前在这一领域的工作一直受到限制的基因型数据，从密切相关的非洲血统的人口在大样本量的最低可用性。现在，GWAS在多个密切相关的非洲血统人群中获得的疟疾、结核病和其他特征的数据（数千个样本）为继续进行这项研究提供了一个有吸引力的机会。在这里，我们将分析西非和非洲裔美国人的数据集，以确定通过不寻常的人口分化的自然选择的信号，同时解决非洲裔美国人样本中的欧洲混合物的复杂性。此外，我们将联合收割机将这些选择信号与独立方法产生的信号相结合，以增加识别和定位选定变体的能力。我们的研究结果将对旨在确定疟疾，结核病和其他传染病的遗传基础的研究人员产生高度兴趣。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) in African-ancestry populations have yet to provide a large payoff in identifying robust novel associations to infectious diseases such as malaria and tuberculosis, even though resistance to these diseases is known to include a substantial heritable component. Genetic variants that affect the risk of infectious disease are often under natural selection, leading to strong signals of unusual population differentiation between closely related populations that experienced different selective pressures. We and others have previously applied this approach to detect signals of selection at risk variants for malaria and other infectious diseases, and have shown that this approach improves power to identify disease associations. This approach is optimally powered when genome-wide genetic differences between populations are small, so that differences at the risk variants of interest lie outside the genome-wide distribution. However, when analyzing closely related populations, very large sample sizes are needed to minimize sampling noise. Previous work in this area has been limited by the minimal availability of genotype data from closely related African-ancestry populations in large sample size. Now, GWAS data for malaria, tuberculosis and other traits in multiple closely related African-ancestry populations with thousands of samples provides an appealing opportunity to proceed with this research. Here, we will analyze West African and African-American data sets to identify signals of natural selection via unusual population differentiation, while addressing the complication of European admixture in African-American samples. Furthermore, we will combine these signals of selection with those produced by independent approaches, to increase power to identify and localize selected variants. Our findings will be of high interest to investigators aiming to identify the genetic basis for malaria, tuberculosis, and other infectious diseases.