Detecting natural selection by comparing African-ancestry populations

描述(由申请人提供)：非洲血统人群的全基因组关联研究尚未在确定与疟疾和结核病等传染病的牢固的新关联方面提供巨大的回报，尽管已知对这些疾病的耐药性包括大量的可遗传成分。影响传染病风险的基因变异通常处于自然选择之下，导致经历不同选择压力的密切相关种群之间出现异常种群分化的强烈信号。我们和其他人以前曾将这种方法应用于检测疟疾和其他传染病的风险选择变种的信号，并表明这种方法提高了识别疾病关联的能力。当种群之间基因组范围的遗传差异很小时，这种方法的动力是最佳的，这样，感兴趣的风险变种的差异就在基因组范围分布之外。然而，当分析密切相关的总体时，需要非常大的样本量来最小化采样噪声。以前在这一领域的工作一直受到来自密切相关的非洲血统人口的大样本数量的基因数据的极小可获得性的限制。现在，拥有数千个样本的多个密切相关的非洲血统人口中的疟疾、结核病和其他特征的Gwas数据为继续这项研究提供了一个吸引人的机会。在这里，我们将分析西非和非裔美国人的数据集，以识别通过异常种群分化而产生的自然选择的信号，同时解决非洲裔美国人样本中欧洲混合的复杂性。此外，我们将把这些选择信号与独立方法产生的信号结合起来，以增加识别和定位所选变体的能力。我们的发现将引起旨在确定疟疾、结核病和其他传染病的遗传基础的研究人员的高度兴趣。

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.