Detecting natural selection by comparing African-ancestry populations

通过比较非洲血统人群来检测自然选择

基本信息

批准号：
8242257
负责人：
ALKES L PRICE
金额：
$ 8.08万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-03-09 至 2013-12-31
项目状态：
已结题

项目摘要

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. PUBLIC HEALTH RELEVANCE: Resistance to infectious diseases such as malaria, tuberculosis and HIV/AIDS is known to include a substantial genetically heritable component in the host individual, but association studies have had limited success in identifying the underlying genetic risk variants. We and others have previously shown that genes affecting resistance to infectious disease are often under natural selection, producing strong signals of unusual population differentiation between closely related populations, and that these signals can be used to improve the success of efforts to identify disease-associated variants. In this proposal, we will search for signals of unusual population differentiation in multiple African populations using genome-wide data in large sample size, to identify signals of natural selection that will aid the search for variants associated to infectious disease.

描述（由申请人提供）：非洲官员种群中的全基因组关联研究（GWAS）尚未在识别与疟疾和结核病等传染病的强大新型关联方面具有巨大的回报，尽管人们对这些疾病的抗性包括众所周知的抗性。影响传染病风险的遗传变异通常在自然选择下，导致强烈的信号在经历了不同选择压力的紧密相关人群之间存在异常的人群分化。我们和其他人先前已应用这种方法来检测疟疾和其他传染病的风险变异的选择信号，并表明这种方法可以提高鉴定疾病关联的能力。当种群之间的全基因组遗传差异很小时，这种方法是最佳动力的，因此感兴趣的风险变异的差异在于全基因组分布之外。但是，在分析密切相关的人群时，需要非常大的样本量以最大程度地减少采样噪声。该领域的先前工作受到了与密切相关的非洲官员人群的基因型数据的最小可用性的限制。现在，在多个与数千个样本的非洲官员人群中，疟疾，结核病和其他特征的GWAS数据为进行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，以通过异常的人口分化来识别自然选择的信号，同时解决非裔美国人样本中欧洲混合的并发症。此外，我们将将这些选择信号与独立方法产生的信号结合在一起，以增加识别和定位选定变体的功率。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将引起我们的发现。公共卫生相关性：众所周知，诸如疟疾，结核病和艾滋病毒/艾滋病等传染病的抵抗力包括在宿主个体中具有实质性的遗传性遗传成分，但是关联研究在识别潜在的遗传风险变异方面取得了有限的成功。我们和其他人先前已经表明，影响对传染病的抗性的基因通常在自然选择下，产生密切相关人群之间异常种群分化的强大信号，并且这些信号可用于改善识别疾病相关变体的努力的成功。在此提案中，我们将使用大型样本量的全基因组数据来搜索多个非洲人群中异常人口分化的信号，以识别自然选择的信号，这将有助于寻找与传染病相关的变体。