Population Structure Admixture and Selection across the 1000 Genomes Data Set

描述(由申请人提供)：1000基因组计划(TGP)具有巨大的潜力来回答人类群体遗传学中的基本问题，并塑造未来医学基因组研究的设计。实现这一潜力的关键是开发高效、稳健和强大的计算方法，以分析该项目产生的大量数据。在这里，我们提出了新的方法来描述种群结构，分析混合模式，并在TGP的2000个样本中定位选择特征。我们的项目有三个主要目标。首先，我们将在三峡工程的基础上构建详细的人类人口历史模型。为了实现这一点，我们开发了分析稀有和常见SNPs、拷贝数变异(CNV)和所有被调查人群的单倍型的联合等位基因频谱的方法。拥有完整的序列数据将使这些方法在对最近的过去做出推断方面显著地更好，其中频谱的扭曲对于测试与罕见变异的关联尤其重要。其次，我们将描述四个拉美裔/拉丁裔和三个非裔美国人TGP样本中的人口结构和混杂模式。TGP为这些重要而未被研究的少数民族群体提供了一个促进人口和医学基因组研究的巨大机会。我们将开发新的统计基因组方法来重建混合种群的遗传史，并将这些方法应用于三峡工程样本。我们的方法将为短读取序列数据量身定做，并将利用采样的三重设计。第三，我们将在完整的TGP数据集中检测平衡、净化和正选择的签名。我们将开发软件工具来整合自然选择的特征，基于一种新的方法，该方法使用数值方法来拟合扩散近似到多维站点频谱。该方法允许识别由正选择、平衡选择或负选择引起的扭曲。该方法特别适用于低覆盖率的短读序列数据。这些推论将与GWAHITS图相结合，以加速发现与疾病相关的变异。 相关性：医学遗传学研究为揭示复杂疾病的可遗传基础提供了一种工具。1000基因组计划是一项国际努力，目的是对大约2000名不同的人类受试者的基因组进行排序。我们建议分析这些数据，以表征基因组之间的差异，并催化世界各地的医学和人口基因组研究。

DESCRIPTION (provided by applicant): The 1000 Genomes Project (TGP) has tremendous potential to answer fundamental questions in human population genetics and shape the future design of medical genomic studies. Key to realizing this potential is the development of efficient, robust, and powerful computational methods for analysis of the copious amounts of data generated by the project. Here, we propose novel approaches for characterizing population structure, analyzing patterns of admixture, and localizing signatures of selection across the 2,000 samples of the TGP. Our project has three primary aims. First, we will construct detailed models of human demographic history based on the TGP. To accomplish this, we develop approaches for analyzing the joint allele frequency spectrum of rare and common SNPs, copy number variants (CNVs), and haplotypes across all the populations being surveyed. Having full sequence data will render these approaches dramatically better at making inferences about the recent past, where distortions in frequency spectra are particularly important for testing associations with rare variants. Second, we will characterize patterns of population structure and admixture in the four Hispanic/Latino and three African-American TGP samples. The TGP presents a tremendous opportunity for catalyzing population and medical genomics research for these important and understudied ethnic minority groups. We will develop novel statistical genomic approaches for reconstructing the genetic history of admixed populations and apply these methods to the TGP samples. Our methods will be tailored for short-read sequence data and will leverage the trio design of the sampling. Third, we will detect signatures of balancing, purifying, and positive selection in the full TGP data set. We will develop software tools to integrate signatures of natural selection based on a new approach that uses numerical methods to fit a diffusion approximation to the multi-dimensional site frequency spectrum. This approach allows identification of distortions caused by positive, balancing, or negative selection. The method is especially well suited to low coverage short-read sequence data. These inferences will be integrated with the maps of GWAS hits to accelerate discovery of disease-associated variants. RELEVANCE: Medical genetics research provides a vehicle for uncovering the heritable basis of complex disease. The 1000 Genomes project is an international effort to sequence the genomes of approximately 2,000 diverse human subjects. We propose to analyze these data in order to characterize differences among genomes and catalyze medical and population genomic research throughout the world.