Population Genetic Inferences from Dense Genotype Data

描述(申请人提供)：HapMap项目带来的技术创新极大地提高了基因分型的速度和准确性，同时大大降低了成本。公共和私人的努力正开始将前所未有的数量的人类基因和DNA序列数据公之于众。为了从这些数据中对人类变异和过去的人类进化做出最好的推断，我们提出了一系列围绕四个目标的调查。首先，我们将从高通量DNA测序平台上开发新的统计方法来进行群体遗传推断。焦磷酸测序技术将产生组装的比对，代表跨个体(多项式)和跨个体内的同源染色体(二项式)的序列读取采样，产生复杂的混合物。从这样的数据推断种群遗传参数将需要新的统计方法，我们概述了一套开发统计严格方法的计划。第二，我们将开发方法对广泛使用的基因分型板上的SNPs确定偏差进行反向工程，以实现群体遗传推断。在Affymetrix和Illumina的高通量基因分型平台上，SNP的确定方法多种多样，而且往往是无法恢复的。从这些数据中进行统计上可靠的群体遗传学推断需要了解这些平台的确定偏差的性质。我们将利用ENCODE和其他密集重测序数据对确定进行逆向工程，并使用这些推断对高密度SNP平台数据进行确定偏差校正。第三，我们将开发新的方法，从人类种群内部和之间的单倍型多样性模式推断自然选择，并将这些方法应用于公开可用的数据集。从SNP频率和单倍型多样性推断自然选择的方法继续变得强大和特异。这些方法的优化需要对确定、人口统计效应、重组中的局部差异以及缺失数据和单倍型阶段的归因的影响进行校正。我们将利用马尔可夫-隐马尔可夫模型来联合估计选择扫描的幅度、位置和年龄。最后，我们将开发新的方法来预测人类基因组中假定功能区域的核苷酸替换的功能后果。全基因组关联测试将从使用SNP对基因功能产生破坏性影响的可能性的先验推断中获得力量和特异性。此外，在全基因组关联测试之后，将对候选区域进行广泛的重新测序，并且推断许多罕见变异的有害影响的可能性也将是有用的。我们提出了比现有方法更具优势的方法，利用比较基因组数据、蛋白质结构、顺式调控信息和分离变异的模式。 项目简介：该项目将开发从人类DNA重测序和SNP基因数据中进行统计推断的方法，这些方法将允许准确估计描述人类群体变异结构的关键参数。这些推断可以为识别与复杂遗传疾病风险相关的基因提供重要线索。

DESCRIPTION (provided by applicant): Technological innovations arising from the HapMap Project have dramatically increased the speed and accuracy of genotyping while greatly reducing cost. Public and private efforts are beginning to release an unprecedented volume of human genotype and DNA sequence data into the public domain. In order to allow the best inferences about human variation and past human evolution from these data, we propose a series of investigations that center around four aims. First, we will develop novel statistical methods for population genetic inference from high-throughput DNA sequencing platforms. Pyrosequencing technology will generate assembled alignments that represent a sampling of sequence reads across individuals (multinomial) and across homologous chromosomes within an individual (binomial), producing a complex mixture. Inference of population genetic parameters from such data will demand novel statistical approaches, and we outline a set of plans to develop statistically rigorous methods. Second, we will develop methods for reverse-engineer the ascertainment biases of SNPs on widely used genotyping panels so as to enable population genetic inference. SNPs on the high-throughput genotyping platforms of Affymetrix and Illumina were ascertained in diverse and often irretrievable ways. Statistically sound population genetic inference from these data requires an understanding of the nature of the ascertainment bias of these platforms. We will reverse engineer the ascertainment by use of ENCODE and other dense resequence data, and use these inferences to perform ascertainment bias correction to high- density SNP platform data. Third, we will develop novel methods for inference of natural selection from patterns of haplotype diversity within and among human populations and apply these approaches to publicly available data sets. Methods of inference of natural selection from SNP frequency and haplotype diversity continue to gain in power and specificity. Optimization of these methods demands correction for effects of ascertainment, demographic effects, local variation in recombination, and for imputation of missing data and of haplotype phase. We will make use of Markov-Hidden Markov models for jointly estimating the magnitude, location, and age of selection sweeps. Finally, we will develop novel approaches for predicting the functional consequences of nucleotide substitutions in putatively functional regions of the human genome. Whole-genome association tests will gain power and specificity from the use of prior inference of the likelihood that a SNP has a damaging effect on a gene's function. In addition, after genome-wide association tests, there will follow extensive resequencing of candidate regions, and inference of the likelihood of deleterious effects of the many rare variants will also have utility. We propose methods that have advantages over existing approaches, making use of comparative genomic data, protein structure, cis-regulatory information, and patterns of segregating variation. Project Narrative: This project will develop methods of statistical inference from human DNA resequencing and SNP genotype data that will allow accurate estimation of critical parameters that describe the structure of variation in human populations. These inferences can provide vital clues to identifying genes that are associated with risk of complex genetic disorders.