Population Genetic Inferences from Dense Genotype Data

根据密集基因型数据进行群体遗传推断

• 批准号: 7918266
• 负责人: Carlos Daniel Bustamante
• 金额: $ 33.08万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-21 至 2011-09-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7918266
• 关键词: Age; Amino Acids; Animal Model; Area; Award; Binding Sites; Chromosomes; Code; Communities; Complex; Complex Mixtures; DNA; DNA Resequencing; DNA Sequence; Data; Data Set; Demography; Disease; Engineering; Evolution; Frequencies; Functional RNA; Genes; Genetic; Genetic Polymorphism; Genetic Recombination; Genome; Genotype; Germ Cells; Haplotypes; Hereditary Disease; Human; Human Genetics; Human Genome; Individual; Institutes; Investigation; Location; Methods; Mutation; Natural Selections; Nature; Nucleotides; Pattern; Phase; Population; Population Genetics; Procedures; Property; Public Domains; Reading; Research; Risk; Role; SNP genotyping; Sampling; Sequence Alignment; Series; Shotguns; Site; Solid; Sorting - Cell Movement; Specificity; Speed; Statistical Methods; Structure; Technology; Testing; Variant; comparative genomics; cost; density; gene function; genetic analysis; genome wide association study; genome-wide; human DNA; improved; innovation; markov model; method development; novel; novel strategies; protein function; protein structure; sound; technological innovation; theories; tool; transcription factor

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.

描述（由申请人提供）：人类基因组单体型图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在开始向公共领域发布前所未有的人类基因型和DNA序列数据。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，我们将开发新的统计方法，用于从高通量DNA测序平台进行群体遗传推断。焦磷酸测序技术将产生组装的比对，其代表个体间（多项）和个体内同源染色体间（二项）的序列读数采样，产生复杂的混合物。从这些数据中推断群体遗传参数将需要新的统计方法，我们概述了一套计划，以开发统计上严格的方法。其次，我们将开发反向工程的方法，广泛使用的基因分型面板上的SNP的确定偏差，使人口遗传推断。在Affyandroid和Illumina的高通量基因分型平台上，SNP以不同且通常不可挽回的方式被确定。从这些数据中进行统计学上合理的群体遗传推断需要了解这些平台的确定偏倚的性质。我们将使用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倚校正。第三，我们将开发新的方法来推断自然选择的单倍型多样性的模式内和人群之间，并将这些方法应用于公开的数据集。从SNP频率和单倍型多样性推断自然选择的方法继续获得力量和特异性。这些方法的优化需要校正的影响，确定，人口统计学的影响，重组的局部变化，以及缺失数据和单倍型阶段的插补。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的pupirine功能区域的核苷酸取代的功能后果。全基因组关联测试将从使用SNP对基因功能具有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具有实用性。我们提出的方法，具有优势，现有的方法，利用比较基因组数据，蛋白质结构，顺式调控信息，和模式的分离变异。 项目叙述：该项目将开发从人类DNA重测序和SNP基因型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。这些推论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。