Novel statistical methods to localize genomic elements underlying adaptive evolution

定位适应性进化背后的基因组元素的新统计方法

• 批准号: 9078921
• 负责人: Sohini Ramachandran
• 金额: $ 32.37万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-06 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9078921
• 关键词: Adult; Africa; African; Algorithms; Alleles; Altitude; Animal Model; Biological; Biology; Biomedical Research; Chromosome Mapping; Complex; Computer software; Computing Methodologies; Critical Pathways; Data; Data Set; Dependency; Detection; Development; Diet; Disease; Disease Pathway; Eating; Elements; Environment; Evolution; Face; Future; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomic Segment; Genomics; Goals; Health; Height; Hemoglobin; Human; Human Genetics; Human Genome; Individual; Insulin; Joints; Knowledge; Lactase; Large-Scale Sequencing; Letters; Light; Measures; Methods; Mission; Modeling; Molecular Evolution; Mutation; Organism; Outcome; Output; Pathway interactions; Phenotype; Population; Population Dynamics; Population Process; Probability; Public Health; Recording of previous events; Regulation; Reporting; Research; Research Personnel; Role; Sampling; Scanning; Shapes; Site; Statistical Methods; Testing; Time; Uncertainty; Ursidae Family; Validation; Variant; Work; exome; fitness; genetic variant; genome sequencing; genome-wide; genomic data; genomic signature; innovation; insight; interest; markov model; mutation screening; next generation sequencing; novel; pathogen; pressure; public health relevance; statistics; tool; trait; whole genome

 DESCRIPTION (provided by applicant): Determining the genomic elements underlying adaptive evolution in a species is essential for connecting genetic variation to phenotypes and fitness, but current statistical methods overlook the confounding effect population histories have on the identification and localization of adaptive mutations. The field of genomics urgently needs methods that (i) model the complex interaction between various modes of selection and population histories; (ii) accurately identify and localize mutations, genes, and pathways underlying adaptive traits for further experimental validation; and (iii) efficiently analyze large scale datasets. Without such methods, the role of adaptation in human molecular evolution cannot be determined. The long-term goal of the researchers is to develop state-of-the-art methods for the detailed inference of evolutionary parameters and disease pathways from next-generation sequencing datasets. The objective of this application is to characterize the genomic elements underlying adaptive evolution in the human genome, through the development and application of a suite of novel statistical and computational methods. The aims of the proposal are to: 1) identify adaptive mutations in diverse human populations using novel, probabilistically interpretable frameworks; 2) develop a frame-work for joint inference of selection and population history from whole-genome sequences; and 3) characterize gene subnetworks underlying human adaptive evolution by developing and applying new tests for polygenic adaption to human genomic data. The methods developed will be applicable to existing and emerging genome- wide polymorphism and next-generation sequencing datasets for humans and a range of other organisms. The contribution of the proposed research will be significant because it will shed light on the mutations that allowed human ancestors to survive in the face of novel environments, diets, and pathogens; humans will face similar environmental pressures in the future, and the proposed research will determine genetic pathways that are critical to human survival in a hostile world. The proposed research is innovative in many distinct ways. First, these new methods will be able to test for multiple modes of selection, moving beyond classifying sites as simply "neutral" or "adaptive". Second, the methods developed here will control for dependencies among statistics measuring selection, enabling new understanding of which combinations of genomic signatures are most informative for the detection of different modes of selection. Third, the proposed research will expand the focus of population-genomic studies of adaptation beyond monogenic adaptation to polygenic adaptation. The out- comes of this research will have an important positive impact: giving new insight into the interaction between selection and dynamic population histories in generating human genetic diversity, while determining how adaptation shapes the human phenotype and advancing our understanding of the biology of the human genome.

 描述（由申请人提供）：确定物种适应性进化背后的基因组元件对于将遗传变异与表型和适应性联系起来至关重要，但当前的统计方法忽视了种群历史对适应性突变的识别和定位的混杂影响。基因组学领域迫切需要以下方法：（i）模拟各种选择模式和种群历史之间复杂的相互作用； (ii) 准确识别和定位适应性特征背后的突变、基因和途径，以进行进一步的实验验证； (iii) 有效分析大型 规模数据集。如果没有这样的方法，就无法确定适应在人类分子进化中的作用。研究人员的长期目标是开发最先进的方法，从下一代测序数据集中详细推断进化参数和疾病途径。该应用程序的目的是通过开发和应用一套新颖的统计和计算方法来表征人类基因组中适应性进化的基因组元素。该提案的目的是：1）使用新颖的、可概率解释的框架来识别不同人群中的适应性突变； 2）开发一个框架，用于从全基因组序列中联合推断选择和种群历史； 3）通过开发和应用人类基因组数据多基因适应的新测试来表征人类适应性进化背后的基因子网络。开发的方法将适用于人类和一系列其他生物体的现有和新兴全基因组多态性和下一代测序数据集。这项研究的贡献将是巨大的，因为它将揭示使人类祖先在面对新环境、饮食和病原体时得以生存的突变。人类未来将面临类似的环境压力，拟议的研究将确定对人类在敌对世界中生存至关重要的遗传途径。拟议的研究在许多方面都具有创新性。首先，这些新方法将能够测试多种选择模式，而不仅仅是将站点分类为简单的“中性”或“适应性”。其次，这里开发的方法将控制测量选择的统计数据之间的依赖性，从而能够新地了解哪些基因组特征组合对于检测不同的选择模式最有信息性。第三，拟议的研究将把适应的群体基因组研究的焦点从单基因适应扩展到多基因适应。这项研究的结果将产生重要的积极影响：为产生人类遗传多样性的选择和动态种群历史之间的相互作用提供新的见解，同时确定适应如何塑造人类表型并增进我们对人类基因组生物学的理解。