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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

基本信息

批准号：
9926886
负责人：
Sohini Ramachandran
金额：
$ 32.44万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-06 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9926886
关键词：
Adult Africa African Algorithms Alleles Altitude Animal Model Biological Biomedical Research Chromosome Mapping Complex Computer software Computing Methodologies Critical Pathways Data Data Set Dependence Detection Development Diet Disease Pathway Elements Environment Evolution Face Future Genes Genetic Genetic Polymorphism Genetic Variation Genome Genomic Segment Genomics Goals Health Height Hemoglobin Human Human Biology Human Genetics Human Genome Individual Joints Knowledge Lactase Large-Scale Sequencing Letters Light Measures Methods Mission Modeling Modernization Molecular Evolution Mutation Organism Outcome Outcomes Research Output Pathway interactions Phenotype Population Population Process Probability Public Health Recording of previous events Reporting Research Research Personnel Role Sampling Scanning Shapes Site Statistical Methods Testing Time Uncertainty United States National Institutes of Health Ursidae Family Validation Variant Work Yang cost exhaustion exome fitness genome-wide genomic data genomic signature innovation insight insulin regulation interest large scale data markov model mutation screening next generation sequencing novel pathogen pressure public health relevance statistics tool trait whole genome

项目摘要

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(申请人提供)：确定一个物种适应性进化背后的基因组元素对于将遗传变异与表型和适应性联系起来是必不可少的，但目前的统计方法忽略了种群历史对适应性突变的识别和定位所产生的混杂影响。基因组学领域迫切需要这样的方法：(I)模拟各种选择模式和种群历史之间的复杂相互作用；(Ii)准确地识别和定位潜在的适应特性的突变、基因和途径，以供进一步的实验验证；以及(Iii)有效地分析缩放数据集。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路径。这项应用的目标是通过开发和应用一套新的统计和计算方法来表征人类基因组中潜在的适应性进化的基因组元素。该提案的目的是：1)使用新的、可概率解释的框架来识别不同人类种群中的适应性突变；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；以及3)通过开发和应用新的多基因适应人类基因组数据的测试来表征人类适应性进化的基因子网络。开发的方法将适用于现有的和新兴的全基因组多态和人类和一系列其他生物的下一代测序数据集。拟议中的研究将做出重大贡献，因为它将揭示使人类祖先能够在新环境、饮食和病原体面前生存的突变；人类未来将面临类似的环境压力，拟议中的研究将确定对人类在敌对世界中生存至关重要的遗传路径。这项拟议的研究在许多不同的方面都是创新的。首先，这些新方法将能够测试多种选择模式，超越了将地点简单地归类为“中性”或“适应性”的范畴。其次，这里开发的方法将控制统计测量选择之间的相关性，使人们能够重新理解基因组签名的哪些组合对于检测不同的选择模式最具信息量。第三，拟议的研究将把适应的种群基因组研究的重点从单基因适应扩大到多基因适应。这项研究的成果将产生重要的积极影响：在产生人类遗传多样性的过程中，为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，并促进我们对人类基因组生物学的理解。