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（由申请人提供）：确定物种适应性进化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。基因组学迫切需要以下方法：（i）模拟各种选择模式和种群历史之间的复杂相互作用；（ii）准确识别和定位适应性特征的突变、基因和途径，以进行进一步的实验验证；以及（iii）有效地分析大样本。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来详细推断未来的进化参数和疾病途径。该应用程序的目标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）确定。使用新颖的、概率可解释的框架；2）开发从全基因组序列联合推断选择和种群历史的框架；3）通过开发和应用人类基因组数据的多基因适应新测试来表征人类适应性进化的基因子网络。所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的全基因组多态性和下一代测序数据集，该研究的贡献将是巨大的，因为它将揭示使人类祖先能够进行基因突变的突变。面对新的环境、饮食和病原体，人类将面临类似的环境压力，而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，而不仅仅是将站点分类为“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，从而实现新的理解。其中基因组特征的组合是第三，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。深入了解选择和动态种群历史在产生人类遗传多样性方面的相互作用，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。