Novel statistical methods to localize genomic elements underlying adaptive evolution

 描述（由申请人提供）：确定一个物种中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至关重要，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群体历史之间的复杂相互作用;（ii）准确地识别和定位适应性性状背后的突变、基因和途径，以供进一步的实验验证;以及（iii）有效地分析大规模的遗传变异。 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，用于从下一代测序数据集详细推断进化参数和疾病途径。本申请的目的是通过一套新的统计和计算方法的开发和应用，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）使用新的，概率可解释的框架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联合推断选择和群体历史; 3）通过开发和应用多基因适应人类基因组数据的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现有和新兴的全基因组多态性和人类和一系列其他生物体的下一代测序数据集。拟议的研究的贡献将是显著的，因为它将揭示允许人类祖先在面对新环境，饮食和病原体时生存的突变;人类在未来将面临类似的环境压力，拟议的研究将确定对人类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，而不仅仅是将网站分类为“中性”或“自适应”。其次，这里开发的方法将控制统计测量选择之间的依赖关系，使新的理解基因组签名的组合是最有用的检测不同的选择模式。第三，拟议的研究将扩大适应的群体基因组研究的重点，从单基因适应到多基因适应。这项研究的结果将产生重要的积极影响：对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提供新的见解，同时确定适应如何塑造人类表型，并推进我们对人类基因组生物学的理解。

 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.