Collaborative Research: ABI Innovation: Computational population-genetic analysis for detection of soft selective sweeps

适应的分子过程--使有机体在其环境中取得成功的遗传变异频率的增加--是进化生物学中的一个中心过程。克服重大挑战，如传染病病原体对药物产生抗药性的能力，以及作物病虫害击败各种日益强大的杀虫剂的能力，需要了解适应的性质。最近的进展表明，适应通常通过“软选择性扫描”发生，在这种情况下，适应性遗传变异多次起源，或者只有在种群中大量出现后才变得受欢迎。该项目通过开发新的计算工具，通过软选择性扫描来检测和研究适应的发生，从而促进了对适应基本进化过程的了解。通过一个跨越进化生物学和生物信息学的多学科团队的互动，该项目将进化模拟的进展与现代高效的计算方法相结合，以在理解适应方面取得进展，同时开发适用于廉价测序的现代“大数据”时代的高效计算工具。此外，它还从进化和生物信息学的角度开展联合指导工作，促进对研究生和博士后科学家的跨学科培训。该项目有四个目标：(1)设计新的测试方法，用于在因遗传变异而进行软选择扫描的情况下检测选择；(2)在已知正在经历正选择的基因组区域中确定携带有益等位基因的单倍型；(3)增强对自然选择的新分析方法，使其对复杂的人口统计情况具有健壮性；(4)在包括人类、果蝇和疟疾寄生虫在内的多个物种的一系列数据集中应用新的选择方法。该项目将使用组合优化和机器学习的算法技术，并将利用种群遗传学和联合理论的想法。它在几个方面取得了突破性进展，提供了对位置-频谱和单倍型数据中的模式的更深入的理解，作为选择签名的基础，并协助设计基因组复杂区域的亚型研究。随着对种群内多个个体的整个基因组进行测序的可能性越来越大，设计用于检测选择的工具以适应软扫描等新现象的智力挑战与将基因组数据集纳入选择研究的计算挑战不谋而合。该项目解决了这些挑战，其结果将在http://proteomics.ucsd.edu/vbafna/research-2/nsf1458059/.上公布

The molecular process of adaptation-the rise in frequency of genetic variants that enable organisms to succeed in their environments-is a central process in evolutionary biology. Surmounting significant challenges such as the ability of infectious agents to evolve resistance to drugs and the ability of crop pests to defeat a diverse array of increasingly powerful insecticides requires an understanding of the nature of adaptation. Recent advances have demonstrated that adaptation often occurs via "soft selective sweeps," in which an adaptive genetic variant originates multiple times or has become favored only after it has been present at a substantial frequency in the population. This project contributes to advancing knowledge of the fundamental evolutionary process of adaptation by developing new computational tools to detect and study the occurrence of adaptation by soft selective sweeps. Through the interactions of a multidisciplinary team spanning evolutionary biology and bioinformatics, the project integrates advances in evolutionary simulation with modern and efficient computational methods in order to produce progress on understanding adaptation, while simultaneously developing efficient computational tools applicable in the modern "big-data" era of inexpensive sequencing. In addition, its joint mentorship efforts from evolutionary and bioinformatics perspectives promote interdisciplinary training of graduate students and postdoctoral scientists. The project has four objectives: (1) To design new tests for detecting selection in the case in which soft selective sweeps occur from standing genetic variation; (2) To identify haplotypes that carry a beneficial allele in genomic regions known to be experiencing positive selection; (3) To enhance new methods of analysis of natural selection to make them robust to confounding demographic scenarios; (4) To apply new selection methods in a series of data sets from multiple species, including humans, Drosophila, and Plasmodium malaria parasites. The project will use algorithmic techniques from combinatorial optimization and machine learning, and it will exploit ideas from population genetics and coalescent theory. It breaks ground on several fronts, providing a deeper understanding of the patterns in site-frequency spectra and haplotype data as a basis for selection signatures, and assisting in the design of subtyping studies for complex regions of the genome. As it becomes increasingly possible to sequence whole genomes of multiple individuals within a population, the intellectual challenge of designing tools for detecting selection to accommodate new phenomena such as soft sweeps coincides with the computational challenge of incorporating genomic data sets into selection studies. These challenges are addressed by the project, whose results will be available at http://proteomics.ucsd.edu/vbafna/research-2/nsf1458059/.