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

适应的分子过程使生物体能够在其环境中获得成功的遗传变异频率的增加是进化生物学的核心过程。要克服重大挑战，如感染性病原体对药物产生抗药性的能力以及作物害虫击败各种日益强大的杀虫剂的能力，就需要了解适应的性质。最近的进展表明，适应通常通过“软选择扫描”发生，其中适应性遗传变异起源多次，或者只有在它以相当高的频率出现在人群中之后才变得有利。该项目有助于通过开发新的计算工具来检测和研究软选择性扫描适应的发生，从而促进对适应的基本进化过程的认识。通过跨越进化生物学和生物信息学的多学科团队的相互作用，该项目将进化模拟的进展与现代有效的计算方法相结合，以便在理解适应方面取得进展，同时开发适用于现代廉价测序的“大数据”时代的有效计算工具。此外，它从进化和生物信息学的角度联合指导努力促进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，用于在长期遗传变异发生软选择扫描的情况下检测选择;（2）在已知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）加强分析自然选择的新方法，使其对混杂的人口统计情景具有鲁棒性;（4）在来自多个物种的一系列数据集中应用新的选择方法，包括人类、果蝇和疟原虫疟疾寄生虫。该项目将使用组合优化和机器学习的算法技术，并将利用群体遗传学和结合理论的思想。它在几个方面取得了突破，提供了对位点频率谱和单体型数据模式的更深入理解，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。随着对群体中多个个体的全基因组进行测序变得越来越可能，设计用于检测选择以适应新现象（如软扫描）的工具的智力挑战与将基因组数据集纳入选择研究的计算挑战相吻合。该项目将应对这些挑战，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/.