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

分子适应过程是进化生物学的核心过程，即基因变异频率的增加使生物体能够在其环境中取得成功。要克服重大挑战，例如传染病病原体进化出抗药性的能力，以及作物害虫战胜各种日益强大的杀虫剂的能力，就需要了解适应的本质。最近的进展表明，适应通常是通过“软选择扫描”发生的，在这种扫描中，一种适应性基因变异起源于多次，或者只有在它在人群中以相当的频率出现后才被偏爱。该项目通过开发新的计算工具来检测和研究软选择扫描的适应发生，有助于提高对适应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高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“大数据”时代的高效计算工具。此外，从进化和生物信息学的角度，它的联合指导努力促进了研究生和博士后科学家的跨学科培养。该项目有四个目标：(1)设计新的测试，以检测从长期遗传变异发生软选择扫描的情况下的选择；(2)在已知正选择的基因组区域中鉴定携带有益等位基因的单倍型；(3)加强自然选择分析的新方法，使其对混杂的人口情景具有鲁棒性；(4)在包括人类、果蝇和疟原虫在内的一系列多物种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，并将利用群体遗传学和聚结理论的思想。它在几个方面取得了突破性进展，提供了对位点频率谱和单倍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。随着对种群内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。该项目解决了这些挑战，其结果将在http://proteomics.ucsd.edu/vbafna/research-2/nsf1458059/上提供。

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/.