Inferring selection from human population genomic data

项目总结/摘要 确定负责最近适应的基因组区域是群体遗传学的主要挑战。 特别是在人类中，自信地检测最近的适应性自然选择（或 积极的选择）已经证明是麻烦的。事实上，关于最近是否有相当大的争议 积极选择对人类遗传变异有重大影响。这里提出的工作将解决 通过在许多人群中创建一个更完整的正选择图来解决这个问题， 识别对从头突变的选择以及对先前存在的变异的选择。 具体来说，拟议的研究旨在构建一个更强大的积极选举扫描 并且比任何当前现有的方法都准确（目标1）。该工具将利用监督机器学习 技术允许它结合联合收割机信息从一些现有的测试自然选择，并将 广泛测试了大量的人口遗传模拟，提出了广泛的潜在 令人困惑的场景该工具将向公众发布。接下来，它将应用于26名人类 1000个基因组计划（Aim 2）对大样本基因组进行了测序的人群， 揭示了人类适应性进化的克里思，模式和目标的相似性和差异 人口。最后，由于对有益和有害突变的选择会扭曲遗传变异， 我们的方法将被用于识别基因组中受自然选择影响最小的区域，这反过来将 用于更准确地推断人类人口统计学历史（目标3）。 这项工作的指导阶段将在罗格斯大学遗传学系进行 大学这是一个智力刺激的环境与众多的期刊俱乐部，一个优秀的研讨会 系列，和其他几个研究小组使用计算技术。该项目将在 Andrew克恩博士的管理，候选人还将接受机器学习方面的培训 和群体遗传学。Schrider博士还将接受群体遗传学方面的培训，并接受Dr. 乔迪嘿（共同导师）在附近的坦普尔大学。这项培训将帮助Schrider博士获得技能， 不仅帮助完成拟议的工作，而且帮助他过渡到一个 国际公认的独立研究计划，研究进化的力量驱动模式， 人类遗传变异

Project Summary/Abstract Identifying genomic regions responsible for recent adaptation is a major challenge in population genetics. Particularly in humans, the task of confidently detecting the action of recent adaptive natural selection (or positive selection) has proved troublesome. Indeed there is considerable controversy over whether recent positive selection has a substantial impact on human genetic variation. The work proposed here will address this problem by creating a more complete map of positive selection across many human populations, identifying selection on de novo mutations as well as selection on previously standing variation. Specifically, the proposed research seeks to construct a scan for positives election that is more robust and accurate than any currently existing methods (Aim 1). This tool will utilize supervised machine learning techniques allowing it combine information from a number of existing tests for natural selection, and will be tested extensively on a large suite of population genetic simulations presenting a wide range of potentially confounding scenarios. This tool will then be released to the public. Next, it will be applied to 26 human populations in which a large sample of genomes have been sequenced by the 1000 Genomes Project (Aim 2), revealing similarities and differences in the tempo, mode, and targets of adaptive evolution across human populations. Finally, because selection on both beneficial and deleterious mutations skews genetic variation, our method will be used to identify regions of the genome least affected by natural selection, which will in turn be used to produce more accurate inferences of human demographic histories (Aim 3). The mentored phase of this work will be performed within the Department of Genetics at Rutgers University. This is an intellectually stimulating environment with numerous journal clubs, an excellent seminar series, and several other research groups using computational techniques. The project will be performed under the stewardship of Dr. Andrew Kern, from whom the candidate will also receive training in machine learning and population genetics. Dr. Schrider will also receive training in population genetics and guidance from Dr. Jody Hey (Co-mentor) at nearby Temple University. This training will help Dr. Schrider acquire skills that will aid not only in the completion of the proposed work but also his transition to principle investigator of an internationally recognized independent research program studying the evolutionary forces driving patterns of human genetic variation.