Human Population Genomics

描述(由申请人提供)：破译人类基因变异的模式将被证明是我们理解疾病和人类进化的遗传基础所不可或缺的。目前，人类群体基因数据正在以令人难以置信的速度产生；然而，其分析方法还处于初级阶段。我的研究旨在创建计算和统计方法，在进化的背景下，在基因组水平上分析种群遗传数据。特别是，这项提议的重点是揭示人类基因组中可能受到最近选择的那些区域。除了描述那些使我们成为人类独一无二的基因变化之外，识别最近选择的靶点可能会在医学上发挥重要作用，例如通过指出对感染性病原体的抵抗力靶点。该提案有两个具体目标：1)发展一种基于合并的似然方法来检测与中性进化不相容的多态和分歧模式，并将其应用于人类变异数据和来自黑猩猩和即将测序的猕猴基因组的分歧数据，以及2)将隐马尔可夫模型(HMM)扩展到群体遗传推断。

DESCRIPTION (provided by applicant): Deciphering patterns of human genetic variation will prove integral in our understanding of the genetic underpinnings of disease and human evolution. Currently, human population genetic data is being produced at an incredible rate; however the methods for its analysis are in their infancy. My research aims to create computational and statistical methods for the analysis of population genetic data at the genome-scale, in an evolutionary context. In particular this proposal focuses on uncovering those regions of the human genome that may have been subject to recent selection. Beyond describing those genetic changes which make us uniquely human, identifying the targets of recent selection may prove to play an important role in medicine, for example by pointing to targets for resistance to infectious agents. There are two specific aims to the proposal: 1) the development of a coalescent-based likelihood method for detecting patterns of polymorphism and divergence that are incompatible with neutral evolution and its application to human variation data and divergence data from the chimp and soon-to-be-sequenced macaque genomes, and 2) the extension of Hidden Markov Models (HMMs) to population genetic inference.