Human Population Genomics

描述（由申请人提供）：人类遗传变异的破译模式将证明是我们理解疾病和人类进化的遗传基础不可或缺的。目前，人类群体遗传数据正在以令人难以置信的速度产生;然而，其分析方法仍处于起步阶段。我的研究旨在创建计算和统计方法，用于在进化背景下分析基因组规模的群体遗传数据。特别是，这项建议侧重于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独特的人类的遗传变化之外，确定最近选择的目标可能在医学上发挥重要作用，例如通过指出对感染因子的抗性目标。该提案有两个具体目标：1）开发一种基于聚结的似然方法，用于检测与中性进化不相容的多态性和分歧模式，并将其应用于来自黑猩猩和即将测序的猕猴基因组的人类变异数据和分歧数据，以及2）将隐马尔可夫模型（Hidden Markov Models）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.