Human Population Genomics

描述（由申请人提供）：破译人类遗传变异的模式将证明我们对疾病和人类进化的遗传基础的理解是不可或缺的。目前，人类种群基因数据正以惊人的速度产生；然而，对其进行分析的方法还处于起步阶段。我的研究目标是在进化的背景下，为基因组规模的群体遗传数据分析创造计算和统计方法。特别地，这一建议侧重于揭示人类基因组中那些可能受到最近选择影响的区域。除了描述那些使我们成为独一无二的人类的基因变化之外，确定最近选择的目标可能在医学上发挥重要作用，例如，通过指出抵抗传染病的目标。该研究有两个具体目标：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化模式，并将其应用于人类变异数据和黑猩猩和即将测序的猕猴基因组的分化数据；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.