Human Population Genomics

人类基因组学

• 批准号: 7283831
• 负责人: ANDREW D KERN
• 金额: $ 4.83万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-16 至 2008-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7283831
• 关键词: Affect; Altretamine; Data; Development; Event; Evolution; Gene Frequency; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Goals; Growth; Heterogeneity; Human; Human Genetics; Human Genome; Infectious Agent; Macaca; Medical Genetics; Medicine; Methods; Mutation; Natural Selections; Pan Genus; Pan troglodytes; Pattern; Play; Population; Population Analysis; Population Genetics; Rate; Research; Resistance; Role; Scanning; Shapes; Statistical Methods; Stochastic Processes; Stretching; Ursidae Family; Variant; base; comparative; human disease; human population genetics; infancy; markov model; theories

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.

描述（由申请人提供）：破译人类遗传变异的模式将有助于我们理解疾病和人类进化的遗传基础。目前，人类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，用于在进化背景下在基因组规模上分析群体遗传数据。特别是，该提案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基因变化之外，确定最近选择的目标可能会在医学中发挥重要作用，例如通过指出对传染原的抵抗力的目标。该提案有两个具体目标：1）开发一种基于合并的似然方法，用于检测与中性进化不相容的多态性和趋异模式，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趋异数据，2）将隐马尔可夫模型（HMM）扩展到群体遗传推理。