Local Adaptations in Humans

描述（由申请人提供）：对不同环境的适应可能在种族群体之间疾病患病率的变化中发挥重要作用。因此，准确描述人类的局部适应对于理解疾病易感性以及其他表型具有重要意义。目前，人们认为许多局部适应是由解剖学上的现代人从东非扩散而来的。如果是这样的话，非非洲个体的多态性模式应该显示出40- 100 Kya的适应特征。然而，迄今为止，对来自有限数量种群的多态性数据的扫描，在当地适应的时间和地理方面产生了相互矛盾的结果。为了澄清这些问题，我们建议：1)从非编码区域生成新的多态性数据，并将其与现有数据结合使用，为15个种群中的每个种群推断一个合理的人口统计学模型。这些模型将提供一个框架，在其中可靠地评估积极选择的证据并估计其时间。2)描述与走出非洲向新环境扩张（即皮肤色素沉着）相关的已知选择表型的适应时间和地理分布。3)描述在一个HapMap群体中，已知基因在选择过程中对未知表型的适应的时间和地理分布。随着多态性研究的快速发展，我们的工作将为基于多态性数据的大规模选择特征分析提供一个解释框架。此外，它将对形成疾病易感位点的人口统计学和选择性因素产生重要见解。

DESCRIPTION (provided by applicant): Adaptations to different environments are likely to play an important role in variation in disease prevalence among ethnic groups. Thus, an accurate characterization of local adaptations in humans is of fundamental importance to understanding disease susceptibility, as well as other phenotypes. Currently, it is thought that many local adaptations result from the dispersal of anatomically modern humans from East Africa. If so, patterns of polymorphism from non-African individuals should show the signature of adaptations dating to 40- 100 Kya. To date, however, scans of polymorphism data from a limited number of populations have yielded conflicting results as to both the chronology and geography of local adaptations. To clarify these issues, we propose to: 1) Generate new polymorphism data from non-coding regions and use it together with existing data to infer a sensible demographic model for each of 15 populations. These models will provide a framework within which to reliably assess the evidence for positive selection and estimate its timing. 2) Characterize the timing and geographic distribution of adaptations for a known selected phenotype linked to Out of Africa expansions into new environments, namely skin pigmentation. 3) Characterize the timing and geographic distribution of adaptations for unknown phenotypes in genes reported to have been under selection in one of the HapMap populations. With the rapid growth of polymorphism studies, our work will provide an interpretive framework for large- scale analysis of the signature of selection based on polymorphism data. Moreover, it will yield important insights into the demographic and selective factors that shape disease susceptibility loci.