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，人类局部适应的准确表征对于理解疾病易感性以及其他表型至关重要。目前，人们认为，许多地方适应性的结果从东非解剖现代人类的传播。如果是这样的话，来自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，对有限数量种群的多态性数据的扫描在当地适应的时间顺序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）从非编码区生成新的多态性数据，并将其与现有数据一起使用，以推断15个人群中每个人群的合理人口统计模型。这些模型将提供一个框架，在此框架内可靠地评估积极选择的证据并估计其时间。2)描述一个已知的选定表型的适应的时间和地理分布，该表型与非洲以外的新环境的扩张有关，即皮肤色素沉着。3)描述HapMap群体中已被选择的基因中未知表型适应的时间和地理分布。随着多态性研究的迅速发展，我们的工作将为基于多态性数据的选择特征的大规模分析提供一个解释框架。此外，它将产生重要的见解，人口和选择因素，形状疾病易感基因座。

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.