Local Adaptations in Humans

描述(由申请人提供)：对不同环境的适应可能在不同种族群体之间的疾病流行差异中发挥重要作用。因此，人类局部适应的准确特征对于理解疾病易感性以及其他表型至关重要。目前，人们认为许多局部适应是来自东非的解剖学上的现代人的分散造成的。如果是这样的话，非非洲人的多态模式应该显示出40-100Kya的适应特征。然而，到目前为止，对有限数量的种群的多态数据进行扫描，得出了关于当地适应的年表和地理位置的相互矛盾的结果。为了阐明这些问题，我们建议：1)从非编码区生成新的多态数据，并将其与现有数据结合使用，为15个群体中的每个群体推断一个合理的人口统计模型。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机。2)描述与走出非洲扩展到新环境有关的已知选定表型的适应时间和地理分布，即皮肤色素沉着。3)描述在一个HapMap群体中对未知表型的适应的时间和地理分布。随着多态研究的快速发展，我们的工作将为基于多态数据的大规模选择特征分析提供一个解释性框架。此外，它还将对形成疾病易感基因的人口统计和选择因素产生重要的见解。

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.