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Statistical methods to infer structure and impact of ancient admixture

推断古代外加剂的结构和影响的统计方法

基本信息

批准号：
8927663
负责人：
Sriram Sankararaman
金额：
$ 3.75万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2015-12-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Studies of human genetic variation over the last decade have revealed that mixture events between highly diverged population groups (archaic admixture), such as between Neandertals and non-Africans, have been a common occurrence and are likely to have had a major impact on human phenotypes. For example, studies have documented its phenotypic impact in analyses of individual loci, such as the MHC locus. However, a lack of adequate analytical tools has hindered a systematic understanding of the phenotypic impact of archaic admixture. This K99/R00 research proposal proposes to develop and validate statistical methods to infer the genetic structure arising from archaic admixture and to leverage this structure to identify genetic variants introduced by archaic admixture that influence phenotypes. Insights from the application of these methods will not only produce a more complete understanding of the genetic factors underlying complex phenotypes, such as common diseases, but will also ensure that currently under-served minority populations, many of whom descend from admixture events or from ancestral groups distinct from those of Europeans, can be studied just as effectively as populations of European descent and can benefit from the discoveries of genomic medicine. The first goal of this proposal is to extend and validate our current statistical model for accurate inference of local ancestry in archaic admixtures. The proposed model attempts to integrate a large number of patterns of genetic variation using the statistical framework of Conditional Random Fields (CRF). An important first example for the application of this model is the inference of Neandertal local ancestry in non-African populations. The inferred Neandertal ancestry will be leveraged for the second goal: to associate Neandertal variants with specific phenotypes. This goal will be pursued by analyzing a custom array designed to capture Neandertal-derived variants and by extending the CRF to infer Neandertal ancestry from SNP genotyping arrays rather than from next-generation sequencing. A complementary approach to study the action of natural selection on Neandertal variants, using a novel diffusion process-based statistical test, will be explored. Finally, the CR will be generalized to handle multiple ancestral populations as well as to the case where no reference genomes are available for the ancestral populations, and will be tested and validated on important examples for each case such as Denisovan admixture into Melanesian populations and sub-Saharan African populations that have evidence of unknown archaic ancestry. All of the methods and the results from this research will be made publicly available.

描述（由申请人提供）：过去十年中对人类遗传变异的研究表明，高度分化的人口群体（古老的混合物）之间的混合事件，如尼安德特人和非非洲人之间的混合事件，已经普遍发生，并可能对人类表型产生重大影响。例如，研究已经记录了其在单个基因座（如MHC基因座）分析中的表型影响。然而，缺乏足够的分析工具，阻碍了系统的了解表型的影响，古老的混合物。本K99/R 00研究提案建议开发和验证统计方法，以推断古混合物产生的遗传结构，并利用该结构来识别影响表型的古混合物引入的遗传变异。从应用这些方法中获得的见解不仅将使人们更全面地了解复杂表型背后的遗传因素，如常见疾病，而且还将确保目前服务不足的少数民族人口，其中许多人是混血事件或与欧洲人不同的祖先群体的后代，可以像研究欧洲血统的人群一样有效，并且可以从基因组医学的发现中受益。本建议的第一个目标是扩展和验证我们目前的统计模型，以准确推断当地的祖先在古代的混合物。所提出的模型试图整合大量的模式的遗传变异使用的统计框架的条件随机场（CRF）。应用该模型的第一个重要例子是推断非非洲人口中的尼安德特人的地方祖先。推断的尼安德特人血统将用于第二个目标：将尼安德特人变体与特定表型联系起来。这一目标将通过分析一个定制的阵列来实现，该阵列旨在捕获尼安德特人衍生的变体，并通过扩展CRF来从SNP基因分型阵列而不是从下一代测序中推断尼安德特人的祖先。一个互补的方法来研究自然选择对尼安德特人变体的作用，使用一种新的扩散过程为基础的统计检验，将进行探索。最后，CR将被推广到处理多个祖先群体以及祖先群体没有参考基因组的情况，并将在每个案例的重要例子上进行测试和验证，例如Denisovan混合到美拉尼西亚人群和撒哈拉以南非洲人群中，这些人群有未知的古代祖先的证据。这项研究的所有方法和结果都将公开提供。