Statistical methods to infer structure and impact of ancient admixture

描述（由申请人提供）：过去十年对人类遗传变异的研究表明，高度分化的人口群体之间的混合事件（古代混合），如尼安德特人和非非洲人之间的混合，已经是一种常见的现象，并且可能对人类表型产生重大影响。例如，研究已经记录了它在个体基因座分析中的表型影响，如MHC基因座。然而，缺乏足够的分析工具阻碍了对古代混合物的表型影响的系统理解。本K99/R00研究计划建议开发和验证统计方法，以推断由古代混合物产生的遗传结构，并利用该结构识别由古代混合物引入的影响表型的遗传变异。从这些方法的应用中获得的见解不仅将使人们更全面地了解复杂表型（如常见疾病）背后的遗传因素，而且还将确保目前服务不足的少数民族人口（其中许多人来自混合事件或来自与欧洲人不同的祖先群体）可以像欧洲人后裔一样得到有效的研究，并可以从基因组医学的发现中受益。本提案的第一个目标是扩展和验证我们目前的统计模型，以准确推断古代混合物中的当地祖先。该模型试图利用条件随机场（CRF）的统计框架整合大量的遗传变异模式。应用该模型的一个重要的第一个例子是对尼安德特人在非非洲人口中的本地祖先的推断。推断出的尼安德特人祖先将被用于第二个目标：将尼安德特人的变异与特定的表型联系起来。这一目标将通过分析定制阵列来实现，该阵列旨在捕获尼安德特人衍生的变体，并通过扩展CRF，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。将探索一种互补的方法来研究自然选择对尼安德特人变体的作用，使用一种新的基于扩散过程的统计检验。最后，CR将被推广到处理多个祖先种群以及没有参考基因组可用于祖先种群的情况，并将在每个案例的重要实例上进行测试和验证，例如丹尼索瓦人与美拉尼西亚种群的混合，以及有未知古代祖先证据的撒哈拉以南非洲种群。这项研究的所有方法和结果都将公开。

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.