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Genomic Insights into Human Population Mixture and its Role in Adaptation and Disease

对人类群体混合及其在适应和疾病中的作用的基因组见解

基本信息

批准号：
10819860
负责人：
Priya Moorjani
金额：
$ 11.36万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-03 至 2026-05-31
项目状态：
未结题

项目摘要

Project Summary Recent studies have shown that population mixture (or `admixture') is pervasive throughout human evolution and has played a major role in shaping human genetic and phenotypic variation. Despite the ubiquity and importance of population mixture, we still lack adequate methods to characterize the impact of admixture on a genomic scale and leverage this information for effective gene mapping. Addressing these topics is the central focus of research in my lab. In this proposal, our goal is to develop new methods to reconstruct fine-scale genomic ancestry in admixed groups and leverage this information to identify novel disease and adaptive mutations and genes. The application of these methods to large genomic surveys will help to discover novel disease and adaptive variants. The first step in characterizing the genomic impact of admixture is to infer the ancestry of each chromosomal segment, referred to as local ancestry. Towards this goal, we are developing new methods for local ancestry inference using machine-learning approaches that are ideally suited for classification problems and computationally tractable for large datasets. Our preliminary results show that our method is highly accurate and applicable across a range of demographic models. With reliable local ancestry inference, we will be well placed to study the impact of admixture on disease architecture and evolution of complex traits. We propose to use Admixture Mapping, a method to identify disease associations by leveraging ancestry differences across the genome, between cases and controls or among cases alone. By applying Admixture Mapping to complex admixed groups like South Asians and Latinxs, we aim to discover new population- specific disease associations and advance our understanding of disease architecture. Further, we will develop a novel method to leverage the demographic history of admixed groups to identify adaptive variants. By applying the method to study selection at various timescales in human evolution, we will uncover candidate genes and pathways related to adaptive gene flow and characterize its role in shaping human genetic variation. Finally, we will build reference-free ancestral genomes by recovering chromosomal segments of our lost ancestors hidden in admixed genomes. We will use these genomes to reconstruct the demographic history of our ancestors, as well as understand the fitness effects of population mixtures and the phenotypic legacy of our extinct ancestors. The successful completion of the proposed project will provide new statistical tools to leverage patterns of admixture to perform effective disease mapping and evolutionary inference in diverse, admixed groups. Application of these methods to large-scale genomic datasets will provide insights into the genetic, evolutionary, and functional impact of admixture during human evolution. Algorithms proposed here will be implemented in freely available software for use by other researchers.

项目摘要最近的研究表明，人口混合（或“混合”）在整个人类中是普遍存在的。进化，并在塑造人类遗传和表型变异中发挥了重要作用。尽管人口混合的普遍性和重要性，我们仍然缺乏足够的方法来描述影响并利用这些信息进行有效的基因定位。解决这些课题是我实验室的研究重点。在这项提案中，我们的目标是开发新的方法，在混合群体中重建精细尺度的基因组祖先，并利用这些信息来识别新的疾病和适应性突变和基因。这些方法在大型基因组调查中的应用将有助于发现新疾病和适应性变异。描述混合物的基因组影响的第一步是推断每种混合物的祖先染色体片段，称为本地祖先。为了实现这一目标，我们正在开发新的方法，使用机器学习方法进行本地祖先推断，这些方法非常适合分类问题并且对于大型数据集来说在计算上易于处理。我们的初步结果表明，我们的方法是高度准确并适用于一系列人口模型。通过可靠的本地祖先推断，我们将能够很好地研究混合物对疾病结构和复杂性状进化的影响。我们我建议使用混合映射，这是一种通过利用祖先来识别疾病关联的方法在整个基因组中，病例和对照之间或单独病例之间的差异。通过应用外加剂映射到复杂的混合群体，如南亚人和拉丁美洲人，我们的目标是发现新的人口- 具体的疾病协会和推进我们对疾病结构的理解。此外，我们将开发一种新的方法来利用混合群体的人口统计历史来识别适应性变体。通过应用这种方法来研究人类进化中不同时间尺度的选择，我们将发现候选人。适应性基因流相关的基因和途径，并描述其在塑造人类遗传变化量最后，我们将通过恢复染色体片段来构建无参考的祖先基因组。我们消失的祖先隐藏在混合的基因组中。我们将用这些基因组来重建我们祖先的历史，以及了解人口混合和表型的健身效果我们已经灭绝的祖先的遗产。拟议项目的成功完成将提供新的统计工具，在不同的混合群体中进行有效的疾病映射和进化推理。将这些方法应用于大规模基因组数据集将提供对遗传，在人类进化过程中混合物的进化和功能影响。这里提出的算法将是在免费软件中实现，供其他研究人员使用。