Local ancestry inference for complex admixtures

复杂混合物的当地血统推断

• 批准号: 10080746
• 负责人: Sharon Browning
• 金额: $ 46.19万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-06 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10080746
• 关键词: Address; Admixture; Africa; African; African American; Blood; Chromosomes; Collaborations; Communicable Diseases; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Data Sources; Disease; Event; Frequencies; Genetic; Genome; Genotype; Haplotypes; Heart; Hispanics; Human; Individual; Learning; Lung; Methodology; Methods; Modeling; Modernization; Persons; Population; Recording of previous events; Resolution; Sample Size; Sampling; Source; Statistical Methods; Time; Trans-Omics for Precision Medicine; admixture mapping; cohort; computerized tools; diverse data; flexibility; improved; migration; open source; trait; user-friendly

Summary All humans are admixtures of various historical source populations. This admixture has occurred across a range of time-scales, from recent admixture such as the intercontinental admixture in African Americans or Hispanics, to ancient admixture such as the admixture with Neanderthals that occurred when modern humans migrated out of Africa around 50,000 years ago. Local ancestry is the population-of-origin of an individual’s chromosomes at each point in the genome. Local ancestry is essential for many applications, including admixture mapping and inferring demographic history. Local ancestry is not directly observed, but must be inferred from an individual’s genotype data. Existing methods for inferring local ancestry are inadequate for untangling complex admixtures in human populations. Existing methods struggle when reference data for the source populations are limited or poorly-matched. These methods are unable to handle genetically similar ancestral populations, divergent admixture times, or more than a few ancestral populations. We propose to develop new methods and computational tools to address these gaps. Our methods will utilize a state-of-the-art haplotype frequency model and new computational methods to greatly improve the accuracy and computational efficiency of local ancestry inference. Our methods will overcome current limitations by flexible modelling of admixture times, by enabling local ancestry inference when reference panels are from closely-related populations, and by creating new reference panels from admixed data when no existing reference population is well-matched to the ancestral population. Our methods will be implemented in user-friendly, computationally efficient, open-source software that scales to analysis of very large samples of sequenced individuals. We will call fine-scale local ancestry in sequence data from diverse African populations. We will use local ancestry calls to detect past migration within Africa, to detect post-admixture selection, and to perform admixture mapping for a broad spectrum of traits including heart, lung, and blood traits, and infectious disease status.

概括 所有人类都是不同历史来源人群的混合物。这种混合现象已经发生 跨越一系列时间尺度，从最近的混合，例如洲际混合 非裔美国人或西班牙裔，到古代的混合物，例如与尼安德特人的混合物 大约五万年前，现代人类迁出非洲时就发生了这种情况。 本地祖先是个体染色体在每个点的起源群体。 基因组。本地血统对于许多应用至关重要，包括混合物映射和 推断人口历史。当地血统无法直接观察到，但必须推断 来自个体的基因型数据。 现有的推断当地血统的方法不足以理清复杂的混合物 在人群中。当引用源数据时，现有方法会遇到困难 人口数量有限或不匹配。这些方法无法处理遗传问题 相似的祖先群体，不同的混合时间，或者多个祖先 人口。 我们建议开发新的方法和计算工具来解决这些差距。我们的 方法将利用最先进的单倍型频率模型和新的计算 大大提高当地血统的准确性和计算效率的方法 推理。我们的方法将通过灵活的混合物建模来克服当前的限制 次，当参考面板来自密切相关时，通过启用本地血统推断 人口，并通过在没有现有数据的情况下从混合数据创建新的参考面板 参考人群与祖先人群匹配良好。我们的方法将是 在用户友好、计算高效、开源软件中实现，可扩展至 分析非常大的已测序个体样本。 我们将把来自不同非洲人群的序列数据称为精细规模的当地血统。我们将 使用当地血统调用来检测过去在非洲境内的迁移，以检测后混合 选择，并对包括心脏、 肺、血液特征以及传染病状况。