Local ancestry inference for complex admixtures

总结 所有人类都是不同历史来源人群的混合物。这种混合物已经发生 在一系列的时间尺度上，从最近的混合物，如洲际混合物， 从非洲裔美国人或西班牙裔美国人，到古老的混血儿，如与尼安德特人的混血儿， 发生在大约5万年前现代人类走出非洲的时候。 地方血统是个体染色体在染色体上每一点的起源群体。 基因组本地祖先对于许多应用程序是必不可少的，包括混合映射和 推断人口统计历史。地方祖先不是直接观察到的，而是必须推断出来的 从一个人的基因型数据中。 现有的方法推断当地祖先是不够的解开复杂的外加剂 在人类群体中。现有的方法在引用源数据时会遇到困难 人口数量有限或不匹配。这些方法无法处理基因 相似的祖先群体，不同的混合时间，或多个祖先 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 方法将利用最先进的单倍型频率模型和新的计算 方法，大大提高当地祖先的准确性和计算效率 推论我们的方法将克服目前的限制，灵活的混合物建模 次，通过启用本地祖先推断时，参考面板是从密切相关 人群，并通过在不存在混合数据时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、计算效率高的开源软件中实现， 对测序个体的大量样本进行分析。 我们将调用来自不同非洲人群的序列数据中的精细尺度本地祖先。我们将 使用当地的祖先电话来检测过去在非洲的迁移， 选择，并执行混合映射的广泛的特点，包括心脏， 肺和血液特征以及传染病状况。

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.