SG: Development of Improved Methods of Biogeography and Ancestry Identification

由于基因组数据的复杂性和潜在的误解，利用基因组数据识别个人地理起源的能力构成了巨大的挑战。了解这一起源和最近的祖先对于人类学、社会学、法医学、个性化医学和流行病学等多个领域的研究至关重要，而祖先是其中的一个重要变量。它还需要理解，包括人类在内的所有物种在一定程度上是混合的，这些混合模式可以解开它们祖先的历史和起源。随着世界各地混血个体比例的增加，有必要更好地推断他们的生物地理分布。目前的方法对欧洲人来说不到50%的准确率，对非欧洲人来说非常不准确。该项目旨在解决这一不足，并开发新的、准确和有效的工具来研究混血儿。它们将对试图在疾病研究中匹配病例和对照的从业者、研究生物多样性和人类、动物和植物起源的遗传学家，以及许多寻求关于过去的答案的人产生巨大的影响。这项工作还将通过为动植物育种者提供选择工具来促进农业基因组学的进步。最近发布的第一代地理种群结构(GPS1)算法，由PI开发，提供了生物地理预测，将全球83%的非混血个体置于正确的原籍国。这项建议建立在GPS1算法的成功基础上，以开发新的工具来预测混合个体的生物地理。目前的目标是：(1)开发下一阶段的全球定位系统算法，它将能够高精度地预测一个人的父母、祖父母或更复杂的混血儿的原籍国；(2)开发一种沿基因组推断当地祖先的工具；(3)开发一条全球定位系统管道，以推断动植物的生物地理起源。将使用现代计算方法，如遗传算法、模拟退火法和其他方法，以实现最佳的精度和计算效率。所有算法都将用与平台无关的语言R和MatLab实现，并分别使用mpiR R包和并行计算工具箱来实现并行处理。该项目得到了美国国家科学基金会进化过程和生物人类学项目的支持。

The ability to identify the geographic origin of an individual using genomic data poses a great challenge due to its complexity and potential misinterpretations. Knowledge of this origin and recent ancestry are essential for research in multiple fields such as anthropology, sociology, forensics, personalized medicine and epidemiology, in which ancestry is an important variable. It also requires understanding that all species, including humans, are mixed to certain degrees and that these mixture patterns can unlock the history and origin of their ancestors. As the proportion of mixed-ancestry individuals increases worldwide, there is a need to better infer their biogeography. Current methods are less than 50% accurate for European populations and highly inaccurate for non-Europeans. This project aims to address this shortage and to develop novel, accurate and efficient tools to study individuals of mixed origin. They will have vast implications for practitioners trying to match cases and controls in disease studies, geneticists studying biodiversity and origins of humans, animals, and plants, as well as many people seeking answers about their past. This work will also contribute to advancement in agricultural genomics by providing selection tools for plant and animal breeders.Recently published first-generation Geographic Population Structure (GPS1) algorithm, developed by the PIs, provided biogeographical predictions that placed 83% of worldwide non-admixed individuals in their correct country of origin. This proposal builds on the success of the GPS1 algorithm to develop new tools for predicting biogeography in mixed individuals. The current aims are: (1) Development of the next phase of GPS algorithms, which will be capable of predicting the countries of origin of an individual's parents, grandparents or a more complex mixture with high accuracy; (2) Development of a tool to infer local ancestry along the genome; (3) Development of a GPS pipeline to infer the biogeographic origin of plants and animals. Modern computational approaches, such as genetic algorithms, simulated annealing, and others will be used to achieve optimal accuracy and computational efficiency. All algorithms will be implemented in the platform-independent languages R and Matlab and use the mpiR R package and parallel computing toolbox, respectively, to enable parallel processing. This project is supported by the Evolutionary Processes and Biological Anthropology programs at NSF.