SG: Development of Improved Methods of Biogeography and Ancestry Identification

由于其复杂性和潜在的误解，使用基因组数据识别个体地理来源的能力构成了巨大的挑战。这种起源和最近的祖先的知识是人类学，社会学，法医学，个性化医疗和流行病学等多个领域的研究必不可少的，其中祖先是一个重要的变量。它还需要理解所有物种，包括人类，在一定程度上都是混合的，这些混合模式可以解开他们祖先的历史和起源。 随着全世界混血人口比例的增加，有必要更好地推断他们的家谱。目前的方法对欧洲人群的准确率不到50%，对非欧洲人的准确率非常高。该项目旨在解决这一短缺问题，并开发新颖、准确和有效的工具来研究混合血统的个体。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他们过去的答案的人产生巨大的影响。这项工作还将通过为植物和动物育种者提供选择工具来促进农业基因组学的发展。最近发表的第一代地理种群结构（GPS 1）算法，由PI开发，提供了地理预测，将全球83%的非混合个体放置在正确的原产国。该建议建立在GPS 1算法的成功基础上，以开发新的工具来预测混合个体中的神经网络。目前的目标是：（1）开发下一阶段的全球定位系统算法，该算法将能够高精度地预测一个人的父母、祖父母或更复杂的混合体的原籍国;（2）开发一种工具，沿着基因组推断当地祖先;（3）开发一个全球定位系统管道，以推断动植物的地理起源。现代计算方法，如遗传算法，模拟退火，和其他将用于实现最佳的精度和计算效率。 所有算法将在平台无关的语言R和Matlab中实现，并分别使用mpiR包和并行计算工具箱来实现并行处理。该项目得到了NSF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.