大规模个体近世祖先起源的估计

Ross E. Curtis, A. Girshick
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引用次数: 2

摘要

过去十年,直接面向消费者的基因组学呈指数级增长。这些测试的一个流行特点是报告遥远的祖先推断概况——对世界上考生祖先可能生活过的地区进行分类。虽然当前的方法和产品通常关注更遥远的过去(例如,数千年前),但我们最近证明,通过利用网络分析工具,如社区检测,可以识别更近的祖先。然而,在可能有数百万个节点的大型网络上使用社区检测之类的网络分析工具,在每天处理数百或数千个新基因型的实时生产环境中是不可用的。在这项研究中,我们描述了一种分类方法,该方法利用网络特征将个体分配到与最近祖先相对应的大型网络中的社区。我们最近在AncestryDNA推出了这项研究的测试版,作为一项新产品功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Recent Ancestral Origins of Individuals on a Large Scale
The last ten years have seen an exponential growth of direct-to-consumer genomics. One popular feature of these tests is the report of a distant ancestral inference profile-a breakdown of the regions of the world where the test-taker's ancestors may have lived. While current methods and products generally focus on the more distant past (e.g., thousands of years ago), we have recently demonstrated that by leveraging network analysis tools such as community detection, more recent ancestry can be identified. However, using a network analysis tool like community detection on a large network with potentially millions of nodes is not feasible in a live production environment where hundreds or thousands of new genotypes are processed every day. In this study, we describe a classification method that leverages network features to assign individuals to communities in a large network corresponding to recent ancestry. We recently launched a beta version of this research as a new product feature at AncestryDNA.
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