Strategies for the Genomic Analysis of Admixed Populations.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Taotao Tan, Elizabeth G Atkinson
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引用次数: 0

Abstract

Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.

混血人群基因组分析策略。
混血人群占全球人类遗传多样性的很大一部分,但他们往往被排除在基因组学分析之外。这种排斥是有问题的,因为它导致了对不同人群遗传结构和历史的理解以及跨人群基因组医学表现的差异。混血人群尤其面临统计方面的挑战,因为他们继承了来自多个来源人群的基因组片段--这也是他们历来被排除在基因研究之外的主要原因。然而,近年来,越来越多的统计方法和软件工具被开发出来,以在基因组学分析中考虑和利用混杂性。在此,我们将对此类计算策略进行调查,以便在大规模基因组学研究中对混血人群进行充分校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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