Integrating Evolutionary Genetics to Medical Genomics: Evolutionary Approaches to Investigate Disease-Causing Variants

U. Sezerman, T. Bozkurt, F. S. Isleyen
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Abstract

In recent years, next-generation sequencing (NGS) platforms that facilitate generation of a vast amount of genomic variation data have become widely used for diagnostic purposes in medicine. However, identifying the potential effects of the variations and their association with a particular disease phenotype is the main challenge in this field. Several strategies are used to discover the causative mutations among hundreds of variants of uncertain significance. Incorporating information from healthy population databases, other organisms’ databases, and computational prediction tools are evolution-based strategies that give valuable insight to interpret the variant pathogenicity. In this chapter, we first provide an overview of NGS analysis workflow. Then, we review how evolutionary principles can be integrated into the prioritization schemes of analyzed variants. Finally, we present an example of a real-life case where the use of evolutionary genetics information facilitated the discovery of disease-causing variants in medical genomics.
整合进化遗传学到医学基因组学:研究致病变异的进化方法
近年来,下一代测序(NGS)平台促进了大量基因组变异数据的生成,已广泛用于医学诊断目的。然而,确定变异的潜在影响及其与特定疾病表型的关联是该领域的主要挑战。在数百个意义不确定的变异中,使用了几种策略来发现致病突变。结合来自健康人群数据库、其他生物数据库和计算预测工具的信息是基于进化的策略,为解释变异致病性提供了有价值的见解。在本章中,我们首先概述了NGS分析工作流程。然后,我们回顾了如何将进化原理集成到分析变量的优先级方案中。最后,我们提出了一个现实生活中的例子,其中使用进化遗传学信息促进了医学基因组学中致病变异的发现。
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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