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

U. Sezerman, T. Bozkurt, F. S. Isleyen
{"title":"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","authors":"U. Sezerman, T. Bozkurt, F. S. Isleyen","doi":"10.5772/intechopen.92738","DOIUrl":null,"url":null,"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.","PeriodicalId":18460,"journal":{"name":"Methods in molecular medicine","volume":"234 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating Evolutionary Genetics to Medical Genomics: Evolutionary Approaches to Investigate Disease-Causing Variants\",\"authors\":\"U. Sezerman, T. Bozkurt, F. S. Isleyen\",\"doi\":\"10.5772/intechopen.92738\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":18460,\"journal\":{\"name\":\"Methods in molecular medicine\",\"volume\":\"234 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Methods in molecular medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5772/intechopen.92738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in molecular medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/intechopen.92738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Integrating Evolutionary Genetics to Medical Genomics: Evolutionary Approaches to Investigate Disease-Causing Variants
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Integrating Evolutionary Genetics to Medical Genomics: Evolutionary Approaches to Investigate Disease-Causing Variants Clinical Validation of a Whole Exome Sequencing Pipeline New Perspectives in Personalization of Therapy for Hematological Cancers Molecular Medicines for Parasitic Diseases Landscape Genetics: From Classic Molecular Markers to Genomics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1