Computational and Experimental Analysis of Genetic Variants.

IF 4.2 2区 医学 Q1 PHYSIOLOGY Comprehensive Physiology Pub Date : 2022-03-29 DOI:10.1002/cphy.c210012
Jeremy W Prokop, Vladislav Jdanov, Lane Savage, Michele Morris, Neil Lamb, Elizabeth VanSickle, Cynthia L Stenger, Surender Rajasekaran, Caleb P Bupp
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引用次数: 4

Abstract

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

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遗传变异的计算与实验分析。
基因组学在过去十年中呈指数级增长。通过全基因组关联研究(GWAS)和定量追踪位点(QTL)等统计策略,常见变异与生理变化相关。通过广泛的过滤工具,包括群体基因组学和基于三人的测序(父母和先证者),罕见变异与疾病相关。然而,基因组关联需要后续分析,以缩小因果变异,识别受影响的基因,并确定生理变化。存在大量数据,可用于将变异与基因变化、细胞类型、蛋白质途径、临床表型和建立生理基因组学的动物模型联系起来。这些数据与生物信息学相结合,包括进化分析、结构洞察和基因调控,可以为基因组变异的机制产生可测试的假设。分子生物学、生物化学、细胞培养、CRISPR编辑和动物模型可以验证这些假设,从而给出分子变异机制。在学习独立研究假设设计的同时,通过教授遗传学的基本概念和术语,变体特征可以成为教育本科生、研究生或医学培训项目中未来专业人员的重要组成部分。本文介绍了变异表征的计算和实验分析策略,并提供了这些工具在出版物中应用的示例。©2022美国生理学会。中国生物医学工程学报(英文版),2012。
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
10.50
自引率
0.00%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Comprehensive Physiology is the most authoritative and comprehensive collection of physiology information ever assembled, and uses the most powerful features of review journals and electronic reference works to cover the latest key developments in the field, through the most authoritative articles on the subjects covered. This makes Comprehensive Physiology a valued reference work on the evolving science of physiology for both researchers and clinicians. It also provides a useful teaching tool for instructors and an informative resource for medical students and other students in the life and health sciences.
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