人类疾病研究中的基因相互作用——证据越来越多。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Pankhuri Singhal, Shefali Setia Verma, Marylyn D Ritchie
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引用次数: 3

摘要

尽管分子技术取得了巨大的进步,可以大规模地生成基因组序列数据,但在大多数复杂疾病中,仍有相当大比例的遗传性仍未得到解释。由于许多发现都是单核苷酸变异,对疾病的影响小到中等,许多变异的功能含义仍然未知,因此,我们的新药物靶点和治疗方法有限。我们和其他许多人认为,限制我们从全基因组关联研究中识别新药物靶点的能力的一个主要因素可能是基因相互作用(上位性)、基因-环境相互作用、网络/途径效应或多组关系。我们认为,这些复杂的模型解释了复杂疾病的许多潜在遗传结构。在这篇综述中,我们讨论了来自多个研究途径的证据,从等位基因对到多组整合研究和药物基因组学,这些证据支持在人类疾病的遗传和基因组研究中进一步研究基因相互作用(或上位性)的必要性。我们的目标是对遗传研究中的上位性以及基因相互作用与人类健康和疾病之间的联系的越来越多的证据进行编目,这些证据可能使未来的精准医学成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gene Interactions in Human Disease Studies-Evidence Is Mounting.

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

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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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