等位基因特异性表达研究进展

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-04-28 DOI:10.1146/annurev-biodatasci-021621-122219
Siobhan Cleary, Cathal Seoighe
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引用次数: 12

摘要

二倍体对群体遗传学和遗传疾病的易感性具有深远的影响。尽管人类基因组中的大多数基因都有两个拷贝,但它们不一定都是活跃的,也不一定在一个特定的个体中处于相同的水平。基因组印记,导致有利于父系或母系等位基因的排他性或偏向性表达,现在被认为影响数百个人类基因。由于顺式作用的基因变异干扰了基因的表达,更多的基因表现出基因拷贝的不平等表达。应用于大量个体和组织类型的RNA测序产生的数据的可用性为评估遗传变异对基因表达中等位基因失衡的贡献提供了前所未有的机会。在这里,我们回顾了通过分析这些数据所获得的见解,包括基因表达不平衡的遗传贡献程度、基因表达不平衡的工具和统计模型,以及所获得的结果揭示了改变基因表达的遗传变异对复杂人类疾病和表型的贡献。
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Perspectives on Allele-Specific Expression.

Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.

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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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