实现效应基因预测全基因组关联研究的前景

IF 31.7 1区 生物学 Q1 GENETICS & HEREDITY
Maria C. Costanzo, Laura W. Harris, Yue Ji, Aoife McMahon, Noël P. Burtt, Jason Flannick
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引用次数: 0

摘要

全基因组关联研究(GWAS)确定基因组中遗传变异与复杂疾病(如糖尿病)的风险或特征(如血压)的大小相关的区域。确定哪些“效应基因”介导了GWAS关联的作用,对于利用GWAS了解疾病机制和开发新疗法至关重要。近年来,GWAS作者越来越多地将效应基因预测作为其研究结果的一部分。然而,研究界尚未就产生或报告这些预测的标准达成一致。在这一观点中,我们说明了用于支持效应基因预测的证据类型的多样性,并提出了未来增加其可及性和有用性的倡议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Realizing the promise of genome-wide association studies for effector gene prediction

Realizing the promise of genome-wide association studies for effector gene prediction

Genome-wide association studies (GWAS) identify regions of the genome in which genetic variation is associated with the risk of complex diseases, such as diabetes, or the magnitude of traits, such as blood pressure. Determining which ‘effector genes’ mediate the effects of GWAS associations is essential to using GWAS to understand disease mechanisms and develop new therapies. In recent years, GWAS authors have increasingly included effector gene predictions as part of their study results. However, the research community has not yet converged on standards for generating or reporting these predictions. In this Perspective, we illustrate the diversity of the evidence types used to support effector gene predictions and argue for future initiatives to increase their accessibility and usefulness.

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来源期刊
Nature genetics
Nature genetics 生物-遗传学
CiteScore
43.00
自引率
2.60%
发文量
241
审稿时长
3 months
期刊介绍: Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation. Integrative genetic topics comprise, but are not limited to: -Genes in the pathology of human disease -Molecular analysis of simple and complex genetic traits -Cancer genetics -Agricultural genomics -Developmental genetics -Regulatory variation in gene expression -Strategies and technologies for extracting function from genomic data -Pharmacological genomics -Genome evolution
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