Nishizaki Sierra S, Boyle Alan P
Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Trends Genet. 2017 Jan;33(1):34-45. doi: 10.1016/j.tig.2016.10.008. Epub 2016 Dec 6.
One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease. Genome-wide association studies (GWASs) bring us closer to this goal by linking variation with disease faster than ever before. Despite this, GWASs alone are unable to pinpoint disease-causing single nucleotide polymorphisms (SNPs). Noncoding SNPs, which represent the majority of GWAS SNPs, present a particular challenge. To address this challenge, an array of computational tools designed to prioritize and predict the function of noncoding GWAS SNPs have been developed. However, fewer than 40% of GWAS publications from 2015 utilized these tools. We discuss several leading methods for annotating noncoding variants and how they can be integrated into research pipelines in hopes that they will be broadly applied in future GWAS analyses.
遗传学研究的一个重要目标是了解基因变异如何导致表型差异和人类疾病。全基因组关联研究（GWAS）通过以前所未有的速度将变异与疾病联系起来，使我们离这个目标更近了一步。尽管如此，仅靠GWAS无法确定致病的单核苷酸多态性（SNP）。非编码SNP占GWAS SNP的大多数，带来了特别的挑战。为应对这一挑战，已开发出一系列旨在对非编码GWAS SNP的功能进行优先级排序和预测的计算工具。然而，2015年以来不到40%的GWAS出版物使用了这些工具。我们讨论了几种注释非编码变异的主要方法以及如何将它们整合到研究流程中，希望它们能在未来的GWAS分析中得到广泛应用。
