Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08873, USA.
J Mol Biol. 2013 Nov 1;425(21):3993-4005. doi: 10.1016/j.jmb.2013.07.038. Epub 2013 Aug 5.
Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.
降低成本、提高测序速度和准确性，可以使基于基因组的个体疾病风险评估应用于临床。虽然过去的研究已经确定了许多可操作的突变，但大部分遗传风险仍然隐藏在序列数据中。基因组医学目前面临的最大挑战是开发新技术，以预测给定环境下每个人的变异组（基因组变异的全集）所编码的特定人类表型组（所有表达表型的集合）。存在许多用于计算识别单个变体的功能效应的工具。然而，充分利用全基因组、外显子组、转录组（和其他）序列的管道直到最近才成为现实。本综述着眼于构建用于预测“变异组”定义的疾病风险的方法学。它还讨论了将此类管道纳入日常医疗实践所面临的一些挑战。
