The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.
Hum Genet. 2018 Sep;137(9):665-678. doi: 10.1007/s00439-018-1916-x. Epub 2018 Aug 2.
Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.
鉴于基因组测序的成本和速度不断提高,我们有理由期待,很快就能了解数百万人的个人基因组。这与我们解读所发现的序列变异的有限能力形成鲜明对比。虽然解读编码区域的变异可能最简单,但绝大多数错义变异的功能影响我们都不得而知。虽然许多计算方法可以预测编码变异的影响,但在目前解读临床变异的指南中,它们的权重很小。实验室检测产生的结果相对更可信,但直到最近,它们还不能扩展到所有可能的突变空间。深度突变扫描和其他变异效应的多重检测方法的发展,使这一努力变得可行。在这里,我们回顾了过去十年在这一领域的进展,将不同的方法分解为其组成部分,并比较了方法学的差异。