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Review
. 2018 Sep;137(9):665-678.
doi: 10.1007/s00439-018-1916-x. Epub 2018 Aug 2.

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Jochen Weile  1   2   3   4 , Frederick P Roth  5   6   7   8
Affiliations
Review

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Jochen Weile et al. Hum Genet. 2018 Sep.

Abstract

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.

Keywords: Deep mutational scanning; MAVE; VUS; Variant effect; Variants of uncertain significance.

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Conflict of interest statement

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Figures

Fig. 1
Fig. 1
Percentage of variants of uncertain significance (VUS) among missense allele Clinvar records over time from 1990 until 2017
Fig. 2
Fig. 2
Variant effects covered in MAVE studies. Top: the total number of variant effects covered in MAVE studies up to a given year. For 2018, the solid bar indicates the current state, while the dashed outline represents an extrapolation for the rest of the year. Bottom: the number of variant effects reported in individual studies, where colour indicates the study’s saturation of its respective target space
Fig. 3
Fig. 3
Generalized workflow of a typical MAVE experiment. Steps marked in gray are downstream computational procedures not found in every study, but contribute to the quality of the data. The proportions of studies using different mutagenesis, selection, and sequencing methods are broken down in pie charts. Colors serve to visually differentiate different categories but do not bear meaning
See this image and copyright information in PMC

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