Prioritizing genes for systematic variant effect mapping

Abstract

Motivation: When rare missense variants are clinically interpreted as to their pathogenicity, most are classified as variants of uncertain significance (VUS). Although functional assays can provide strong evidence for variant classification, such results are generally unavailable. Multiplexed assays of variant effect can generate experimental 'variant effect maps' that score nearly all possible missense variants in selected protein targets for their impact on protein function. However, these efforts have not always prioritized proteins for which variant effect maps would have the greatest impact on clinical variant interpretation.

Results: Here, we mined databases of clinically interpreted variants and applied three strategies, each building on the previous, to prioritize genes for systematic functional testing of missense variation. The strategies ranked genes (i) by the number of unique missense VUS that had been reported to ClinVar; (ii) by movability- and reappearance-weighted impact scores, to give extra weight to reappearing, movable VUS and (iii) by difficulty-adjusted impact scores, to account for the more resource-intensive nature of generating variant effect maps for longer genes. Our results could be used to guide systematic functional testing of missense variation toward greater impact on clinical variant interpretation.

Availability and implementation: Source code available at: https://github.com/rothlab/mave-gene-prioritization.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Three strategies for ranking genes according to the potential impact of variant effect map on clinical interpretation of VUS. Missense VUS collected through clinical testing were extracted from the ClinVar and Invitae databases. The first strategy ranked genes based on their unique VUS count. The second strategy ranked genes based on their MARWIS to give extra weight to reappearing, movable VUS. The third strategy ranked the genes by their DAIS, calculated to account for the costs associated with studying longer genes

Fig. 2.

Correlation between MARWIS calculated from two datasets. The MARWIS calculated using unique missense VUS from ClinVar ($S_{\mathrm{MARWIS}}\left(C_{\mathrm{ClinVar}}\right)$) correlated well (r = 0.900) with the MARWIS calculated using unique missense VUS from the Invitae dataset for 1921 genes ($S_{\mathrm{MARWIS}}\left(C_{\mathrm{Invitae}}\right)$). The blue line shows the reduced major axis regression of the dataset

Fig. 3.

Correlation between unique VUS count and MARWIS. The unique VUS count correlated well (r = 0.919) with the MARWIS. Some genes, including RYR1, NF1, MYH7, ATM and BRCA2, exhibited more frequently reappearing and movable VUS than the group average, whereas other genes (e.g., TTN and APC) showed fewer. The blue line shows the linear regression of the data