Variant effect predictors: a systematic review and practical guide - Cristian Riccio; Max L. Jansen; Linlin Guo; Andreas Ziegler (2024)
Abstract Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions. URL: https://cardio-care.shinyapps.io/VEP_Finder/ . Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct .