Cargando…
Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants
BACKGROUND: Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncra...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286708/ https://www.ncbi.nlm.nih.gov/pubmed/34316407 http://dx.doi.org/10.7717/peerj.11774 |
_version_ | 1783723769188057088 |
---|---|
author | McConnell, Hannah Andrews, T. Daniel Field, Matt A. |
author_facet | McConnell, Hannah Andrews, T. Daniel Field, Matt A. |
author_sort | McConnell, Hannah |
collection | PubMed |
description | BACKGROUND: Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. METHODS: Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. RESULTS: As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. CONCLUSION: In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice. |
format | Online Article Text |
id | pubmed-8286708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82867082021-07-26 Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants McConnell, Hannah Andrews, T. Daniel Field, Matt A. PeerJ Bioinformatics BACKGROUND: Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. METHODS: Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. RESULTS: As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. CONCLUSION: In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice. PeerJ Inc. 2021-07-15 /pmc/articles/PMC8286708/ /pubmed/34316407 http://dx.doi.org/10.7717/peerj.11774 Text en © 2021 McConnell et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics McConnell, Hannah Andrews, T. Daniel Field, Matt A. Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title | Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title_full | Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title_fullStr | Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title_full_unstemmed | Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title_short | Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
title_sort | efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286708/ https://www.ncbi.nlm.nih.gov/pubmed/34316407 http://dx.doi.org/10.7717/peerj.11774 |
work_keys_str_mv | AT mcconnellhannah efficacyofcomputationalpredictionsofthefunctionaleffectofidiosyncraticpharmacogeneticvariants AT andrewstdaniel efficacyofcomputationalpredictionsofthefunctionaleffectofidiosyncraticpharmacogeneticvariants AT fieldmatta efficacyofcomputationalpredictionsofthefunctionaleffectofidiosyncraticpharmacogeneticvariants |