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, idiosyncrat...
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PeerJ Inc.
2021-07-01
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| Online Access: | https://peerj.com/articles/11774.pdf |
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| author | Hannah McConnell T. Daniel Andrews Matt A. Field |
| author_facet | Hannah McConnell T. Daniel Andrews Matt A. Field |
| author_sort | Hannah McConnell |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:25:47Z |
| format | Article |
| id | doaj.art-50179604b0514e178bbd9b32409b13cf |
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| language | English |
| last_indexed | 2024-03-09T06:25:47Z |
| publishDate | 2021-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-50179604b0514e178bbd9b32409b13cf2023-12-03T11:19:45ZengPeerJ Inc.PeerJ2167-83592021-07-019e1177410.7717/peerj.11774Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variantsHannah McConnell0T. Daniel Andrews1Matt A. Field2John Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaJohn Curtin School of Medical Research, Australian National University, Canberra, ACT, AustraliaAustralian Institute of Tropical Health and Medicine, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Smithfield, AustraliaBackground 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.https://peerj.com/articles/11774.pdfPharmacogeneticsPharmacogenomicsVariantOff-targetMissense mutationFunctional inference prediction |
| spellingShingle | Hannah McConnell T. Daniel Andrews Matt A. Field Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants PeerJ Pharmacogenetics Pharmacogenomics Variant Off-target Missense mutation Functional inference prediction |
| 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 | Pharmacogenetics Pharmacogenomics Variant Off-target Missense mutation Functional inference prediction |
| url | https://peerj.com/articles/11774.pdf |
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