---
OA_place: publisher
OA_type: hybrid
_id: '21503'
abstract:
- lang: eng
  text: Currently, pharmacogenetics relies on partially annotated star alleles, leaving
    novel variants and complex haplotypes uninterpretable. Computational scoring frameworks
    could overcome these limitations. Here, we comprehensively evaluated the ability
    of existing (CADD, FATHMM-XF, PROVEAN, MutationAssessor, SIFT, PhyloP100, APF,
    APF2) and novel (PharmGScore and PharmMLScore) variant effect predictors to assess
    pharmacogenetic alleles in multiple scenarios. Altogether we analyzed 541 PharmVar
    alleles, high‑throughput CYP2C9 and CYP2C19 mutational maps, and 200 642 UK Biobank
    exomes linked with health records containing antidepressant treatment outcomes.
    Many evaluated tools, especially ensemble frameworks, matched or exceeded star
    allele classifications (ROC‑AUC up to 0.85 for allele definitions, 0.95 in vitro;
    TPR up to 0.99 for exomes) and accurately predicted severe antidepressant adverse
    events for carriers of deleterious variants in CYP2C19 (OR 1.20–1.35). Our findings
    show that computational predictors deliver star allele accuracy while overcoming
    their limitations. With additional validation, computational tools could enhance
    clinical decision frameworks by enabling continuous scoring, incorporating previously
    unknown variants, and providing genome-wide applicability.
acknowledgement: "This research has been conducted using the UK Biobank Resource under
  Application Number 62979. We are grateful to the UK Biobank and all its voluntary
  participants. This work used data provided by patients and collected by the NHS
  as part of their care and support.\r\n\r\nThis study was funded by the National
  Science Center, Poland: PRELUDIUM BIS-3 grant no. 2021/43/O/NZ7/01187 (development
  and benchmarking of variant scores) and SONATINA 5 grant 2021/40/C/NZ2/00218 (UKB
  analyses). Additional support came from the statutory funds of the Maj Institute
  of Pharmacology PAS. We gratefully acknowledge Poland’s high-performance Infrastructure
  PLGrid ACK Cyfronet AGH, for providing computer facilities and support within computational
  grant no PLG/2022/015861. DMF and GEB were funded by NIH grants NIH R35GM152106
  and UM1HG011969."
article_number: '8'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jacek
  full_name: Hajto, Jacek
  last_name: Hajto
- first_name: Marcin
  full_name: Piechota, Marcin
  last_name: Piechota
- first_name: Ilse
  full_name: Krätschmer, Ilse
  id: 30d4014e-7753-11eb-b44b-db6d61112e73
  last_name: Krätschmer
  orcid: 0000-0002-5636-9259
- first_name: Paula
  full_name: Konowalska, Paula
  last_name: Konowalska
- first_name: Gabriel E.
  full_name: Boyle, Gabriel E.
  last_name: Boyle
- first_name: Douglas M.
  full_name: Fowler, Douglas M.
  last_name: Fowler
- first_name: Malgorzata
  full_name: Borczyk, Malgorzata
  last_name: Borczyk
- first_name: Michal
  full_name: Korostynski, Michal
  last_name: Korostynski
citation:
  ama: 'Hajto J, Piechota M, Krätschmer I, et al. Computational variant predictors
    for pharmacogenomics: From evaluation of single alleles to assessment of adverse
    drug reactions to antidepressants. <i>Pharmacogenomics Journal</i>. 2026;26(2).
    doi:<a href="https://doi.org/10.1038/s41397-026-00399-0">10.1038/s41397-026-00399-0</a>'
  apa: 'Hajto, J., Piechota, M., Krätschmer, I., Konowalska, P., Boyle, G. E., Fowler,
    D. M., … Korostynski, M. (2026). Computational variant predictors for pharmacogenomics:
    From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
    <i>Pharmacogenomics Journal</i>. Springer Nature. <a href="https://doi.org/10.1038/s41397-026-00399-0">https://doi.org/10.1038/s41397-026-00399-0</a>'
  chicago: 'Hajto, Jacek, Marcin Piechota, Ilse Krätschmer, Paula Konowalska, Gabriel
    E. Boyle, Douglas M. Fowler, Malgorzata Borczyk, and Michal Korostynski. “Computational
    Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to
    Assessment of Adverse Drug Reactions to Antidepressants.” <i>Pharmacogenomics
    Journal</i>. Springer Nature, 2026. <a href="https://doi.org/10.1038/s41397-026-00399-0">https://doi.org/10.1038/s41397-026-00399-0</a>.'
  ieee: 'J. Hajto <i>et al.</i>, “Computational variant predictors for pharmacogenomics:
    From evaluation of single alleles to assessment of adverse drug reactions to antidepressants,”
    <i>Pharmacogenomics Journal</i>, vol. 26, no. 2. Springer Nature, 2026.'
  ista: 'Hajto J, Piechota M, Krätschmer I, Konowalska P, Boyle GE, Fowler DM, Borczyk
    M, Korostynski M. 2026. Computational variant predictors for pharmacogenomics:
    From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
    Pharmacogenomics Journal. 26(2), 8.'
  mla: 'Hajto, Jacek, et al. “Computational Variant Predictors for Pharmacogenomics:
    From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.”
    <i>Pharmacogenomics Journal</i>, vol. 26, no. 2, 8, Springer Nature, 2026, doi:<a
    href="https://doi.org/10.1038/s41397-026-00399-0">10.1038/s41397-026-00399-0</a>.'
  short: J. Hajto, M. Piechota, I. Krätschmer, P. Konowalska, G.E. Boyle, D.M. Fowler,
    M. Borczyk, M. Korostynski, Pharmacogenomics Journal 26 (2026).
date_created: 2026-03-29T22:07:08Z
date_published: 2026-03-09T00:00:00Z
date_updated: 2026-03-30T07:10:50Z
day: '09'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41397-026-00399-0
external_id:
  pmid:
  - '41803106'
file:
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  creator: dernst
  date_created: 2026-03-30T07:04:08Z
  date_updated: 2026-03-30T07:04:08Z
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file_date_updated: 2026-03-30T07:04:08Z
fulldoi: https://doi.org/10.1038/s41397-026-00399-0
has_accepted_license: '1'
intvolume: '        26'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Pharmacogenomics Journal
publication_identifier:
  eissn:
  - 1473-1150
  issn:
  - ' 1470-269X'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Computational variant predictors for pharmacogenomics: From evaluation of
  single alleles to assessment of adverse drug reactions to antidepressants'
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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  short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2026'
...