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Article

Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling

by
Artem Podvalnyi
1,2,
Arina Kopernik
1,
Mariia Sayganova
1,
Mary Woroncow
3,
Gauhar Zobkova
4,
Anna Smirnova
4,
Anton Esibov
1,
Andrey Deviatkin
1,
Pavel Volchkov
1,3,* and
Eugene Albert
1,3,*
1
Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, 125315 Moscow, Russia
2
Faculty of Computer Science, HSE University, 101000 Moscow, Russia
3
Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
4
Evogen LLC, 115191 Moscow, Russia
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(1), 363; https://doi.org/10.3390/ijms26010363
Submission received: 2 December 2024 / Revised: 30 December 2024 / Accepted: 2 January 2025 / Published: 3 January 2025
(This article belongs to the Special Issue New Breakthroughs in Molecular Diagnostic Tools for Human Diseases)
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Abstract

A pseudogene is a non-functional copy of a protein-coding gene. Processed pseudogenes, which are created by the reverse transcription of mRNA and subsequent integration of the resulting cDNA into the genome, being a major pseudogene class, represent a significant challenge in genome analysis due to their high sequence similarity to the parent genes and their frequent absence in the reference genome. This homology can lead to errors in variant identification, as sequences derived from processed pseudogenes can be incorrectly assigned to parental genes, complicating correct variant calling. In this study, we quantified the occurrence of variant calling errors associated with pseudogenes, generated by the most popular germline variant callers, namely GATK-HC, DRAGEN, and DeepVariant, when analysing 30x human whole-genome sequencing data (n = 13,307). The results show that the presence of pseudogenes can interfere with variant calling, leading to false positive identifications of potentially clinically relevant variants. Compared to other approaches, DeepVariant was the most effective in correcting these errors.
Keywords: processed pseudogenes; SNPs; ACMG processed pseudogenes; SNPs; ACMG

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MDPI and ACS Style

Podvalnyi, A.; Kopernik, A.; Sayganova, M.; Woroncow, M.; Zobkova, G.; Smirnova, A.; Esibov, A.; Deviatkin, A.; Volchkov, P.; Albert, E. Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling. Int. J. Mol. Sci. 2025, 26, 363. https://doi.org/10.3390/ijms26010363

AMA Style

Podvalnyi A, Kopernik A, Sayganova M, Woroncow M, Zobkova G, Smirnova A, Esibov A, Deviatkin A, Volchkov P, Albert E. Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling. International Journal of Molecular Sciences. 2025; 26(1):363. https://doi.org/10.3390/ijms26010363

Chicago/Turabian Style

Podvalnyi, Artem, Arina Kopernik, Mariia Sayganova, Mary Woroncow, Gauhar Zobkova, Anna Smirnova, Anton Esibov, Andrey Deviatkin, Pavel Volchkov, and Eugene Albert. 2025. "Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling" International Journal of Molecular Sciences 26, no. 1: 363. https://doi.org/10.3390/ijms26010363

APA Style

Podvalnyi, A., Kopernik, A., Sayganova, M., Woroncow, M., Zobkova, G., Smirnova, A., Esibov, A., Deviatkin, A., Volchkov, P., & Albert, E. (2025). Quantitative Analysis of Pseudogene-Associated Errors During Germline Variant Calling. International Journal of Molecular Sciences, 26(1), 363. https://doi.org/10.3390/ijms26010363

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