Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies

Abstract Background Gene panels represent a widely used strategy for genetic testing in a vast range of Mendelian disorders. While this approach aids reliable bioinformatic detection of short coding variants, it often fails to detect many larger variants. Recent studies have recommended the adoption of pangenome references (as opposed to linear reference genomes like GRCh38) to augment detection of large variants from targeted sequencing, potentially providing diagnostic laboratories with the possibility to streamline diagnostic work-ups and reduce costs. Methods Here, we analyze 1969 cardiomyopathy cases and 1805 controls sequenced with the Illumina Trusight Cardio panel using a pangenome-based workflow (GRAF) and five conventional orthogonal methodologies (GATK HaplotypeCaller, GATK-gCNV, ExomeDepth, Manta and Lumpy-SV) to detect variants ≥ 20 bp in size. Results Following lab-based variant validation by means of PCR and Sanger sequencing, we show that GRAF conjugates higher precision and recall (F1 score 0.86) compared with other methods (F1 0-0.57) in detecting potentially pathogenic variants ≥ 20 bp from short-read panel data. Results were complemented by a comparison of the tools’ performance in detecting ground truth variants on reference sample HG002 from Genome In A Bottle, which confirmed GRAF to outperform other tools also on exome sequencing (F1 0.97 vs. 0-0.94). Notably, in the HG002 benchmark dataset, GRAF also showed slightly improved performance compared to GATK HaplotypeCaller in the identification of small variants (1–19 bp; F1 0.975 vs. 0.968). Conclusions Our results indicate that pangenome-based workflows aid improved detection of large variants from targeted sequencing data in the clinical context and suggest that they may contribute to more unified variant detection frameworks for all-size genetic variants in the future.

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Journal
Genome Medicine
Published
2026-08-26
DOI
https://doi.org/10.1186/s13073-026-01749-0
Primary Topic
Genomics and Rare Diseases
Type
article
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article

Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies

Marco Ritelli, Amit Jain, Sarah Halawa, Deniz Turgut et al.
Genome Medicine
Genomics and Rare Diseases
article

Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies

Marco Ritelli, Amit Jain, Sarah Halawa, Deniz Turgut, Valeria Bertini, Francesca Girolami, Elisabetta Pelo, Paul J.R. Barton, Iacopo Olivotto, Massimo Gennarelli, Alessia Azzu, Rachel Buchan, Jiří Bonaventura, Roddy Walsh, Marina Colombi, Güngör Budak, Özem Kalay, Nik Matthews, Francesco Mazzarotto, Petra Peldová, James S Ware, Elif Arslan, Mona Allouba, Dudley J. Pennell, Yasmine Aguib, H. Serhat Tetikol, Esmé Cavanagh, Milan Macek, Alaa Afify, Magdi Yacoub, Pantazis Theotokis, Valeria Cinquina
article en

Abstract

Abstract Background Gene panels represent a widely used strategy for genetic testing in a vast range of Mendelian disorders. While this approach aids reliable bioinformatic detection of short coding variants, it often fails to detect many larger variants. Recent studies have recommended the adoption of pangenome references (as opposed to linear reference genomes like GRCh38) to augment detection of large variants from targeted sequencing, potentially providing diagnostic laboratories with the possibility to streamline diagnostic work-ups and reduce costs. Methods Here, we analyze 1969 cardiomyopathy cases and 1805 controls sequenced with the Illumina Trusight Cardio panel using a pangenome-based workflow (GRAF) and five conventional orthogonal methodologies (GATK HaplotypeCaller, GATK-gCNV, ExomeDepth, Manta and Lumpy-SV) to detect variants ≥ 20 bp in size. Results Following lab-based variant validation by means of PCR and Sanger sequencing, we show that GRAF conjugates higher precision and recall (F1 score 0.86) compared with other methods (F1 0-0.57) in detecting potentially pathogenic variants ≥ 20 bp from short-read panel data. Results were complemented by a comparison of the tools’ performance in detecting ground truth variants on reference sample HG002 from Genome In A Bottle, which confirmed GRAF to outperform other tools also on exome sequencing (F1 0.97 vs. 0-0.94). Notably, in the HG002 benchmark dataset, GRAF also showed slightly improved performance compared to GATK HaplotypeCaller in the identification of small variants (1–19 bp; F1 0.975 vs. 0.968). Conclusions Our results indicate that pangenome-based workflows aid improved detection of large variants from targeted sequencing data in the clinical context and suggest that they may contribute to more unified variant detection frameworks for all-size genetic variants in the future.

Genome Medicine
Imperial College Healthcare NHS Trust (GB), St George's, University of London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), Charles University (CZ), Hammersmith Hospital (GB), Harefield Hospital (GB), Bristol-Myers Squibb (United States) (US), Royal Brompton Hospital (GB), University Hospital in Motol (CZ), NIHR Imperial Biomedical Research Centre (GB), Meyer Children's Hospital (IT), Centro San Giovanni di Dio Fatebenefratelli (IT), Azienda Ospedaliero-Universitaria Careggi (IT), Genomics England (GB), Genomics (United Kingdom) (GB), Amsterdam University Medical Centers (NL), Istituti di Ricovero e Cura a Carattere Scientifico (IT), MRC London Institute of Medical Sciences (GB), University of Florence (IT), Imperial College London (GB), University of Brescia (IT), Aswan University (EG), University of Amsterdam (NL)
British Heart Foundation, Sir Jules Thorn Charitable Trust, Ministerstvo Zdravotnictví Ceské Republiky, Medical Research Council
Openalex Percentile: Top 10%
Genomics and Rare Diseases
DOIOpen Access PDFOpenAlex
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