System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish

PubMed: 42723105 DOI: 10.1186/s13073-026-01749-0

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

Genome Med 18(1)

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

Affiliations

  • Department of Molecular and Translational Medicine, Università degli Studi di Brescia, Brescia, Italy.
  • National Heart and Lung Institute, Imperial College London, London, UK.
  • Velsera Inc, Charlestown, MA, USA.
  • Department of Molecular and Translational Medicine, Università degli Studi di Brescia, Brescia, Italy.
  • National Heart and Lung Institute, Imperial College London, London, UK.
  • Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
  • MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
  • Aswan Heart Centre, Aswan, Egypt.
  • Meyer Children's Hospital, IRCCS, Florence, Italy.
  • Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Department of Biology and Medical Genetics, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
  • Imperial BRC Genomics Facility, Imperial College London, London, UK.
  • Department of Cardiology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
  • Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
  • SOD Diagnostica Genetica, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
  • Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  • Bristol Myers Squibb, Cambridge, MA, USA.
  • Cardiovascular and Genomics Research Institute, City St. George's University of London, London, UK.
  • Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
  • Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK.
  • Velsera Inc, Charlestown, MA, USA.
  • Meiosys Ltd, London, UK.

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.

MeSH

Genes, Genomics, Workflow, Genome, Work, Cardiomyopathies