Skip to content
DraconiticResearch
Search Sources API
190,869 studies31 sourcesUpdated 8 min ago
Peer-reviewedCC BY-NC-NDNature Medicine

Automated reanalysis of genomic data for rare disease diagnostics at scale

  • Matthew J. Welland1,2
  • K. D. Ahlquist3,4
  • Paul De Fazio5
  • Christina Anne Austin-Tse3,4
  • Lynn S. Pais4
  • Laura Wedd1,2
  • Samantha Bryen1,2
  • Rocío Rius1,2
  • Michael Franklin1,2
  • Caitlin Morrison1,2
  • Giles Hall6
  • Laura Gauthier3,6
25 more authors
  • Alex Bloemendal6,4
  • David I. Francis5
  • Andrew John Mallett7,8,9
  • Amali C. Mallawaarachchi10,11
  • Paul J. Lockhart12,13
  • Richard Jacob Leventer12,14,13
  • Ingrid Eileen Scheffer15,14,13
  • Katherine B. Howell12,14,13
  • Karin S. Kassahn16,17
  • Hamish S. Scott16,18,17,19
  • Julie M. McGaughran20,21
  • John Christodoulou12,13
  • David Ross Thorburn12,13,5
  • Bryony A. Thompson22
  • Chirag Patel21
  • Greg Smith23
  • Anne O’Donnell‐Luria3,24,4
  • Simon Sadedin12,5
  • Heidi L. Rehm3,4
  • Sebastian Lunke12,13,5
  • Jeremiah D. Wander23
  • Kaitlin E. Samocha3,4
  • Cas Simons1,2
  • Daniel G. MacArthur1,2
  • Zornitza Stark25,26,27
27 affiliations
  1. 1Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia
  2. 2Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
  3. 3Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
  4. 4Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  5. 5Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
  6. 6Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  7. 7College of Medicine and Dentistry, James Cook University, Townsville, Queensland, Australia
  8. 8Institute for Molecular Bioscience and Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  9. 9Townsville Hospital and Health Service, Townsville, Queensland, Australia
  10. 10Clinical Genetics Service, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
  11. 11Garvan Institute of Medical Research, Sydney, New South Wales, Australia
  12. 12Murdoch Children's Research Institute, Melbourne, Victoria, Australia
  13. 13University of Melbourne, Melbourne, Victoria, Australia
  14. 14Royal Children's Hospital, Melbourne, Victoria, Australia
  15. 15Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
  16. 16Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
  17. 17Department of Genetics and Molecular Pathology, SA Pathology, Adelaide, South Australia, Australia
  18. 18Centre for Cancer Biology, an alliance between SA Pathology and the University of South Australia, Adelaide, South Australia, Australia
  19. 19UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
  20. 20Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  21. 21Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
  22. 22Department of Pathology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
  23. 23Microsoft Research, Redmond, WA, USA
  24. 24Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA
  25. 25Australian Genomics, Melbourne, Victoria, Australia. [email protected]
  26. 26University of Melbourne, Melbourne, Victoria, Australia. [email protected]
  27. 27Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Victoria, Australia. [email protected]
Open at sourcePDFDOICite

Abstract

Reanalysis of genomic data in rare disease is highly effective in increasing diagnostic yields but remains limited by manual approaches. Automation and optimization for high specificity will be necessary to ensure scalability, adoption and sustainability of iterative reanalysis. We developed Talos, an open-source tool that automates variant prioritization by integrating dynamically updated gene-disease and variant-level evidence with inheritance-aware filtering and validated its performance using data from 1,089 individuals with rare disease. Trio-based analysis identified 90% of known diagnoses, returning 1.3 variants per case on average. Variant burden reduced to one variant per 200 cases on iterative monthly reanalysis. Application to an unselected cohort of 4,735 undiagnosed individuals identified 241 diagnoses (5.1% yield): 78 (32%) due to new gene-disease relationships, 54 (22%) due to new variant-level evidence and 109 (45%) due to improved analysis strategies. Our automated, iterative reanalysis model demonstrates the feasibility of delivering frequent, systematic reanalysis at scale.

Related

  1. Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)
    M. Nunez, B. Davies
    bioRxivPreprintCC BYFull textBioinformatics & computational biologybioRxiv
  2. Cross-Dataset Reanalysis of Mouse Alzheimer Disease Single-Cell RNA-Sequencing Data Reveals a Reproducible Innate Immune Activation Signature
    A. Anand, E. Natarajan
    bioRxivPreprintAll rights reservedFull textBioinformatics & computational biologybioRxiv
  3. Emerging and Novel Ovine Viruses: Molecular Diagnostics, Genomic and Metagenomic Surveillance, and One Health Perspectives.
    Gul Muhammad Shahbaz, Zeeshan Ashraf, Shuxin Chen +10
    Animals : an open access journal from MDPIPeer-reviewedCC BYFull textZoology & animal behaviourEurope PMC
  4. GLARE: Genomic Language model for Automated Repetitive Element classification
    N. Haas, T. Bruna
    bioRxivPreprintCC BY-NC-NDFull textBioinformatics & computational biologybioRxiv
  5. Clinical Long-Read Genome Sequencing for Rare-Disease Diagnostics
    Tessa J.J. de Bitter, Bart P. G. H. van der Sanden, Lydia Sagath +34
    New England Journal of MedicinePeer-reviewedLicence not statedFull text9 citedGenetics & genomicsOpenAlex

Cite

BibTeXRIS
BibTeX
@article{welland2026automated,
  title = {{Automated reanalysis of genomic data for rare disease diagnostics at scale}},
  author = {Matthew J. Welland and K. D. Ahlquist and Paul De Fazio and Christina Anne Austin-Tse and Lynn S. Pais and Laura Wedd and Samantha Bryen and Rocío Rius and Michael Franklin and Caitlin Morrison and Giles Hall and Laura Gauthier and Alex Bloemendal and David I. Francis and Andrew John Mallett and Amali C. Mallawaarachchi and Paul J. Lockhart and Richard Jacob Leventer and Ingrid Eileen Scheffer and Katherine B. Howell and Karin S. Kassahn and Hamish S. Scott and Julie M. McGaughran and John Christodoulou and David Ross Thorburn and Bryony A. Thompson and Chirag Patel and Greg Smith and Anne O’Donnell‐Luria and Simon Sadedin and Heidi L. Rehm and Sebastian Lunke and Jeremiah D. Wander and Kaitlin E. Samocha and Cas Simons and Daniel G. MacArthur and Zornitza Stark},
  journal = {Nature Medicine},
  year = {2026},
  month = jun,
  doi = {10.1038/s41591-026-04477-5},
  url = {https://doi.org/10.1038/s41591-026-04477-5},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Automated reanalysis of genomic data for rare disease diagnostics at scale
AU  - Matthew J. Welland
AU  - K. D. Ahlquist
AU  - Paul De Fazio
AU  - Christina Anne Austin-Tse
AU  - Lynn S. Pais
AU  - Laura Wedd
AU  - Samantha Bryen
AU  - Rocío Rius
AU  - Michael Franklin
AU  - Caitlin Morrison
AU  - Giles Hall
AU  - Laura Gauthier
AU  - Alex Bloemendal
AU  - David I. Francis
AU  - Andrew John Mallett
AU  - Amali C. Mallawaarachchi
AU  - Paul J. Lockhart
AU  - Richard Jacob Leventer
AU  - Ingrid Eileen Scheffer
AU  - Katherine B. Howell
AU  - Karin S. Kassahn
AU  - Hamish S. Scott
AU  - Julie M. McGaughran
AU  - John Christodoulou
AU  - David Ross Thorburn
AU  - Bryony A. Thompson
AU  - Chirag Patel
AU  - Greg Smith
AU  - Anne O’Donnell‐Luria
AU  - Simon Sadedin
AU  - Heidi L. Rehm
AU  - Sebastian Lunke
AU  - Jeremiah D. Wander
AU  - Kaitlin E. Samocha
AU  - Cas Simons
AU  - Daniel G. MacArthur
AU  - Zornitza Stark
PY  - 2026
DA  - 2026/06/24
JO  - Nature Medicine
DO  - 10.1038/s41591-026-04477-5
UR  - https://doi.org/10.1038/s41591-026-04477-5
AB  - Reanalysis of genomic data in rare disease is highly effective in increasing diagnostic yields but remains limited by manual approaches. Automation and optimization for high specificity will be necessary to ensure scalability, adoption and sustainability of iterative reanalysis. We developed Talos, an open-source tool that automates variant prioritization by integrating dynamically updated gene-disease and variant-level evidence with inheritance-aware filtering and validated its performance using data from 1,089 individuals with rare disease. Trio-based analysis identified 90% of known diagnoses, returning 1.3 variants per case on average. Variant burden reduced to one variant per 200 cases on iterative monthly reanalysis. Application to an unselected cohort of 4,735 undiagnosed individuals identified 241 diagnoses (5.1% yield): 78 (32%) due to new gene-disease relationships, 54 (22%) due to new variant-level evidence and 109 (45%) due to improved analysis strategies. Our automated, iterative reanalysis model demonstrates the feasibility of delivering frequent, systematic reanalysis at scale.
SN  - 1078-8956
SN  - 1546-170X
SN  - 1744-7933
ER  -