Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Shilpa Nadimpalli Kobren, Mikhail A. Moldovan, Rebecca Reimers, Daniel Traviglia, Xinyun Li, Danielle Barnum, Alexander Veit, Rosario I. Corona, George de V. Carvalho Neto, Julian Willett, Michele Berselli, William Ronchetti, Stanley F. Nelson, Julian A. Martinez-Agosto, Richard Sherwood, Joel Krier, Isaac S. Kohane and Shamil R. Sunyaev ()
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Shilpa Nadimpalli Kobren: Harvard Medical School
Mikhail A. Moldovan: Harvard Medical School
Rebecca Reimers: La Jolla
Daniel Traviglia: Harvard Medical School
Xinyun Li: Boston
Danielle Barnum: 1081 HV
Alexander Veit: Harvard Medical School
Rosario I. Corona: Los Angeles
George de V. Carvalho Neto: Los Angeles
Julian Willett: New York
Michele Berselli: Harvard Medical School
William Ronchetti: Harvard Medical School
Stanley F. Nelson: Los Angeles
Julian A. Martinez-Agosto: Los Angeles
Richard Sherwood: Boston
Joel Krier: Boston
Isaac S. Kohane: Harvard Medical School
Shamil R. Sunyaev: Harvard Medical School

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform in-depth analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development. The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale case-based diagnostic analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser ( https://dbmi-bgm.github.io/udn-browser/ ). These results show that case-level diagnostic efforts should be supplemented by a joint genomic analysis across cohorts.

Date: 2025
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DOI: 10.1038/s41467-025-61712-2

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