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, Julian Willett,Michele Berselli, William Ronchetti,Richard Sherwood,Joel Krier,Isaac S. Kohane, Undiagnosed Diseases Network,Shamil R. Sunyaev

bioRxiv the preprint server for biology(2024)

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摘要
Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform “N-of-1” analyses on individual patients. The increasing sizes of ultra-rare, “N-of-1” 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.[1][1],[2][2] The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We apply existing and 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 make our gene-level findings and variant-level information across the cohort available in a public-facing browser (). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2
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