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

Autor:	Kobren SN; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Moldovan MA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Reimers R; Scripps Research Translational Institute, La Jolla, CA., Traviglia D; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Li X; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT., Barnum D; Access to Medicine Foundation, Amsterdam, The Netherlands., Veit A; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Corona RI; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA., Carvalho Neto GV; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA., Willett J; Department of Pathology and Laboratory Medicine, NewYork-Presbyterian Weill Cornell Medical Center, New York, NY., Berselli M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Ronchetti W; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Nelson SF; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA., Martinez-Agosto JA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA., Sherwood R; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA., Krier J; Department of Genetics, Atrius Health, Boston, MA., Kohane IS; Department of Biomedical Informatics, Harvard Medical School, Boston, MA., Sunyaev SR; Department of Biomedical Informatics, Harvard Medical School, Boston, MA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Aug 13. Date of Electronic Publication: 2024 Aug 13.
DOI:	10.1101/2024.02.13.580158
Abstrakt:	Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" 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. 1,2 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 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 N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.
Databáze:	MEDLINE
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