Kobren Shilpa Nadimpalli, Moldovan Mikhail A, Reimers Rebecca, Traviglia Daniel, Li Xinyun, Barnum Danielle, Veit Alexander, Corona Rosario I, Carvalho Neto George de V, Willett Julian, Berselli Michele, Ronchetti William, Nelson Stanley F, Martinez-Agosto Julian A, Sherwood Richard, Krier Joel, Kohane Isaac S, Sunyaev Shamil R
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
Scripps Research Translational Institute, La Jolla, California, USA.
Nat Commun. 2025 Aug 7;16(1):7267. doi: 10.1038/s41467-025-61712-2.
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.
由于我们有能力对患有超罕见疾病的个体患者进行深入分析，用于罕见病诊断的基因组学发展迅速。国际上超罕见疾病队列规模的不断扩大，使得我们能够对新发现进行全队列分析，但用于联合分析这些患者的经过良好校准的统计遗传学方法仍在开发中。未确诊疾病网络（UDN）将美国多个临床、研究和实验中心整合在一起，以促进和扩大基于病例的诊断分析。在此，我们展示了对UDN网络中患者全基因组测序数据的首次联合分析。我们引入了新的、经过良好校准的统计方法，用于对新发复发和复合杂合性的疾病基因进行优先级排序。我们还检测了富含候选和已知诊断基因的通路。我们的计算分析与系统的临床审查相结合，重现了已知诊断并揭示了新的疾病关联。我们进一步发布了一个软件包RaMeDiES，可对去识别化的测序队列进行自动交叉分析，以实现新的诊断和研究发现。全队列的基因水平发现和变异水平信息可在一个面向公众的浏览器（https://dbmi-bgm.github.io/udn-browser/）中获取。这些结果表明，病例水平的诊断工作应以跨队列的联合基因组分析作为补充。
