BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).
BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).
Authors: Zornitza Stark; Lena Dolman; Teri A Manolio; Brad Ozenberger; Sue L Hill; Mark J Caulfied; Yves Levy; David Glazer; Julia Wilson; Mark Lawler; Tiffany Boughtwood; Jeffrey Braithwaite; Peter Goodhand; Ewan Birney; Kathryn N North Journal: Am J Hum Genet Date: 2019-01-03 Impact factor: 11.025
Authors: Allyn McConkie-Rosell; Kelly Schoch; Jennifer Sullivan; Heidi Cope; Rebecca Spillmann; Christina G S Palmer; Loren Pena; Yong-Hui Jiang; Nicole Daniels; Nicole Walley; Khoon G Tan; Stephen R Hooper; Vandana Shashi Journal: Clin Genet Date: 2019-10-08 Impact factor: 4.438
Authors: In-Hee Lee; Jose A Negron; Carles Hernandez-Ferrer; William Jefferson Alvarez; Kenneth D Mandl; Sek Won Kong Journal: Hum Mutat Date: 2019-11-15 Impact factor: 4.878
Authors: Hyung-Lok Chung; Michael F Wangler; Paul C Marcogliese; Juyeon Jo; Thomas A Ravenscroft; Zhongyuan Zuo; Lita Duraine; Sina Sadeghzadeh; David Li-Kroeger; Robert E Schmidt; Alan Pestronk; Jill A Rosenfeld; Lindsay Burrage; Mitchell J Herndon; Shan Chen; Amelle Shillington; Marissa Vawter-Lee; Robert Hopkin; Jackeline Rodriguez-Smith; Michael Henrickson; Brendan Lee; Ann B Moser; Richard O Jones; Paul Watkins; Taekyeong Yoo; Soe Mar; Murim Choi; Robert C Bucelli; Shinya Yamamoto; Hyun Kyoung Lee; Carlos E Prada; Jong-Hee Chae; Tiphanie P Vogel; Hugo J Bellen Journal: Neuron Date: 2020-03-12 Impact factor: 17.173
Authors: David R Murdock; Hongzheng Dai; Lindsay C Burrage; Jill A Rosenfeld; Shamika Ketkar; Michaela F Müller; Vicente A Yépez; Julien Gagneur; Pengfei Liu; Shan Chen; Mahim Jain; Gladys Zapata; Carlos A Bacino; Hsiao-Tuan Chao; Paolo Moretti; William J Craigen; Neil A Hanchard; Brendan Lee Journal: J Clin Invest Date: 2021-01-04 Impact factor: 14.808