scholarly journals Cases from the Undiagnosed Diseases Network: The continued value of counseling skills in a new genomic era

2019 ◽  
Vol 28 (2) ◽  
pp. 194-201 ◽  
Author(s):  
Ellen F. Macnamara ◽  
Kelly Schoch ◽  
Emily G. Kelley ◽  
Elizabeth Fieg ◽  
Elly Brokamp ◽  
...  
Keyword(s):  
Counseling Skills ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

scholarly journals Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

2021 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
◽  
Dustin Baldridge ◽  
Matt Velinder ◽  
Joel B. Krier ◽  
...  
Keyword(s):  
Online Survey ◽  
Variant Calling ◽  
Theoretical Method ◽  
The United States ◽  
Genomic Sequencing ◽  
Biomedical Data ◽  
Sequencing Data ◽  
Multimodal Data ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

The yield of thorough record review in the Undiagnosed Diseases Network

2021 ◽  
Vol 132 ◽  
pp. S187
Author(s):  
Laurie Findley ◽  
Jill Rosenfeld ◽  
Rebecca Spillman ◽  
Heidi Cope ◽  
Kelly Schoch ◽  
...  
Keyword(s):  
Record Review ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

The Undiagnosed Diseases Network International: Five years and more!

2020 ◽  
Vol 129 (4) ◽  
pp. 243-254 ◽  
Author(s):  
D. Taruscio ◽  
G. Baynam ◽  
H. Cederroth ◽  
S.C. Groft ◽  
E.W. Klee ◽  
...  
Keyword(s):  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

scholarly journals Identifying digenic disease genes using machine learning in the undiagnosed diseases network

2020 ◽  
Author(s):  
Souhrid Mukherjee ◽  
Joy D Cogan ◽  
John H Newman ◽  
John A Phillips ◽  
Rizwan Hamid ◽  
...  
Keyword(s):  
Biological Networks ◽  
Rapid Screening ◽  
Disease Genes ◽  
Individual Genome ◽  
Genetic Causes ◽  
Gene Pairs ◽  
Monogenic Diseases ◽  
Variant Gene ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

ABSTRACTRare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

2021 ◽  
Author(s):  
Souhrid Mukherjee ◽  
Joy D. Cogan ◽  
John H. Newman ◽  
John A. Phillips ◽  
Rizwan Hamid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Genes ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

scholarly journals A New Approach to Rare Diseases of Children: The Undiagnosed Diseases Network

2018 ◽  
Vol 196 ◽  
pp. 291-297.e2 ◽  
Author(s):  
Chloe M. Reuter ◽  
Elise Brimble ◽  
Colette DeFilippo ◽  
Annika M. Dries ◽  
Gregory M. Enns ◽  
...  
Keyword(s):  
Rare Diseases ◽  
New Approach ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

scholarly journals A window into living with an undiagnosed disease: illness narratives from the Undiagnosed Diseases Network

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Rebecca C. Spillmann ◽  
◽  
Allyn McConkie-Rosell ◽  
Loren Pena ◽  
Yong-Hui Jiang ◽  
...  
Keyword(s):  
Illness Narratives ◽  
Undiagnosed Disease ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

scholarly journals Rare disease patient matchmaking: development and outcomes of an internet case-finding strategy in the Undiagnosed Diseases Network

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Kimberly LeBlanc ◽  
◽  
Emily G. Kelley ◽  
Anna Nagy ◽  
Jorick Bater ◽  
...  
Keyword(s):  
Rare Disease ◽  
General Public ◽  
Disease Patient ◽  
Disease Diagnosis ◽  
Case Finding ◽  
Web Pages ◽  
Patient Resources ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network ◽  
Rare Disease Patient

Abstract Background Although clinician, researcher, and patient resources for matchmaking exist, finding similar patients remains an obstacle for rare disease diagnosis. The goals of this study were to develop and test the effectiveness of an Internet case-finding strategy and identify factors associated with increased matching within a rare disease population. Methods Public web pages were created for consented participants. Matches made, time to each inquiry and match, and outcomes were recorded and analyzed using descriptive statistics. A Poisson regression model was run to identify characteristics associated with matches. Results 385 participants were referred to the project and 158 had pages posted. 579 inquiries were received; 89.0% were from the general public and 24.7% resulted in a match. 81.6% of pages received at least one inquiry and 15.0% had at least one patient match. Primary symptom category of neurology, diagnosis, gene page, and photo were associated with increased matches (p ≤ 0.05). Conclusions This Internet case-finding strategy was of interest to patients, families, and clinicians, and similar patients were identified using this approach. Extending matchmaking efforts to the general public resulted in matches and suggests including this population in matchmaking activities can improve identification of similar patients.

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