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. Author manuscript; available in PMC: 2021 Apr 23.
Published in final edited form as: Genet Med. 2020 Oct 23;23(2):259–271. doi: 10.1038/s41436-020-00984-z

Table 4.

Comparison of UDN-driven investigations at the clinical sites, to standard clinical genetics practice

Characteristics/Investigations UDN Clinical Practice
Participant characteristics Refractory to multiple prior clinical and laboratory evaluations, and often ES negative More likely to not have ES, may or may not have failed prior clinical evaluations
Time spent on pre-. post-, and face-to-face activities Face-to-face time represents a minority of time required for clinical and research activities (record review, literature review, phenotyping, bioinformatics, variant curation, RNASeq, collaborative science, integration of all data) Limited by clinical demands and financial constraints to a few hours for all activities
Equity in access:
 • Geographic access
 • Financial considerations
Accessible to all in USA and internationallya
All eligible irrespective of finances
Regional access more likelya
Financial considerations likely factor
Complementation/Supplementation of prior clinical data Personalized, temporally concentrated, comprehensive N-of-1 clinical consultations/laboratory tests/imaging/procedures
 • Fills in phenotypic gaps and generates additional clinical information
 • Leads to clinical diagnoses, diagnoses on targeted testing and contributes to genomic diagnoses
Variable, less likely to be temporally concentrated and comprehensive
Time and financially constrained in filling in gaps and obtaining new information
Innovative analyses of genomic data Straightforward diagnoses on UDN sequencing
 • ES/GS (35% diagnostic yield)


Research reanalysis of pre-UDN raw data from non-diagnostic ES (diagnostic yield of 43%)
 • Multiple other approaches to resolving prior ES negatives




Dual analysis of UDN generated genomic data by UDN core lab and clinical sites
 • Clinical site analysis led to additional genomic diagnoses (8%)

Manual curation of research variants generated by clinical site and core lab genomic analysis

RNASeq: Internal collaborations led to generation and analyses of RNASeq (contributed to diagnoses in 15%)

New disease gene identification
 • 8% of genomic diagnoses were novel disease gene associations
 • Can be pursued with internal collaborations
Straightforward diagnoses on clinical ES (diagnostic yield 25–30% in literature). GS less widely available

Standard reanalysis of negative ES with same pipeline (diagnosis yield of 6.5% at Duke, Stanford and Vanderbilt), 10–15% in literature
 • Limited further options to resolve ES negatives

Dual analysis unavailable due to lack of bioinformatics in clinics




Clinicians do not receive research variants from clinical labs for curation

Limited availability of RNASeq, with the clinical laboratory determining access

New disease gene identification
 • Time and resource constrained
a

See Figure S2 for detailed travel data