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 nondiagnostic 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 |
