Clinical GeneticsGeneticsPeer-reviewed journal article
Pilot study of EVIDENCE: High diagnostic yield and clinical utility of whole exome sequencing using an automated interpretation system for patients with suspected genetic disorders
Go Hun SeoiD, Tae Ho KimiD, Jung-young Park, Jungsul LeeiD, Sehwan Kim, Dhong‐gun Won, Arum OhiD, Yena LeeiD, In Hee ChoiiD, Jeongmin Choi, Hajeong LeeiD, Hee Gyung KangiD, Hee Yeon Cho, Min Hyun ChoiD, Yoon Jeon KimiD, Young Hee YooniD, Baik‐Lin EuniD, Robert J. DesnickiD, Changwon KeumiD, Beom Hee LeeiD
Abstract
PurposeEVIDENCE, an automated interpretation system, has been developed to facilitate the entire process of whole exome sequencing (WES) analyses. This study investigated the diagnostic yield of EVIDENCE in patients suspected genetic disorders. MethodsDNA from 330 probands (age range, 0-68 years) with suspected genetic disorders were subjected to WES. Candidate variants were identified by EVIDENCE and confirmed by testing family members and/or clinical reassessments. ResultsThe average number of overlapping organ categories per patient was 4.5 {+/-} 5.0. EVIDENCE reported a total 244 variants in 215 (65.1%) of the 330 probands. After clinical reassessment and/or family member testing, 196 variants were identified in 171 probands (51.8%), including 115 novel variants. These variants were confirmed as being responsible for 146 genetic disorders. One hundred-seven (54.6%) of the 196 variants were categorized as pathogenic or likely pathogenic before, and 146 (74.6%) after, clinical assessment and/or family member testing. Factors associated with a variant being confirmed as causative include rules, such as PVS1, PS1, PM1, PM5, and PP5, and similar symptom scores between that variant and a patients phenotype. ConclusionThis new, automated variant interpretation system facilitated the diagnosis of various genetic diseases with a 51% improvement in diagnostic yield.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 174
- FWCI
- 11.71
- Field percentile
- top 1%
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