Concordance between whole‐exome sequencing and clinical Sanger sequencing: implications for patient care
Bibliographic record
Abstract
The clinical translation of next-generation sequencing has created a paradigm shift in the diagnostic assessment of individuals with suspected rare genetic diseases. Whole-exome sequencing (WES) simultaneously examines the majority of the coding portion of the genome and is rapidly becoming accepted as an efficient alternative to clinical Sanger sequencing for diagnosing genetically heterogeneous disorders. Among reports of the clinical and diagnostic utility of WES, few studies to date have directly compared its concordance to Sanger sequencing, which is considered the clinical "gold standard". We performed a direct comparison of 391 coding and noncoding polymorphisms and variants of unknown significance identified by clinical Sanger sequencing to the WES results of 26 patients. Of the 150 well-covered coding variants identified by Sanger sequencing, 146 (97.3%) were also reported by WES. Nine genes were excluded from the comparison due to consistently low coverage in WES, which might be attributed to the use of older exome capture kits. We performed confirmatory Sanger sequencing of discordant variants; including five variants with discordant bases and four with discordant zygosity. Confirmatory Sanger sequencing supported the original Sanger report for three of the five discordant bases, one was shown to be a false positive supporting the WES data, and one result differed from both the Sanger and WES data. Two of the discordant zygosity results supported Sanger and the other two supported WES data. We report high concordance for well-covered coding variants, supporting the use of WES as a screening tool for heterogeneous disorders, and recommend the use of supplementary Sanger sequencing for poorly-covered genes when the clinical suspicion is high. Importantly, despite remaining difficulties with achieving complete coverage of the whole exome, 10 (38.5%) of the 26 compared patients were diagnosed through WES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".