Impact of Variation in Practice in the Prenatal Reporting of Variants of Uncertain Significance by Commercial Laboratories: Need for Greater Adherence to Published Guidelines
Bibliographic record
Abstract
ABSTRACT Ultrasound will identify fetal structural anomalies in up to 2% to 3% of pregnancies; these can vary from minor isolated defects to severe multisystem abnormalities. As genetic investigations into the causes can assist with prenatal and perinatal decision-making, various tests exist to assist in this endeavor. Rapid aneuploidy detection and chromosomal microarray analysis are both used for detecting chromosomal aneuploidy and microdeletions or duplications (termed copy number variations) for pregnancies with fetal abnormalities. Approximately 6% of fetuses with identifiable structural abnormalities have pathogenic copy number variations, and 32% have an abnormal karyotype. In the last 8 years, whole exome sequencing (WES) has been used for making a genetic diagnosis in the remaining undiagnosed pregnancies, and a recent meta-analysis determined an exome sequencing diagnostic yield of 31% following nondiagnostic chromosomal microarray analysis or karyotype. Use of exome sequencing is complicated, however, by its association with possibly detecting variants of uncertain significance (VUSs) and pathogenic/likely pathogenic (P/LP) variants in genes possibly noncausal for the prenatal phenotypes. Thus, the value of reporting VUSs is under debate, with various policies worldwide. This study's purpose was to evaluate the clinical impact of WES and targeted panel implementation at the clinical level when fetal structural anomalies suggest a single underlying gene disorder or syndrome. The study focus was the frequency of reporting VUSs, the diagnostic yield, and the contribution of reported VUSs toward a final diagnosis. This retrospective chart review at the Provincial Medical Genetics Program at BC Women's Hospital in Vancouver included patients undergoing WES between January 2016 and December 2020. Amniocentesis was used for obtaining fetal samples, except for 4 cases of sample acquisition at the time of pregnancy termination. A total of 124 patients underwent prenatal WES or panel testing at different commercial laboratories following the identification of fetal structural anomalies by ultrasound. Patients had either WES (n = 90) or panel testing (n = 31), with 3 patients undergoing both, as decided by the ordering clinical geneticist. Genetic diagnoses of variants classified by the laboratory as P or LP were obtained in 20 of 93 patients (21.5%) via prenatal WES and in 9 of 34 patients (26%) who underwent panel testing. The 2 tests' overall diagnostic yield was 23%. Of these, 20 patients (69%) were diagnosed with autosomal dominant disorders. Overall, 32% of patients with panel testing and 42% of patients who had WES had at least 1 VUS, and 23% of WES and 15% of panels reported more than 1 VUS. There was great variation in the reporting of VUSs between laboratories. The study uncovered a lack of strict adherence to guidelines for reporting of prenatal VUSs among various laboratories. One weakness (and strength) of the study is its use of multiple commercial laboratories for the WES, panel testing, and VUS reporting. Although this is beneficial to gain various perspectives (such as the lack of adherence to guidelines), it also diminishes the consistency of the results across the entire study because of varied procedures for diagnoses. In conclusion, this study reports on 124 pregnancies with fetal anomalies at a large tertiary care center experiencing the provision of prenatal WES (or targeted gene panels) with variant interpretation and sequencing outsourced to various laboratories. A result of 23% diagnostic yield for WES and panel testing occurred based on the laboratory-reported LP and P variants. The large number of VUSs reported by laboratories implies significant resource implications due to the time to review evidence and further examine or investigate the fetus and/or parent. This is essential, and the authors emphasize the complex nature of the required posttest genetic counseling. Based on these results, the authors strongly encourage strict adherence to recommendations on VUS reporting in the prenatal setting, with the importance of a multidisciplinary approach bringing laboratory and clinical expertise of prenatal genomics and genetics together.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.378 | 0.666 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".