Incidental and clinically actionable genetic variants in 1005 whole exomes and genomes from Qatar
Bibliographic record
Abstract
Next generation sequencing (NGS) technologies such as whole genome and whole exome sequencing has enabled accurate diagnosis of genetic diseases through identification of variations at the genome wide level. While many large populations have been adequately covered in global sequencing efforts little is known on the genomic architecture of populations from Middle East, and South Asia and Africa. Incidental findings and their prevalence in populations have been extensively studied in populations of Caucasian descent. The recent emphasis on genomics and availability of genome-scale datasets in public domain for ethnic population in the Middle East prompted us to estimate the prevalence of incidental findings for this population. In this study, we used whole genome and exome data for a total 1005 non-related healthy individuals from Qatar population dataset which contained 20,930,177 variants. Systematic analysis of the variants in 59 genes recommended by the American College of Medical Genetics and Genomics for reporting of incidental findings revealed a total of 2 pathogenic and 2 likely pathogenic variants. Our analysis suggests the prevalence of incidental variants in population-scale datasets is approx. 0.6%, much lower than those reported for global populations. Our study underlines the essentiality to study population-scale genomes from ethnic groups to understand systematic differences in genetic variants associated with disease predisposition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".