Carrier frequency of autosomal recessive genetic conditions in diverse populations: Lessons learned from the genome aggregation database
Bibliographic record
Abstract
An equitable approach by the American College of Medical Genetics and Genomics (ACMG) has recently recommended carrier screening for genes associated with moderate to severe autosomal recessive conditions with a carrier frequency of ≥1/200 in the Genome Aggregation Database exomes (gnomADv2.0.2). We analyzed carrier frequencies in gnomADv3.1.1 genomes representing diverse populations. ClinVar data on 35 996 pathogenic/likely pathogenic variants in 419 genes were used to estimate the gnomAD frequency of heterozygous carriers. We found that ninety-two genes had a carrier frequency of ≥1/200, of which 63 were shared between v3.1.1 and v2.0.2 and 29 were new in v3.1.1. Addition of new populations (Amish, Finnish and Middle Eastern) increased the number of new genes with a carrier frequency of ≥1/200 to 71. Changes in carrier frequencies were attributed to new gnomAD populations, different sample sizes, new ClinVar data, and technical differences between exomes and genomes. This study highlights the dynamic changes in carrier frequencies due to new datasets from diverse populations and provides updated carrier frequencies based on the combined data from 184 352 genomes and exomes in gnomAD. We recommend a periodic review for inclusion of new population data to update carrier screening panels in the future.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".