The ethnogeographic variability of genetic factors underlying G6PD deficiency
Bibliographic record
Abstract
Glucose-6-phosphate dehydrogenase (G6PD) deficiency caused by genetic variants in the G6PD gene, constitutes the most common enzymopathy worldwide, affecting approximately 5% of the global population. While carriers are mostly asymptomatic, they are at substantial risk of acute hemolytic anemia upon certain infections or exposure to various medications. As such, information about G6PD activity status in a given patient can constitute an important parameter to guide clinical decision-making. Here, we leveraged whole genome sequencing data from 142,069 unrelated individuals across seven human populations to provide a global comprehensive map of G6PD variability. By integrating established functional classifications with stringent computational predictions using 13 partly orthogonal algorithms for uncharacterized and novel variants, we reveal the large extent of ethnogeographic variability in G6PD deficiency and highlight its population-specific genetic composition. Overall, estimated disease prevalence in males ranged between 12.2% in Africans, 2.7–3.5% across Asia and 2.1% in Middle Easterners to < 0.3% in Europeans, Finnish and Amish. In Africans, the major deficient alleles were A-202A/376 G (minor allele frequency 11.6%) and A-968 C/376 G (0.5%). In contrast, G6PD deficiency in Middle Easterners was primarily due to the Mediterranean allele (1.3%) and the population-specific Cairo variant (0.4%). In South Asia, the most prevalent deficient alleles were Mediterranean (1.7%), Kerala (1.1%), Gond (0.9%) and Orissa (0.2%), whereas in East Asian populations the Canton (1.1%), Kaiping (0.7%) and Viangchan (0.3%) variants were predominant. Combined, our analyses provide a large dataset of G6PD variability across major ethnogeographic groups and can instruct population-specific genotyping strategies to optimize genetically guided therapeutic interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".