Adverse outcomes in SARS-CoV-2 infections are associated with a combination of variant genotypes at two loci in the APOL1 gene: a UK Biobank study
Bibliographic record
Abstract
Risk of hospitalisation or death from COVID-19 in the UK is disproportionately high in people of African ancestry. Two APOL1 haplotypes (G1 and G2) found at high frequency only in populations of African descent are associated with increased risk of non-communicable and infectious diseases. Here, we test the hypothesis that adverse COVID-19 outcomes are also associated with these APOL1 high-risk variants. Within 9,433 individuals with African ancestry in the UK Biobank, there were 172 hospitalisations and 47 deaths attributed to COVID-19 as of December 2021. We examined APOL1 genotypes for association with hospitalisation and death while controlling for risk factors previously associated with poor COVID-19 outcomes. We identified an association between carriage of two APOL1 high-risk variants and death from COVID-19 (OR=2.7, 95% CI: 1.2-6.4). Stratified by genotype, those with G1/G2 had a higher odds of COVID-19 hospitalisation (OR=2.1, 95% CI: 1.1-3.8) and death (OR=5.9, 95% CI: 2.2-15.3) than G0/G0. There was no significant association detected in carriers of G1/G1 and G2/G2. These data suggest that the APOL1 G1/G2 genotype contributes to the increased rates of hospitalisation and mortality from COVID-19 in people of African ancestry, and could help to identify those at higher risk of severe COVID-19. This is especially relevant to geographical regions where APOL1 G1 and G2 high-risk variants are common, such as West and Central Africa and their diaspora.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".