Multitrait Genome-Wide Analysis in the UK Biobank Reveals Novel and Distinct Variants Influencing Cardiovascular Traits in Africans and Europeans
Bibliographic record
Abstract
Abstract In exploring the trans-ancestral genetic nuances of cardiovascular traits, we conducted a multi-trait genome-wide association study focusing on African (AFR) and European (EUR) populations in the UK Biobank. Here, we identify 50 genomic risk loci in the AFR population, among which 43 are novel discoveries associated with four cardiovascular traits. Similarly, we identify 829 loci in the EUR population, with 47 being novel. Also, at these loci, we identify 52 SNPs (45 novel) in the AFR population and 1,856 SNPs (957 novel) in the EUR population, among which 83 are shared, highlighting both the shared and rich diversity of the genetic underpinnings of cardiovascular disease across populations. Furthermore, functional mapping of these SNPs reveals distinct distribution patterns, with the EUR population showing a higher proportion in intronic and untranslated regions. Further, our study unravels population-specific genetic associations, identifying 3,011 genes exclusive to the EUR group and 36 distinct to the AFR group. Additionally, gene enrichment analyses show unique enriched pathways for each population, highlighting the potential influence of genetic ancestry on cardiovascular trait mechanisms and manifestation. Collectively, our results underscore the importance of population-specific approaches in studying the genetic underpinnings of cardiovascular health and further indicate potential avenues for personalised medicine and targeted 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".