GWAS identifies genetic clusters of cardiometabolic risk factors in continental Africans
Bibliographic record
Abstract
Abstract Cardiometabolic risk factors (CMRFs) are a growing global health concern. Here, we set out to identify novel genetic loci associated with CMRFs in two continental African populations, Ugandans and South African Zulu’s. Thus, we conducted a multivariate meta-analysis with META-SCOPA; where six CMRFs comprising of waist circumference (WC), triglycerides (TG), high-density lipoprotein (HDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), and glycated hemoglobin A1c (HbA1c) were used. META-SCOPA identified five novel loci and its Multi-marker Analysis of GenoMic Annotation (MAGMA) gene-set analysis showed an association with human histone acetyltransferase/male sex-lethal (MSL) complex. Fine-mapping, colocalization, tissue expression analyses, and phenome-wide association study (PheWAS) were also implemented to better understand the meta-analysis findings. Furthermore, as a supplement a principal component analysis (PCA) GWAS was also performed on the same six CMRFs. The first principal component denoted cardiometabolic_PC1 was driven mostly by DBP and SBP. Five novel, significant loci were identified after the meta-analysis. Ultimately, our novel loci and gene-sets offer fresh insights into the clustering of CMRFs and propose a unique link to the human histone acetyltransferase/MSL complex, as well as circulatory system processes in continental Africans. Additional large-scale efforts in Africa are needed to understand the clustering of CMRFs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".