Fine mapping of the association with obesity at the FTO locus in African-derived populations
Bibliographic record
Abstract
Genome-wide association studies have identified many common genetic variants that are associated with polygenic traits, and have typically been performed with individuals of recent European ancestry. In these populations, many common variants are tightly correlated, with the perfect or near-perfect proxies for the functional or true variant showing equivalent evidence of association, considerably limiting the resolution of fine mapping. Populations with recent African ancestry often have less extensive and/or different patterns of linkage disequilibrium (LD), and have been proposed to be useful in fine-mapping studies. Here, we strongly replicate and fine map in populations of predominantly African ancestry the association between variation at the FTO locus and body mass index (BMI) that is well established in populations of European ancestry. We genotyped single nucleotide polymorphisms that are correlated with the signal of association in individuals of European ancestry but that have varying degrees of correlation in African-derived individuals. Most of the variants, including one previously proposed as functionally important, have no significant association with BMI, but two variants, rs3751812 and rs9941349, show strong evidence of association (P = 2.58 x 10(-6) and 3.61 x 10(-6) in a meta-analysis of 9881 individuals). Thus, we have both strongly replicated this association in African-ancestry populations and narrowed the list of potentially causal variants to those that are correlated with rs3751812 and rs9941349 in African-derived populations. This study illustrates the potential of using populations with different LD patterns to fine map associations and helps pave the way for genetically guided functional studies at the FTO locus.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".