Genetic control of body weight by the human brain proteome
Bibliographic record
Abstract
ABSTRACT Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with body weight but the biological relevance of most remains unexplored. Given the critical role of the brain in multiple biological processes associated with body weight regulation, we set out to determine whether genetic variants linked with body mass index (BMI) could be mapped to brain proteins. Using genetic colocalization, we mapped 23 loci from the largest BMI GWAS (n=806,834) to brain proteins (obtained from a dataset of >7000 dorsolateral prefrontal cortex proteins measured by mass spectrometry in >400 individuals). We also performed a proteome-wide Mendelian randomization analysis followed by genetic colocalization, which allowed us to identify an additional 48 brain proteins linked with BMI. Multi-trait colocalization suggested that more than 75% of the protein quantitative trait loci (pQTL)-BMI associations are mediated via protein expression and not via RNA expression. Single-cell sequencing from the human brain cortex revealed that the genes expressing the proteins associated with BMI may be predominantly expressed in oligodendrocytes. In the Québec Family Study, a genetic risk score (GRS) including these brain pQTLs was associated with higher dietary carbohydrate intake and lower lipid intake whereas a GRS including the 67 variants most strongly associated with BMI was not associated with dietary intake. In conclusions, we identified 71 proteins expressed in the prefrontal cortex that may be critical regulators of body weight and possibly dietary intake in humans.
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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 | 0.000 |
| 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".