Population-scale gene-based analysis of whole-genome sequencing provides insights into metabolic health
Bibliographic record
Abstract
Abstract In addition to its coverage of the noncoding genome, whole-genome sequencing (WGS) may better capture the coding genome than exome sequencing. Here we sought to exploit this and identify new rare, protein-coding variants associated with metabolic health in WGS data ( n = 708,956) from the UK Biobank and All of Us studies. Identified genes highlight new biological mechanisms, including protein-truncating variants (PTVs) in the DNA double-strand break repair gene RIF1 that have a substantial effect on body mass index (2.66 kg m − 2 , s.e. 0.43, P = 3.7 × 10 −10 ). UBR3 is an intriguing example where PTVs independently increase body mass index and type 2 diabetes risk. Furthermore, PTVs in IRS2 have a substantial effect on type 2 diabetes (odds ratio 6.4 (3.7–11.3), P = 9.9 × 10 −14 , 34% case prevalence among carriers) and were also associated with chronic kidney disease independent of diabetes status, suggesting an important role for IRS2 in maintaining renal health. Our study demonstrates that large-scale WGS provides new mechanistic insights into human metabolic phenotypes through improved capture of coding sequences.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".