Exome-wide rare variant analyses of two bone mineral density phenotypes: the challenges of analyzing rare genetic variation
Bibliographic record
Abstract
Performance of a recently developed test for association between multivariate phenotypes and sets of genetic variants (MURAT) is demonstrated using measures of bone mineral density (BMD). By combining individual-level whole genome sequenced data from the UK10K study, and imputed genome-wide genetic data on individuals from the Study of Osteoporotic Fractures (SOF) and the Osteoporotic Fractures in Men Study (MrOS), a data set of 8810 individuals was assembled; tests of association were performed between autosomal gene-sets of genetic variants and BMD measured at lumbar spine and femoral neck. Distributions of p-values obtained from analyses of a single BMD phenotype are compared to those from the multivariate tests, across several region definitions and variant weightings. There is evidence of increased power with the multivariate test, although no new loci for BMD were identified. Among 17 genes highlighted either because there were significant p-values in region-based association tests or because they were in well-known BMD genes, 4 windows in 2 genes as well as 6 single SNPs in one of these genes showed association at genome-wide significant thresholds with the multivariate phenotype test but not with the single-phenotype test, Sequence Kernel Association Test (SKAT).
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".