Meta-analysis of GWAS of bovine stature with >50,000 animals imputed to whole-genome sequence
Bibliographic record
Abstract
Extensive meta analysis of GWAS in humans has identified 697 significant SNP, however these SNP explainonly 20% the total genetic variation. In order to compare the genetic architecture of stature in humans tostature in cattle, we performed a large meta-analysis using imputed sequence data. The 1000 Bull Genomesproject provided a multi-breed reference population of 1,147 sequenced animals to impute SNP-chipgenotypes up to whole genome sequence for 15 populations. The populations from Australia, Canada,Denmark, Finland, France, Germany, the Netherlands, and the USA represented the Angus, Fleckvieh,Holstein, Jersey, Montbeliarde, Normande, and Nordic Red Dairy Cattle breeds. Genome-wide associationstudies were performed on stature phenotypes for each of the populations. Individual GWAS studies revealedmany QTL regions and several regions harboured good candidate genes, e.g. PLAG1, IGF2. Results fromthese GWAS studies were combined in a meta-analysis to increase the power for QTL detection and torefine QTL regions exploiting the different patterns of LD among the breeds. Results of this meta-analysiswill be validated in an independent population to determine how much of the variation in stature can beexplained by the significant SNP
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".