A Generalized Quasi-Likelihood Scoring Approach for Simultaneously Testing the Genetic Association of Multiple Traits
Bibliographic record
Abstract
Summary In the genetic association analysis of Holstein cattle data, researchers are interested in testing the association between a genetic marker with more than one estimated breeding value phenotype. It is well known that testing each trait individually may lead to problems of controlling the overall type I error rate and simultaneous testing of the association between a marker and multiple traits is desired. The analysis of Holstein cattle data has additional challenges due to complicated relationships between subjects. Furthermore, phenotypic data in many other genetic studies can be quantitative, binary, ordinal, count data or a combination of different types of data. Motivated by these problems, we propose a novel statistical method that allows simultaneous testing of multiple phenotypes and the flexibility to accommodate data from a broad range of study designs. The empirical results indicate that this new method effectively controls the overall type I error rate at the desired level; it is also generally more powerful than testing each trait individually at a given overall type I error rate. The method is applied to the analysis of Holstein cattle data as well as to data from the Collaborative Study on the Genetics of Alcoholism to demonstrate the flexibility of the approach with different phenotypic data types.
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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 | 0.035 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".