Variance Formulae for Correlation Measures of Linkage Disequilibrium
Bibliographic record
Abstract
BACKGROUND: Linkage disequilibrium (LD) is the non-random association between alleles at different loci and remains important for disease mapping studies in humans. A common measure of LD is the sample correlation between indicator variables for alleles at the 2 loci. Knowledge of LD estimate precision may help inform biomedical decisions based on those estimates. OBJECTIVES AND METHODS: Variance formulae are obtained for correlation measures of LD in 4 scenarios. These scenarios include data in the form of gametic and genotypic counts, with different assumptions used to simplify the analysis. RESULTS: The formulae are expressed as polynomials (or ratios of polynomials) in higher-order disequilibrium coefficients with constants which are functions of the allele frequencies and Hardy-Weinberg disequilibrium coefficients. With genotypic data, the variance is the same as with gametic data when the phase is known and there is random mating. When the phase is unknown, the correlation LD has variance which is twice as large. CONCLUSIONS: Symbolic computation proved to be effective in facilitating algebraic derivations which would otherwise have been intractable.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".