Assessment of identity disequilibrium and its relation to empirical heterozygosity fitness correlations: a meta‐analysis
Bibliographic record
Abstract
Heterozygosity fitness correlations (HFCs) have frequently been used to detect inbreeding depression, under the assumption that genome-wide heterozygosity is a good proxy for inbreeding. However, meta-analyses of the association between fitness measures and individual heterozygosity have shown that often either no correlations are observed or the effect sizes are small. One of the reasons for this may be the absence of variance in inbreeding, a requisite for generating general-effect HFCs. Recent work has highlighted identity disequilibrium (ID) as a measure that may capture variance in the level of inbreeding within a population; however, no thorough assessment of ID in natural populations has been conducted. In this meta-analysis, we assess the magnitude of ID (as measured by the g2 statistic) from 50 previously published HFC studies and its relationship to the observed effect sizes of those studies. We then assess how much power the studies had to detect general-effect HFCs, and the number of markers that would have been needed to generate a high expected correlation (r(2) = 0.9) between observed heterozygosity and inbreeding. Across the majority of studies, g2 values were not significantly different than zero. Despite this, we found that the magnitude of g2 was associated with the average effect sizes observed in a population, even when point estimates were nonsignificant. These low values of g2 translated into low expected correlations between heterozygosity and inbreeding and suggest that many more markers than typically used are needed to robustly detect HFCs.
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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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".