A quantitative review of heterozygosity–fitness correlations in animal populations
Bibliographic record
Abstract
The ease of obtaining genotypic data from wild populations has renewed interest in the relationship between individual genetic diversity and fitness-related traits (heterozygosity-fitness correlations, or HFC). Here we present a comprehensive meta-analysis of HFC studies using powerful multivariate techniques which account for nonindependence of data. We compare these findings with those from univariate techniques, and test the influence of a range of factors hypothesized to influence the strength of HFCs. We found small but significantly positive effect sizes for life-history, morphological, and physiological traits; while theory predicts higher mean effect sizes for life-history traits, effect size did not differ consistently with trait type. Newly proposed measures of variation were no more powerful at detecting relationships than multilocus heterozygosity, and populations predicted to have elevated inbreeding variance did not exhibit higher mean effect sizes. Finally, we found evidence for publication bias, with studies reporting weak, nonsignificant effects being under-represented in the literature. In general, our review shows that HFC studies do not generally reveal patterns predicted by population genetic theory, and are of small effect (less than 1% of the variance in phenotypic characters explained). Future studies should use more genetic marker data and utilize sampling designs that shed more light on the biological mechanisms that may modulate the strength of association, for example by contrasting the strength of HFCs in mainland and island populations of the same species, investigating the role of environmental stress, or by considering how selection has shaped the traits under investigation.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".