|Title||Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested.|
|Publication Type||Journal Article|
|Year of Publication||2023|
|Authors||Larios, Eugenio, and Susan Mazer|
Seed size affects individual fitness in wild plant populations, but its ability to evolve may be limited by low narrow-sense heritability (h2). h2 is estimated as the proportion of total phenotypic variance (σ2P) attributable to additive genetic variance (σ2A), so low values of h2 may be due to low σ2A (potentially eroded by natural selection) or to high values of the other factors that contribute to σ2P, such as extranuclear maternal effects (m2) and environmental variance effects (e2). Here, we reviewed the published literature and performed a meta-analysis to determine whetherh2of seed size is routinely low in wild populations and, if so, which components of σ2P contribute most strongly to total phenotypic variance. We analyzed available estimates of narrow-sense heritability (h2) of seed size, as well as the variance components contributing to these parameters. Maternal and environmental components of σ2P were significantly greater than σ2A, dominance, paternal, and epistatic components. These results suggest that low h2 of seed size in wild populations (the mean value observed in this study was 0.13) is due to both high values of maternally derived and environmental (residual) σ2, and low values of σ2A in seed size. The type of breeding design used to estimate h2 and m2 also influenced their values, with studies using diallel designs generating lower variance ratios than nested and other designs. e2 was not influenced by breeding design. For some breeding designs, the number of genotypes included in a study also influenced the resulting h2 and e2 estimates, but not m2. Our data support the view that a diallel design is better suited than the alternatives for the accurate estimation of σ2A in seed size due to its factorial design and the inclusion of reciprocal crosses, which allows the independent estimation of both additive and non-additive components of variance.