Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested

野生植物种子大小的遗传力和方差组分:育种设计和测试基因型数量的影响

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Abstract

Seed size affects individual fitness in wild plant populations, but its ability to evolve may be limited by low narrow-sense heritability (h(2)). h(2) is estimated as the proportion of total phenotypic variance (σ(2)(P)) attributable to additive genetic variance (σ(2)(A)), so low values of h(2) may be due to low σ(2)(A) (potentially eroded by natural selection) or to high values of the other factors that contribute to σ(2)(P), such as extranuclear maternal effects (m(2)) and environmental variance effects (e(2)). Here, we reviewed the published literature and performed a meta-analysis to determine whether h(2) of seed size is routinely low in wild populations and, if so, which components of σ(2)(P) contribute most strongly to total phenotypic variance. We analyzed available estimates of narrow-sense heritability (h(2)) of seed size, as well as the variance components contributing to these parameters. Maternal and environmental components of σ(2)(P) were significantly greater than σ(2)(A), dominance, paternal, and epistatic components. These results suggest that low h(2) 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) σ(2), and low values of σ(2)(A) in seed size. The type of breeding design used to estimate h(2) and m(2) also influenced their values, with studies using diallel designs generating lower variance ratios than nested and other designs. e(2) was not influenced by breeding design. For some breeding designs, the number of genotypes included in a study also influenced the resulting h(2) and e(2) estimates, but not m(2). Our data support the view that a diallel design is better suited than the alternatives for the accurate estimation of σ(2)(A) 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.

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