2018
DOI: 10.1111/nyas.13571
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Quantitative genetic methods depending on the nature of the phenotypic trait

Abstract: A consequence of the assumptions of the infinitesimal model, one of the most important theoretical foundations of quantitative genetics, is that phenotypic traits are predicted to be most often normally distributed (so-called Gaussian traits). But phenotypic traits, especially those interesting for evolutionary biology, might be shaped according to very diverse distributions. Here, I show how quantitative genetics tools have been extended to account for a wider diversity of phenotypic traits using first the th… Show more

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Cited by 64 publications
(77 citation statements)
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“…Instead, a novel approach for future herbicide resistance fitness studies would be to draw on evolutionary quantitative genetic analyses developed within other ecological and resistance disciplines (Juenger and Bergelson, 2000; Agrawal et al, 2002; Klerks et al, 2011). These methods directly measure the underlying genetic correlations between resistance and fitness traits and can be directly examined from pedigreed lines representing a broad range of independently evolved R and S genotypes, even when the underlying genetic basis of resistance is unknown (Wilson et al, 2010; de Villemereuil, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…Instead, a novel approach for future herbicide resistance fitness studies would be to draw on evolutionary quantitative genetic analyses developed within other ecological and resistance disciplines (Juenger and Bergelson, 2000; Agrawal et al, 2002; Klerks et al, 2011). These methods directly measure the underlying genetic correlations between resistance and fitness traits and can be directly examined from pedigreed lines representing a broad range of independently evolved R and S genotypes, even when the underlying genetic basis of resistance is unknown (Wilson et al, 2010; de Villemereuil, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…Non‐Gaussian traits are inherently nonadditive on the scale on which they are expressed. In GLMMs, a link function is used to model traits on a latent scale that has a Gaussian distribution and where effects are expected to be additive (de Villemereuil, 2018; de Villemereuil, Schielzeth, Nakagawa, & Morrissey, 2016). This latent scale is statistically convenient and is the scale on which heritabilities for binomial and count data are commonly reported.…”
Section: Methodsmentioning
confidence: 99%
“…All analyses were conducted using ASReml‐R version 4.1.0.106 (VSN International, Hemel Hempstead) within the R framework (version 3.6.1) (R Core Team 2019) and were based on restricted maximum likelihood estimation. While clutch size is discontinuous, we do not consider it to be determined by a, for example, Poisson process, and it is thus modeled here assuming a Gaussian process (de Villemereuil 2018), as is laydate. Total narrow‐sense heritability was estimated as the ratio of the total additive genetic variance relative to the within‐year phenotypic variance.…”
Section: Methodsmentioning
confidence: 99%