Abstract
Estimating heritability based on individual phenotypic and genotypic measurements can be expensive and labour-intensive in commercial aquaculture breeding. Here, the feasibility of estimating heritability using within-family means of phenotypes and allelic frequencies was investigated. Different numbers of full-sib families and family sizes across ten generations with phenotypic and genotypic information on 10 K SNPs were analysed in ten replicates. Three scenarios, representing differing numbers of pools per family (one, two and five) were considered. The results showed that using one pool per family did not reliably estimate the heritability of family means. Using simulation parameters appropriate for aquaculture, at least 200 families of 60 progeny per family divided equally in two pools per family was required to estimate the heritability of family means effectively. Although application of five pools generated more within- and between- family relationships, it reduced the number of individuals per pool and increased within-family residual variation, hence, decreased the heritability of family means. Moreover, increasing the size of pools resulted in increasing the heritability of family means towards one. In addition, heritability of family mean estimates were higher than family heritabilities obtained from Falconer’s formula due to lower intraclass correlation estimate compared to the coefficient of relationship.
Highlights
Narrow-sense heritability determines the extent to which additive genetic component is contributing to the phenotypic variation among individuals or families (Viana 2002, Mathew et al 2012, Kruijer et al 2015, Kumar et al 2016, Speed et al 2017)
Quality control of genotypes was performed based on minor allelic frequency (MAF) of higher than and 200 family scenarios with 20, 40 and 60-progeny from Scenario 1 (S1) and Scenario 2 (S2) and, 50 and 400 families from S1, by first fitting the following animal model: y 1⁄4 μ1 þ Zv þ e where y is the vector of individual phenotypic values, μ represents the constant term, Z defines the design matrix relating records to appropriate randomÀ effeÁcts, v is the vector of animal random effects, assumed v $ NÀ0; σ2aAÁ, and e is the vector of residual errors, assumed e $ N 0; σ2e I, with σ2a and σ2e being the additive genetic and residual variances
(4) means The following sections summarise the effects of number of families, where y is the vector of family phenotypic mean values, μ represents the constant term, Z defines the design matrix relating pools to families, v is the vector of raÀndom Áeffects capturing the genetic value of the family, assumÀed v $Á N 0; σ2aG, and e is the vector of residual errors, assumed e $ N 0; σ2e I, with σ2a and σ2e being the additive genetic and residual variances
Summary
Narrow-sense heritability determines the extent to which additive genetic component is contributing to the phenotypic variation among individuals or families (Viana 2002, Mathew et al 2012, Kruijer et al 2015, Kumar et al 2016, Speed et al 2017). Genetic parameters are usually estimated using information on the pedigree and the phenotypic values of individuals to estimate heritability (Bérénos et al 2014, de los Campos et al 2015). One popular approach for pedigree-based genetic analysis is Henderson’s linear mixed model (Henderson 1950), which only estimates the additive genetic component of the phenotypic variance. MME can estimate non-additive genetic components using pedigree and/or genotypic information. Using molecular markers to capture the relationship between individuals via construction of a genomic relationship matrix (GRM) can be more accurate compared to pedigree information (Gay et al 2013, Bérénos et al 2014, de los Campos et al.2015, Kim et al 2015, Kruijer et al 2015), can be more advantageous in estimating genetic components
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