Abstract
Plant breeders always face the challenge to select the best individuals. Selection methods are required that maximize selection gain based on available data. When several crosses have been made, the BLUP procedure achieves this by combining phenotypic data with information on pedigree relationships via an index, known as family-index selection. The index, estimated based on the intra-class correlation coefficient, exploits the relationship among individuals within a family relative to other families in the population. An intra-class correlation coefficient of one indicates that the individual performance can be fully explained based on the family background, whereas an intra-class correlation coefficient of zero indicates the performance of individuals is independent of the family background. In the case the intra-class correlation coefficient is one, family-index selection is considered. In the case the intra-class correlation coefficient is zero, individual selection is considered. The main difference between individual and family-index selection lies in the adjustment in estimating the individual's effect depending on the intra-class correlation coefficient afforded by the latter. Two examples serve to illustrate the application of the BLUP method. The efficiency of individual and family-index selection was evaluated in terms of the heritability obtained from linear mixed models implementing the selection methods by suitably defining the treatment factor as the sum of individual and family effect. Family-index selection was found to be at least as efficient as individual selection in Dianthus caryophyllus L., except for flower size in standard carnation and vase life in mini carnation for which traits family-index selection outperformed individual selection. Family-index selection was superior to individual selection in Pelargonium zonale in cases when the heritability was low. Hence, the pedigree-based BLUP procedure can enhance selection efficiency in production-related traits in P. zonale or shelf-life related in D. caryophyllus L.
Highlights
For decades “Best Linear Unbiased Prediction” (BLUP) has been the standard selection method in animal breeding (Henderson, 1950), where the breeding values of sires are estimated based on progeny performance to select superior genotypes and to breed superior families (Robinson, 1991)
The context of Best Linear Unbiased Estimation” (BLUE) and best linear unbiased prediction (BLUP) is the standard linear mixed model (LMM; Robinson, 1991; Piepho, 1994), y = Xβ + Zu + e, where y is a vector of n observations, β is a vector of fixed effects, X and Z are design matrices associated with the fixed and random effects, u, the vector of random effects assumed to be distributed according to u ∼ MVN(0, G) where 0 is a null vector and G is the variance-covariance matrix of the random effects, and e is the vector of residual errors assumed to be distributed as e ∼ MVN(0, R) with R the variance-covariance matrix of the residual errors
The results were supported by the variance component estimates and box plots of BLUPs, which are described in detail below
Summary
For decades “Best Linear Unbiased Prediction” (BLUP) has been the standard selection method in animal breeding (Henderson, 1950), where the breeding values of sires are estimated based on progeny performance to select superior genotypes and to breed superior families (Robinson, 1991). This method has been used in commodity crops (Piepho et al, 2008). The pedigree-based BLUP method proposed in the present study is currently the most promising selection method to use when no marker-data is available By this method, useful information can be obtained as to whether the trait is dependent or independent of the family background. This information is vital for selecting individuals for genotyping, because the goal of creating diversity panels is to represent the entire genetic diversity of parental populations, i.e., individuals should be selected with similar biotic or abiotic adaptation or photoperiod requirements (Singh and Singh, 2015, p. 220)
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