Effect of Hidden Relatedness on Single-Step Genetic Evaluation in an Advanced Open-Pollinated Breeding Program

Abstract

Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

“…In this study, we used single-step GBLUP (ssGBLUP), GBLUP and ABLUP models to estimate genetic parameters and to test the prediction accuracies of different traits in an E. pellita breeding population. Most of the previous studies in forest trees which used ssGBLUP have included only the additive effects in the model ( Ratcliffe et al 2017 ; Cappa et al 2017 , 2019 ; Klápšte et al 2018 ). In contrast, we used additive and dominance effects in ssGBLUP model to study the genetic parameters and to test prediction accuracies of different traits.…”

“…Application of markers in open-pollinated (OP) families is particularly useful as markers can uncover half-sib and full-sib relationships within the OP families ( El-Kassaby et al 2011 ). This will have a significant impact on the precision and accuracy of the estimated genetic parameters as demonstrated by a number of studies ( Bush and Thumma 2013 ; Gamal El-Dien et al 2016 , 2018 ; Müller et al 2017 ; Klápště et al 2017 ; Klápšte et al 2018 ). In traditional methods, all individuals of an OP family are treated as half-sibs.…”

“…While, there are many studies using the ssGBLUP in animal breeding there are only a few such studies in forest trees ( Ratcliffe et al 2017 ; Cappa et al 2017 , 2019 ; Klápšte et al 2018 ). In one of the first studies to implement this method in tree breeding, Cappa et al (2017) found increased accuracies of breeding values with the ssGBLUP compared to pedigree-based ABLUP method.…”

Effect of trait’s expression level on single-step genomic evaluation of resistance to Dothistroma needle blight

“…Forest tree progeny tests are typically large populations containing thousands of individuals, which has prevented full-scale genotyping [25]. Therefore, the combination of phenotype, pedigree and genomic information through a single-step evaluation approach [31] is the preferred strategy in forest tree breeding [32][33][34].…”

Single-step genomic prediction of fruit-quality traits using phenotypic records of non-genotyped relatives in citrus

“…Through the H matrix, A is augmented by G and vice versa, enabling ssGBLUP to improve accuracy in the evaluation of breeding values for both genotyped and non-genotyped relatives. However, although ssGBLUP has several advantages to the multiple-step method as described above, the application of this method for plant breeding has been limited to several species, including rice ( Oryza sativa L.) [16,17], wheat ( Triticum aestivum L.) [18,19], maize ( Zea mays L.) [20], and those of forest trees [21–24], and to the best of our knowledge, no previous studies of ssGBLUP have reported for fruit breeding. Accordingly, we applied ssGBLUP to a real dataset of fruit-quality traits obtained from an ongoing citrus breeding program.…”