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

Abstract

The potential of genomic selection (GS) is currently being evaluated for fruit breeding. GS models are usually constructed based on information from both the genotype and phenotype of population. However, information from phenotyped but non-genotyped relatives can also be used to construct GS models, and this additional information can improve their accuracy. In the present study, we evaluated the utility of single-step genomic best linear unbiased prediction (ssGBLUP) in citrus breeding, which is a genomic prediction method that combines the kinship information from genotyped and non-genotyped relatives into a single relationship matrix for a mixed model to apply GS. Fruit weight, sugar content, and acid content of 1,935 citrus individuals, of which 483 had genotype data of 2,354 genome-wide single nucleotide polymorphisms, were evaluated from 2009–2012. The prediction accuracy of ssGBLUP for genotyped individuals was similar to or higher than that of usual genomic best linear unbiased prediction method using only genotyped individuals, especially for sugar content. Therefore, ssGBLUP could yield higher accuracy in genotyped individuals by adding information from non-genotyped relatives. The prediction accuracy of ssGBLUP for non-genotyped individuals was also slightly higher than that of conventional best linear unbiased prediction method using pedigree information. This indicates that ssGBLUP can enhance prediction accuracy of breeding values for non-genotyped individuals using genomic information of genotyped relatives. These results demonstrate the potential of ssGBLUP for fruit breeding, including citrus.

Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi‐trait pedigree and genomic selection models

“…A convenient possibility to make use of this additional information source is given by combining both the pedigree and genomic relationship matrices in a common hybrid matrix (Imai et al., 2019; Velazco, Malosetti, et al., 2019), which led to a slight increase in the prediction ability for the dough rheology traits by increasing the differentiation according to the expected population average of the 360 involved families. Employing this method had moreover a beneficial impact of the prediction ability when fitting models with a training population consisting solely of lines with superior baking quality parameters.…”

Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

“…Some merit of using a single-step genomic prediction was found in this study when testing a breeding strategy in which only advanced generation lines tested in multi-environment trials are genotyped to improve the prediction of non-genotyped early generation lines tested in preliminary yield trials. The above-mentioned studies employed however fixed values for the scaling factors α, β, τ and ω, whereas a slight advantage was found by [41] when varying the mixing proportion of the pedigree and genomic relationship matrix by altering the ratio τ : (τ − 1) with the constraint τ = ω and τ > 0. The variation of the scaling factors showed on the other hand a marginal effect on accuracy of the tested single-step genomic prediction approach in the study at hand, making the basic weights of τ = ω = 1 preferable, while setting arbitrary values of α = 0.95 and β = 0.05 can furthermore ensure that G mod in the lower right quadrant of H is invertible and aid in the convergence of the respective prediction models [39] albeit this was not an issue in the study at hand.…”

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

“… Kumar et al (2012) have shown high prediction accuracy in apple for different quality traits utilizing a factorial mating design (0.70–0.90). Imai et al (2019) reported that ssGBLUP predicts with higher accuracy (0.650, 0.519, and 0.666) than GBLUP (0.642, 0.432, and 0.655) for quality traits in citrus, viz., fruit weight, sugar content, and acid content from population where some individuals are not genotyped using information from genotyped related individuals, hence reducing the cost at hand.…”