Comparison of Marker-Based Genomic Estimated Breeding Values and Phenotypic Evaluation for Selection of Bacterial Spot Resistance in Tomato

Abstract

Bacterial spot affects tomato crops (Solanum lycopersicum) grown under humid conditions. Major genes and quantitative trait loci (QTL) for resistance have been described, and multiple loci from diverse sources need to be combined to improve disease control. We investigated genomic selection (GS) prediction models for resistance to Xanthomonas euvesicatoria and experimentally evaluated the accuracy of these models. The training population consisted of 109 families combining resistance from four sources and directionally selected from a population of 1,100 individuals. The families were evaluated on a plot basis in replicated inoculated trials and genotyped with single nucleotide polymorphisms (SNP). We compared the prediction ability of models developed with 14 to 387 SNP. Genomic estimated breeding values (GEBV) were derived using Bayesian least absolute shrinkage and selection operator regression (BL) and ridge regression (RR). Evaluations were based on leave-one-out cross validation and on empirical observations in replicated field trials using the next generation of inbred progeny and a hybrid population resulting from selections in the training population. Prediction ability was evaluated based on correlations between GEBV and phenotypes (r), percentage of coselection between genomic and phenotypic selection, and relative efficiency of selection (r/r). Results were similar with BL and RR models. Models using only markers previously identified as significantly associated with resistance but weighted based on GEBV and mixed models with markers associated with resistance treated as fixed effects and markers distributed in the genome treated as random effects offered greater accuracy and a high percentage of coselection. The accuracy of these models to predict the performance of progeny and hybrids exceeded the accuracy of phenotypic selection.

Evaluation of Genomic Prediction for Pasmo Resistance in Flax

“…RR-BLUP successfully recognized complex patterns with additive effects and delivered good GP in wheat disease resistance [50]. Furthermore, RR-BLUP has a clearcut computational efficiency [11,49,51,52]. In this study, GP accuracy does not vary in three different models ( Figure S1) and the RR-BLUP model with the 500 QTL markers and the 5-year mean PS produced high prediction accuracy and is therefore recommended for the prediction of PR in flax.…”

“…Therefore, diverse phenotypic and genetic variabilities within the flax core collection render it useful as a resource for breeding and as a TP for GP model construction. [11,14,42,[47][48][49]. For example, comparisons among RR-BLUP, Bayes-Cπ, and RKHSR showed no difference in accuracies in a wheat FHB study [19].…”

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

“…Genomic selection (GS), which uses genome-wide markers to predict breeding values, may greatly improve the selection gain in breeding programs for complex traits controlled by several minor genes. In last year, pioneer studies concerning the application of GS to transfer yield-related traits in tomato varieties from wild related species were reported [ 22 , 23 ] as well as to assess the potential of GS to increase soluble solids content and fruit weight in F1 tomato varieties [ 24 ] and to develop bacterial spot resistant tomato lines [ 25 ]. GS models were widely exploited for predicting phenotypes of progeny and parents, although the efficiency varied depending on the parental cross combinations and the selected traits [ 26 ].…”

“…The effectiveness of GEBVs for prediction was mainly demonstrated in polyploid wheat [ 27 , 52 , 53 , 54 , 55 , 56 , 57 ] but studies are also available for diploids rice [ 54 , 58 ], barley [ 59 , 63 ], soybean [ 60 , 61 ], maize [ 27 , 62 ] and tomato [ 22 , 23 , 24 , 25 ]. Lorenzana and Bernardo [ 59 ] obtained GEBV accuracies between 0.64 and 0.83 using 150 DHs (doubled-haploid) barley lines and 223 Restriction Fragment Length Polymorphism (RFLP) markers to improve grain yield and amylase activity.…”

Advances and Challenges in Bacterial Spot Resistance Breeding in Tomato (Solanum lycopersicum L.)

“…Duangjit et al [67] studied the potential of GS in tomato breeding programs to improve fruit quality traits in tomato. In the case of BS disease resistance, Liabeuf et al, [68] observed higher prediction accuracy through the GS model for resistance to X. euvesicatoria compared to phenotypic selection.…”