Bettina Lado

Wheat ( Triticum aestivum L.), a crucial cereal crop for global food security, faces growing challenges from climate change. Future production requires varieties that are resilient to environmental extremes and fluctuations. The goal of this study was to assess strategies to increase selection response through genomic selection in wheat by integrating genotypic‐specific phenology‐derived environmental covariates (ECs) and random regression models (RRM) in multi‐environment trials. We analyzed phenotypic and genomic data from 1683 genotypes from 2010 to 2020 across 71 environments using 45 ECs derived from vegetative, reproductive, and grain‐filling phenological phases. Seven key ECs were selected via partial least squares regression to model genotype by environment interaction (GEI) and evaluate their integration in three different genomic prediction scenarios (CV0, CV1, and CV2). Genomic best linear unbiased prediction models (GBLUP), GBLUP models with GEI (GBLUP _{G × E} ) modeled as a factor analytic (FA) model, and RRM were compared for their predictive ability performance. RRM with three ECs outperformed GBLUP achieving 50%–100% higher accuracy in CV1 and CV2. The FA exhibited the highest accuracy overall for CV2 but not for CV1. At least one RRM model improved predictions in >89% of environments when predicting new, un‐phenotyped environments. Integrating ECs into the RRM enhances genomic prediction by effectively capturing the GEI with a limited number of covariates.

Increasing selection accuracy for parental lines in the early stages of a breeding program can significantly shorten the breeding cycle and accelerate genetic gain. Public breeding programs have limited resources, which in many cases implies not having enough funds for genomic selection. In this study, we propose a strategy that allows leveraging a data source that is often overlooked: the breeding program's historical data. We evaluated the impact of incorporating historical data through multi‐environment analysis for selection at early stages of evaluation in a public breeding program. Two distinct breeding goals were assessed: line advancement and parent selection. For the first objective we used available phenotypic data, and for the second objective we leveraged existing pedigree records in addition to the phenotypic data. The evaluation strategy employed replicated the breeding program's structure, accounting for the timing of data availability. We investigated five prediction strategies, starting from single trial analysis, incorporating one or 5 years of historical data, and including or not a genotype by environment interaction term. To compare the prediction strategies, we considered grain yield phenotypic variance partition and predictive ability. Joint analysis of multiple trials and environments led to more accurate estimations of variance components and higher predictive ability for early selection of parents and line advancement. Additionally, we found that modeling genotype by environment interaction did not consistently enhance predictive ability. This study highlights the benefits of joint analyses of multi‐environment trials for early selection of parents and lines in breeding programs.

Strawberry, a profitable crop, adapts well to diverse environments. This study evaluated the stability of fruit quality in different strawberry varieties produced as green plants without chilling during the harvest season in northern Uruguay. The focus was on understanding the impact of harvest date versus agricultural practices (different orchards and growers) on key fruit quality variables such as color, firmness, sugars, and acidity. Results indicated that the INIA Yrupé variety showed greater stability in external coloration and sugar content across harvest dates, suggesting that standardizing fruit coloration is viable under challenging conditions. INIA Guapa consistently met minimum requirements for soluble solids and ratio, with stable acidity and red coloration. Firmness was positively correlated with ratio (r = 0.81) and negatively impacted by rainfall 2–3 days before harvest. The findings suggest that harvest date has a more significant impact on INIA Yrupé fruit quality than growing conditions or practices, with variability observed in firmness and ratio but minimal variation in fruit coloration. These insights highlight the importance of harvest timing for maintaining stable fruit quality traits, which is crucial for breeding programs and ensuring consistent sensory quality and crop profitability.

The inclusion of genomic selection (GS) in a breeding program allows us to shorten breeding time, increasing the genetic gain per unit of time. GS can be used to predict the best crosses, accounting for allelic complementation between parents and/or predicting individuals’ performance. Mixed models used in GS are a powerful tool to include individuals´ correlations, environments and traits, allowing the use of the whole available information. The goal of this study is the optimization of GS as a tool in the wheat breeding program, to manage the genotype by environment interaction, multi-trait prediction, and best crosses selection. Historical data from the national wheat breeding program were used; data were obtained from 35 environments, 1495 experimental lines, evaluated for yield; and 695 lines evaluated for baking quality. Multi-trait and multi-environment models were compared modeling correlations to predict new individuals, and individuals partially phenotyped. Best crosses were also predicted considering or not considering the variance between parents. Results showed that multi- environment and multi-trait models were useful to optimize the breeding program resources, by predicting partially phenotyped lines. Finally, crosses selection considering parent’s mean performance was as good as including the variance in selection for grain yield, but not for quality traits.

Key Message Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. Abstract Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid‐parent value and variance prediction accounting for linkage disequilibrium (V _LD ) or assuming linkage equilibrium (V _LE ). After predicting the mean and the variance of each cross, we selected crosses based on mid‐parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( Triticum aestivum L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid‐parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses.

Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.