Understanding the genetic and environmental basis of genotype × environment interaction (G×E) is of fundamental importance in plant breeding. If we consider G×E in the context of genotype × year interactions (G×Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice (Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations (indica and tropicaljaponica). We also sought to explain G×E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in subtropical and temperate rice growing areas.

Genotype × environment interaction (G × E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype × year‐interaction (G × Y) is a relevant component of G × E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G × Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross‐validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs.

Rice ( Oryza sativa L.) is one of the most important staple food crops in the world; however, there has recently been a shift in consumer demand for higher grain quality. Therefore, understanding the genetic architecture of grain quality has become a key objective of rice breeding programs. Genome‐wide association studies (GWAS) using large diversity panels have successfully identified genomic regions associated with complex traits in diverse crop species. Our main objective was to identify genomic regions associated with grain quality and to identify and characterize favorable haplotypes for selection. We used two locally adapted rice breeding populations and historical phenotypic data for three rice quality traits: yield after milling, percentage of head rice recovery, and percentage of chalky grain. We detected 22 putative quantitative trait loci (QTL) in the same genomic regions as starch synthesis, starch metabolism, and cell wall synthesis‐related genes are found. Additionally, we found a genomic region on chromosome 6 in the tropical japonica population that was associated with all quality traits and we identified favorable haplotypes. Furthermore, this region is linked to the OsBEI gene that codes for a starch branching enzyme I, which is implicated in starch granule formation. In tropical japonica , we also found two putative QTL linked to OsBEII , OsDEP1 , and OsDEP2 . Our study provides an insight into the genetic basis of rice grain chalkiness, yield after milling, and head rice, identifying favorable haplotypes and molecular markers for selection in breeding programs.

Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype‐specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega‐environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single‐step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank‐change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME.

BackgroundWeedy rice (Oryza sativa L.) is a noxious form of cultivated rice (O. sativa L.) associated with intensive rice production and dry seeding. A cost-efficient strategy to control this weed is the Clearfield rice production system, which combines imidazolinone herbicides with mutant imidazolinone-resistant rice varieties. However, imidazolinone resistance mutations can be introgressed in weedy rice populations by natural outcrossing, reducing the life span of the Clearfield technology. Timely and accurate detection of imidazolinone resistance mutations in weedy rice may contribute to avoiding the multiplication and dispersion of resistant weeds and to protect the Clearfield system. Thus, highly sensitive and specific methods with high throughput and low cost are needed. KBioscience’s Allele Specific PCR (KASP) is a codominant, competitive allele-specific PCR-based genotyping method. KASP enables both alleles to be detected in a single reaction in a closed-tube format. The aim of this work is to assess the suitability and validity of the KASP method for detection in weedy rice of the three imidazolinone resistance mutations reported to date in rice.ResultsValidation was carried out by determining the analytical performance of the new method and comparing it with conventional allele-specific PCR, when genotyping sets of cultivated and weedy rice samples. The conventional technique had a specificity of 0.97 and a sensibility of 0.95, whereas for the KASP method, both parameters were 1.00.ConclusionsThe new method has equal accuracy while being more informative and saving time and resources compared with conventional methods, which make it suitable for monitoring imidazolinone-resistant weedy rice in Clearfield rice fields