Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Genetic control of yield under reproductive‐stage drought stress was studied in a population of 436 random F3–derived lines from a cross between the upland rice (Oryza sativa L.) cultivars Vandana and Way Rarem. Screening was conducted under upland conditions at IRRI during the dry seasons of 2005 and 2006. Lines were evaluated in drought stress and nonstress trials in both years to identify QTL contributing to drought resistance. For QTL detection, a set of random lines and the highest and lowest‐yielding lines under both stress and nonstress conditions were genotyped by 126 SSR markers. A QTL (qtl12.1) with a large effect on grain yield under stress was detected on Chromosome 12 in both years. The whole population was genotyped for additional markers on Chromosome 12, allowing QTL localization to a 10.2 cM region between SSR markers RM28048 and RM511. Under stress conditions, the locus also increased harvest index, biomass yield, and plant height while reducing the number of days to flowering. Under nonstress conditions, qtl12.1 did not significantly affect any trait. The additive effect of this QTL on grain yield under stress was 172 kg ha−1 per year over the 2 yr of testing, representing 47% of the average yield under stress and explaining 51% of the genetic variance. The yield‐increasing allele was derived from the susceptible parent, Way Rarem, suggesting an epistatic effect. This is the first QTL reported in rice having a large and repeatable effect on grain yield under severe drought stress in the field.

Key message The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. Abstract The breeder’s equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

No abstract

Plant breeding, using the combined potential of conventional, molecular and genetically modified technologies, will provide cultivars with greatly enhanced nutrient and water-use efficiency, enhanced tolerance to heat and drought, resistance to diseases and appropriate end-use and nutritional quality, and, possibly most important, increased ability to cope with the increasing extremes in temperature and precipitation occurring at one location over years. Modern crop cultivars developed by seed companies, international crop research centres and national breeding programmes often exhibit very wide geographical adaptation, as well as broad adaptation to the range of environmental and management conditions that occur within and between a target population of environments, or megaenvironments. To identify such cultivars, multi-location testing done by the International Maize and Wheat Improvement Center (CIMMYT) and the International Rice Research Institute (IRRI) remains the most efficient system. International evaluation networks based on exchange of and free access to germplasm and multi-location testing are therefore a cornerstone in the strategies and efforts to develop wheat, rice and maize germplasm that is adapted to the increasingly variable growing conditions encountered due to global climate change. Information from such trials must be combined with information from managed stress trials. Wide performance adaptation is essential to respond to global climate change, to the vagaries of spatial heterogeneity within farmers' fields and their production input management efficacies, and from unpredictable temporal climatic seasonal variability.