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Abstract Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

AbstractPopulation breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa00:20:3

Since 1996, the collaborative project between CIRAD (Agricultural Research Centre for International Development) and CIAT (Centro Internacional de Agricultura Tropical) has gradually phased out intra-specific tropical japonica conventional crossbreeding activities and concentrated on rice genetic base broadening. The development and enhancement by recurrent selection of upland rice composite populations are the new breeding strategies. Using a recessive male-sterile gene (ms), the development of rice population was made easier. In Colombia, upland basic composite populations were enhanced using two recurrent selection-breeding methods: mass recurrent selection and S2 progenies evaluation. At each enhancement cycle, fertile plants are selected for the development of segregating lines and progeny selection using the conventional pedigree method. The most advanced lines are evaluated in yield trials by the project and in collaboration with the Colombian research organization – Corpoica Regional 8, in Villavicencio, Colombia. Promising lines having good adaptation to acid soils were identified. One promising line, extracted from the first cycle of recurrent selection of the composite population PCT-4, shows a higher yield potential with the same earliness of the best commercial check (Línea 30), developed by conventional crossbreeding. KEY-WORDS: Upland rice; recurrent selection; composite population; promising line. Em colaboração, desde 1996, CIRAD (Centre de Coopé-ration Internacionale en Recherche Agronomique pour le Dévelop-pment) e CIAT (Centro Internacional de Agricultura Tropical) desenvolvem atividades de melhoramento intra-populacional, para ampliar a base genética do arroz cultivado. O desenvolvimento de populações base (compostos) de arroz de terras altas, por meio de seleção recorrente, compõem as novas estratégias para se alcançar esse objetivo. Usando-se um gene recessivo de macho-esterilidade (ms), essas populações têm sido desenvolvidas facilmente. Na Colômbia, essas populações de arroz têm sido melhoradas por dois procedimentos de seleção recorrente: seleção massal e de progênies S2. Em cada ciclo de seleção, plantas férteis são selecionadas para o desenvolvimento de linhagens segregantes e para a seleção de progênies, usando-se o método convencional de pedigree. As linhagens avançadas são avaliadas em ensaios de rendimento, com apoio do projeto e em colaboração com a empresa colombiana de pesquisa – Corpoica Regional 8, em Villavicencio, Colômbia. Linhagens promissoras de boa adaptação a solos ácidos foram identificadas. Uma destas linhagens, extraída a partir do primeiro ciclo de seleção recorrente da população base PCT-4, mostra alto potencial de produtividade e mesmo nível de precocidade da melhor testemunha comercial (Línea 30), desenvolvida por melhoramento convencional. PALAVRAS-CHAVE: Oryza sativa; arroz de terras altas; seleção recorrente; composto; população base.

[This corrects the article DOI: 10.1371/journal.pone.0136594.].

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.