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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.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

Two populations of interspecific introgression lines (ILs) in a common recurrent parent were developed for use in pre-breeding and QTL mapping. The ILs were derived from crosses between cv Curinga, a tropical japonica upland cultivar, and two different wild donors, Oryza meridionalis Ng. accession (W2112) and Oryza rufipogon Griff. accession (IRGC 105491). The lines were genotyped using genotyping-by-sequencing (GBS) and SSRs. The 32 Curinga/ O. meridionalis ILs contain 76.73 % of the donor genome in individual introgressed segments, and each line has an average of 94.9 % recurrent parent genome. The 48 Curinga/ O. rufipogon ILs collectively contain 97.6 % of the donor genome with an average of 89.9 % recurrent parent genome per line. To confirm that these populations were segregating for traits of interest, they were phenotyped for pericarp color in the greenhouse and for four agronomic traits—days to flowering, plant height, number of tillers, and number of panicles—in an upland field environment. Seeds from these IL libraries and the accompanying GBS datasets are publicly available and represent valuable genetic resources for exploring the genetics and breeding potential of rice wild relatives.

Population 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 sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.

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.

Results presented for 2 traits, 9 incidence matrices and 3 k-fold cross validation experiments. https://doi.org/10.1371/journal.pone.0154976.g001 thumbnail Download: * PPT PowerPoint slide * PNG larger image * TIFF original image Fig 3. The results for days to flowering (FL), plant height (PH), panicle weight (PW) and grain yield (YLD) and 35 incidence matrices are presented (a, b, c, d and e: minor allele frequency (MAF) thresholds of ≥ 10%, ≥ 7.5%, ≥ 5%, ≥ 2.5% and ≥ 0.01%). Supporting Information Legends S6 Fig.: Variation in the prediction accuracy of plant height (PH) according to the composition of the training (TP) and validation (TV) populations.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

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 less than or equal to 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.

Two populations of interspecific introgression lines (ILs) in a common recurrent parent were developed for use in pre-breeding and QTL mapping. The ILs were derived from crosses between cv Curinga, a tropical japonica upland cultivar, and two different wild donors, Oryza meridionalis Ng. accession (W2112) and Oryza rufipogon Griff. accession (IRGC 105491). The lines were genotyped using genotyping-by-sequencing (GBS) and SSRs. The 32 Curinga/O. meridionalis ILs contain 76.73 % of the donor genome in individual introgressed segments, and each line has an average of 94.9 % recurrent parent genome. The 48 Curinga/O. rufipogon ILs collectively contain 97.6 % of the donor genome with an average of 89.9 % recurrent parent genome per line. To confirm that these populations were segregating for traits of interest, they were phenotyped for pericarp color in the greenhouse and for four agronomic traits-days to flowering, plant height, number of tillers, and number of panicles-in an upland field environment. Seeds from these IL libraries and the accompanying GBS datasets are publicly available and represent valuable genetic resources for exploring the genetics and breeding potential of rice wild relatives.

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.