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BACKGROUND: Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. RESULTS: The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. CONCLUSIONS: Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.
Assuntos
Interação Gene-Ambiente , Zea mays , Zea mays/genética , Genoma de Planta/genética , Modelos Genéticos , Seleção Genética , Fenótipo , Genótipo , Genômica/métodos , Alocação de RecursosRESUMO
Sweetpotato is a highly heterozygous hybrid, and populations of orange-fleshed sweetpotato (OFSP) have a considerable importance for food security and health. The objectives were to estimate heterosis increments and response to selection in three OFSP hybrid populations (H1) developed in Peru for different product profiles after one reciprocal recurrent selection cycle, namely, H1 for wide adaptation and earliness (O-WAE), H1 for no sweetness after cooking (O-NSSP), and H1 for high iron (O-HIFE). The H1 populations were evaluated at two contrasting locations together with parents, foundation (parents in H0), and two widely adapted checks. Additionally, O-WAE was tested under two environmental conditions of 90-day and a normal 120-day harvest. In each H1, the yield and selected quality traits were recorded. The data were analyzed using linear mixed models. The storage root yield traits exhibited population average heterosis increments of up to 43.5%. The quality traits examined have exhibited no heterosis increments that are worth exploiting. The storage root yield genetic gain relative to the foundation was remarkable: 118.8% for H1-O-WAE for early harvest time, 81.5% for H1-O-WAE for normal harvest time, 132.4% for H1-O-NSSP, and 97.1% for H1-O-HIFE. Population hybrid breeding is a tool to achieve large genetic gains in sweetpotato yield via more efficient population improvement and allows a rapid dissemination of globally true seed that is generated from reproducible elite crosses, thus, avoiding costly and time-consuming virus cleaning of elite clones typically transferred as vegetative plantlets. The population hybrid breeding approach is probably applicable to other clonally propagated crops, where potential for true seed production exists.
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In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers' risk from year-to-year environmental variation in India's wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.
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We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.
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Nile tilapia (Oreochromis niloticus) is the major fish species produced in Brazil, a country with a vast territory and great climate diversity. This study assessed the effects of the genotype × environment interaction on heritability estimates and selection responses in Nile tilapia (Tilamax strain) cultivated in earthen ponds and net cages. The weight at harvest, trunk length, and head percentage of 4400 individuals were determined. Trait heritabilities were higher in pond fish (0.27-0.52) than in caged fish (0.09-0.33). Genetic correlations between farming systems were lower than 0.5 for the three traits. The rank position of the top 10 families differed according to the environment, as did the response to direct and indirect selection. The results revealed significant genotype × environment effects on the heritability of Nile tilapia farmed under different systems.
Assuntos
Peso Corporal/genética , Interação Gene-Ambiente , Tilápia/genética , Animais , Aquicultura , Brasil , Genótipo , Fenótipo , Tilápia/crescimento & desenvolvimentoRESUMO
Abstract Background: Knowledge of genetic correlations and the economics of traits are essential to decide which traits should be used as selection criteria. Objective: To estimate heritabilities and genetic, environmental, and phenotypic correlations, and direct (DRS) and correlated (CRS) responses to selection by scrotal circumference (SC), frame score (FS), and yearling weight (YW) of Mexican Charolais (CH), and Charbray (CB) young bulls. Methods: Actual SC, height and YW records (10,078 for CH, and 500 for CB) were adjusted to 365 d. The 0.0505 adjustment factor recommended by the Beef Improvement Federation was used to obtain the 365-d adjusted SC for both breeds. Height and age records were used to obtain FS. Data were analyzed using a three-trait animal model. The animal model for each trait included bull breed, contemporary group (groups of young bulls born in the same herd, year, and season of the year), and age of dam as a linear covariate as fixed effects, and direct additive genetic and residual as random effects. Results: Heritability estimates for SC, FS and YW were 0.21 ± 0.04, 0.25 ± 0.04, and 0.29 ± 0.04, respectively. The genetic correlation between YW with SC was 0.37 ± 0.16, and between YW with FS was 0.42 ± 0.16. The estimate of genetic correlation between SC and FS was low and positive (0.15 ± 0.14). The DRS was 0.38 cm, 0.18 units, and 8.30 kg for SC, FS and YW. The CRS was 0.16 cm, and 0.08 units for SC and FS from indirect selection on YW. Conclusions: Direct selection for YW is expected to be effective. Indirect selection for SC and FS based on YW would not be expected to be as effective as direct selection for improving SC and FS.
Resumen Antecedentes: el conocimiento de las correlaciones genéticas y el aspecto económico de las características son necesarios para decidir qué características usar como criterios de selección. Objetivo: estimar las heredabilidades y correlaciones genéticas, ambientales y fenotípicas, y respuesta directa (DRS) y correlacionada (CRS) a la selección por circunferencia escrotal (SC), talla corporal (FS), y peso al año (YW) de toros jóvenes mexicanos Charolais (CH), y Charbray (CB). Métodos: registros (10.078 para CH y 500 para CB) de SC, altura y YW se ajustaron a 365 d. El factor de ajuste de 0,0505 recomendado por la Beef Improvement Federation se usó para obtener la SC ajustada a 365 d para ambas razas. Registros de altura y edad del animal se usaron para calcular FS. Los datos se analizaron usando un modelo animal para tres características. El modelo animal para cada característica incluyó raza del toro, grupo contemporáneo (grupos de toros jóvenes nacidos en el mismo hato, año y época del año) y edad de la madre como covariable lineal como efectos fijos, y el genético aditivo directo y el error como efectos aleatorios. Resultados: los estimadores de heredabilidad de SC, FS y YW fueron 0,21 ± 0,04, 0,25 ± 0,04 y 0,29 ± 0,04, respectivamente. La correlación genética de YW con SC fue 0,37 ± 0,16, y de YW con FS fue 0,42 ± 0,16. El estimador de la correlación genética entre SC y FS fue bajo y positivo (0,15 ± 0,14). La DRS fue 0,38 cm, 0,18 unidades, y 8,30 kg para SC, FS y YW. La CRS fue 0,16 cm y 0,08 unidades para SC y FS al seleccionar YW. Conclusiones: se espera que la selección directa de YW sea efectiva. La selección indirecta de SC y FS basada en YW no se espera que sea tan efectiva como la selección directa para mejorar SC y FS.
Resumo Antecedentes: o conhecimento das correlações genéticas, e aspecto econômico de as características são necessário para decidir que características usar como critérios de seleção. Objetivo: estimar herdabilidades e correlações genéticas, ambientais e fenotípicas, e resposta direta (DRS), e correlacionada (CRS) à seleção do perímetro escrotal (SC), escore de frame (FS), e peso ao ano de idade (YW) de touros jovens mexicanos Charolês (CH), e Charbray (CB). Métodos: registros (10.078 para CH e 500 para CB) de SC, altura e YW foram ajustados a 365 d. O fator de ajuste 0,0505 recomendado por a Beef Improvement Federation foi usado para obter o SC ajustado aos 365 d para ambas raças. Registros de altura na garupa e idade do animal foram usados para obter o FS. Os dados foram analisados usando um modelo animal para três características. O modelo animal para cada característica incluiu raça do touro, grupo contemporâneo (grupos de touros jovens nascidos no mesmo fazenda, ano e época do ano) e idade materna como covariável linear como efeitos fixos, e genético aditivo direto e o erro como efeitos aleatórios. Resultados: as estimativas de herdabilidade para SC, FS e YW foram 0,21 ± 0,04, 0,25 ± 0,04 e 0,29 ± 0,04, respetivamente. A correlação genética do YW com SC foi 0,37 ± 0,16, e de YW com FS foi 0,42 ± 0,16. A estimativa da correlação genética entre SC e FS foi baixa e positiva (0,15 ± 0,14). A DRS foi 0,38 cm, 0,18 unidades, e 8,30 kg para SC, FS e YW. A CRS foi 0,16 cm e 0,08 unidades para SC e FS al selecionar YW. Conclusões: espera-se que a seleção direta do YW seja eficaz. A seleção indireta de SC e FS com base no YW não se espera que seja tão efetiva como a seleção direta para melhorar SC e FS.
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The efficiency of recurrent selection was assessed in obtaining common bean (Phaseolus vulgaris L) plant lines resistant to the phytopathogenic fungi Phaeoisariopsis griseola. The base bean population was obtained from the partial diallel between seven lines with carioca-type grains and 10 sources of resistance to P. griseola. The plants most resistant to the pathogen were selected in the F2 (S0) generation of the populations (C-0). The best S0:1 plants that presented carioca-type grains were intercrossed to obtain cycle I (C-I). The same procedure was adopted to obtain cycles C-II to C-V. In each recurrent selection cycle, S0:1 progenies selected were also assessed in experiments carried out in Lavras, Brazil, always using as check the Carioca MG (susceptible to P. griseola) and Pérola (tolerant) cultivars. The response to selection for resistance to the pathogen was estimated from the general mean of the S0:1 progenies from each selective cycle compared to the susceptible check Carioca MG. The estimated gain was 6.4 percent per cycle and the indirect response in grain yield by selection for resistance to the pathogen was 8.9 percent per cycle. The variability detected among the progenies in the last selective cycles enabled the prediction of additional responses to recurrent selection.