RESUMO

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 Recursos

RESUMO

KEY MESSAGE: In genomic recurrent selection, the more markers, the better because they buffer the linkage disequilibrium losses caused by recombination over cycles, and consequently, provide higher responses to selection. Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, studies that focus on long selective breeding cycles generally do not consider GS models with the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models [additive, additive-dominant, general (GCA), and this plus specific combining ability (GCA + SCA)]. Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or GCA + SCA for reciprocal.

Assuntos

Modelos Genéticos , Seleção Genética , Humanos , Genótipo , Melhoramento Vegetal , Genômica/métodos , Produtos Agrícolas/genética , Fenótipo

RESUMO

Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

RESUMO

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Assuntos

Biologia Computacional , Técnicas de Genotipagem , Modelos Genéticos , Animais , Quimera/genética , Biologia Computacional/métodos , Biologia Computacional/normas , Conjuntos de Dados como Assunto , Genoma , Estudo de Associação Genômica Ampla/métodos , Estudo de Associação Genômica Ampla/normas , Genômica/métodos , Genômica/normas , Genótipo , Técnicas de Genotipagem/métodos , Técnicas de Genotipagem/normas , Fenótipo , Padrões de Referência , Reprodutibilidade dos Testes , Seleção Genética , Especificidade da Espécie , Zea mays/classificação , Zea mays/genética

RESUMO

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

Assuntos

Interação Gene-Ambiente , Modelos Genéticos , Agricultura , Genômica , Genótipo , Fenótipo , Software

RESUMO

The objective of this study was to determine the relationship between heterosis and genetic divergence for phosphorus use efficiency (PUE) in tropical maize. It was used two groups of genitors, each consisting of seven lines, contrasting with each other in the nitrogen and phosphorus use efficiency. It was obtained 41 hybrid combinations between these groups, which were evaluated in low phosphorus. Randomized complete block design with two replications was used. For obtaining the components of variance and the breeding values were used REML/BLUP method. In the genotyping of the parental lines were used 80 microsatellite markers. Through the correlation between genetic distance obtained by the markers and specific combining ability it was not possible to determine with accuracy by molecular markers, the crosses that produced hybrids with the highest heterosis for PUE. Thus, is possible to conclude that there is no relationship between genetic divergence and heterosis for phosphorus use efficiency and its components in tropical maize.

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O objetivo deste estudo foi determinar a relação entre divergência genética e heterose para a eficiência no uso de fósforo (EUP) em milho tropical. Utilizaram-se dois grupos de genitores, compostos de sete linhagens cada, contrastantes entre si para as eficiências no uso de nitrogênio e fósforo. Foram obtidas 41 combinações híbridas entre esses grupos, as quais foram avaliadas em baixo fósforo. Usou-se o delineamento em blocos ao acaso com duas repetições. A obtenção dos componentes de variância e valores genéticos foi realizada via REML/BLUP e, para genotipagem das linhagens genitoras, foram utilizados 80 marcadores microssatélites. Através da correlação entre a distância genética obtida pelos marcadores e a capacidade específica de combinação, observou-se não ser possível a determinação com acurácia, via marcadores moleculares, dos cruzamentos que produziram os híbridos com as maiores heteroses para EUP. Com isso, é possível concluir que não há relação entre divergência genética e heterose para eficiência no uso de fósforo e seus componentes em milho tropical.