Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications

Genome wide selection optimization in maize breeding

Maize is a staple crop and the most grown cereal worldwide. The expansion of this crop was possible due to efforts in management and breeding. In the breeding standpoint, advances were achieved in the release of hybrids presenting heterosis, field experimental design and analyses, establishment of heterotic patterns, and effective seed production and marketing. From the last decade on, advances in data analyses benefited from the surge of genotypic data, allowing the prediction of hybrids without being tested through genomic selection approaches. This study aims to convert a high-density SNP data set and use it in a genomic selection or predicting non- tested hybrids and non-observed environments, and for indicating most promising mating parent material for obtaining hybrids and inbred lines for ASI, EPP, FFT, GY, and MFT maize traits. For that, we ranked the SNPs according to their effects from a ME analyses and selected the minimum portion of markers that reached the plateau of prediction accuracy per chromosome, followed by eliminating the repeated markers between traits, and removing the ones tightly linked according to LD analyses. For the GS of hybrids and environments, three methods that comprised GCA and SCA main and interaction effects were fitted, and the prediction accuracy was assessed. The step of selecting parent material was performed according to PS, GS, and GM. The GM methods used the marker effects predicted in the previous GS step, and the 40 top- and bottom-performing crosses and their respective parent lines were selected for each trait. The selected SNPs maintained the accuracy for all traits under drought or well-watered conditions when compared to using full SNP set. For GWS of hybrids, Model 3 performed better for all traits when cross validation schemes had information of all environments (CV1 and CV2) in terms of prediction accuracy, and Model 2 performed better when there was missing information about environments (CV0 and CV00). The mating parents chosen for positive selection were different than the ones from negative selection, ensuring maximization of gains for hybrid and inbred lines development. The highest coincidences of selected parent lines occurred in GS-based methods (Methods 1, 3, 5, 7, 9, 11, 13, and 15), where parents were directly selected based on means or GCA/SCA (and interaction) values of their respective hybrids. The methods based on crosses simulations (Methods 2, 4, 6, 8, 10, 11, 12, 14, and 16) had moderate to low coincidences, but were consistent in indicating the best parent materials overall. GS- and GM-based parent selection results must be further compared to Method 17 (observed crosses) for an effective validation. PS, GS, and GM methods together must help in the decision making of selecting parent material for future crosses. These approaches must be further performed using other training populations. Keywords: Cross-validation. Hybrids. Inbred Lines. Prediction Accuracy. SNP Markers.O milho é o cereal mais cultivado em todo o mundo. A expansão dessa cultura foi possível devido aos esforços de manejo e melhoramento. Do ponto de vista do melhoramento, avanços foram alcançados na liberação de híbridos com heterose, delineamento e análises experimentais em campo, estabelecimento de padrões heteróticos e produção e comercialização efetiva de sementes. A partir da última década, avanços na análise de dados foram beneficiados pelo surgimento de dados genotípicos, permitindo a predição de híbridos sem serem testados por meio de abordagens de seleção genômica. Este estudo tem como objetivo converter um conjunto de dados de SNP de alta densidade e usá-lo em seleção genômica para predizer híbridos não testados e ambientes não observados, e indicar o material parental de acasalamento mais promissor para a obtenção de híbridos e linhagens para os caracteres ASI, EPP, FFT, GY e MFT em milho. Para isso, SNPs foram classificadas de acordo com seus efeitos estimados, e uma porção mínima de marcadores que atingiram o platô de acurácia de predição por cromossomo foram selecionados, seguida de uma eliminação de marcadores repetidos entre as características e remoção daqueles ligados de acordo com uma análise de LD. Para a GWS de híbridos e ambientes, três modelos que compreenderam os efeitos principais e de interação de GCA e SCA foram ajustados e a acurácia de predição foi avaliada. A etapa de seleção do material de parental foi realizada de acordo com PS, GS e GM. Os métodos de GM usaram os efeitos de marcadores preditos na etapa anterior de GS, e os 40 cruzamentos de desempenho superior e inferior e suas respectivas linhagens parentais foram selecionados para cada característica. Os SNPs selecionados mantiveram a acurácia de predição para todas as características em condição de déficit hídrico e irrigação, quando comparado ao uso de todos os marcadores disponíveis. Para GS de híbridos, o Modelo 3 teve melhor desempenho para todas as características quando os esquemas de validação cruzada tinham informações de todos os ambientes (CV1 e CV2) em termos acurácia, e o Modelo 2 teve melhor desempenho quando faltavam informações sobre os ambientes (CV0 e CV00). Os genitores escolhidos para seleção positiva foram diferentes dos genitores para seleção negativa, garantindo a maximização dos ganhos para o desenvolvimento de híbridos e linhagens endogâmicas. As maiores coincidências de linhagens parentais selecionadas ocorreram em métodos baseados em GS (Métodos 1, 3, 5, 7, 9, 11, 13 e 15), onde os pais foram selecionados diretamente com base em médias ou valores de GCA/SCA (e interação) de seus respectivos híbridos. Os métodos baseados em simulações de cruzamentos (Métodos 2, 4, 6, 8, 10, 11, 12, 14 e 16) tiveram coincidências moderadas a baixas, mas foram consistentes em indicar os melhores materiais de parentais em geral. Os resultados da seleção de pais baseados em GS e GM devem ser comparados com o Método 17 (cruzamentos observados) para uma validação eficaz. Os métodos PS, GS e GM juntos devem ajudar na tomada de decisão de seleção de material parental para futuros cruzamentos. Palavras-chave: Acurácia de Predição. Híbridos. Linhagens Endogâmicas. Marcadores SNP. Validação Cruzada

SNP validation for high seed protein content in soybean [Glycine max (L.) Merr.]

O objetivo deste estudo foi estimar o efeito de marcadores moleculares SNP sobre o fenótipo ( 2 ), determinar a herdabilidade dos conteúdos de proteína e óleo em grãos de soja e o coeficiente de correlação entre estes dois caracteres, e identificar as progênies superiores via seleção assistida e fenotipagem. Para isto, 271 RILs derivadas de F5 foram testadas em ensaios de campo em Capinópolis e Viçosa, em 2017, genotipadas, e fenotipadas para conteúdos de proteína e óleo via NIR. Os genótipos apresentaram diferenças significativas entre si e ao longo dos ambientes testados, sendo que os resultados de Capinópolis foram maiores para conteúdo de proteína das RILs (47,845 %) e os de Viçosa para o conteúdo de óleo das testemunhas (23,291 %). As herdabilidades foram maiores para a análise conjunta dos dados que para a análise 2 individual, em que ℎ í = 79,490 % e 72,810 %, e ℎ ó = 84,190 % e 79,520 %, 2 para Capinópolis e Viçosa, respectivamente; e ℎ í = 88,230 % e ℎ ó = 91,050 %, para análise conjunta. Os coeficientes de Correlação de Pearson para dados fenotípicos foram de . = -0,664 e ç = -0,587. Dos marcadores moleculares avaliados, ss56, ss62, ss115 e ss190 associaram para o conteúdo de proteína em Capinópolis; e ss56, ss62 e ss190 para este mesmo caráter em Viçosa. Os marcadores ss62, ss115 e ss190 associaram para o conteúdo de óleo em ambas as localidades. O marcador ss190 destacou-se ao apresentar 2 entre 25-29 % para ambos os conteúdos nas duas localidades. Efeito pleiotrópico de todos os marcadores foi observado, exceto para ss56, em que seu uso na seleção assistida promove o aumento do conteúdo de proteína e não onera o de óleo. Em termos práticos, as progênies superiores para alta proteína foram as que reuniram todos ou a maioria dos alelos favoráveis. As progênies 61-9, 78-38, 84-13 e 84-33 foram superiores para o conteúdo de proteína e em desempenho de campo, além de reunirem alelos favoráveis identificados via seleção assistida das marcas associadas neste estudo, justificando seu uso no programa de melhoramento.The objective of this research was to estimate the SNP effect over the phenotype ( 2 ), determine heritability related to seed proteín and oil content and the coefficient of correlation between these two traits, as well as to identify the superior lines through marker assisted selection and phenotyping. 271 F5-derived RILs were submitted to field trials in Capinopolis and Viçosa in 2017, genotyped, and phenotýped for protein and oil contents through NIR. The genotypes presented significant difference among them and along the environments, in which the mean of protein content from Capinopolis was higher for the RILs (47,845 %) and mean from oil content from Viçosa was higher for the checks (23,291 %). Heritabilities were higher for the joint analysis than to individual 2 ones, in which ℎ = 79,490 % and 72,810 %, and ℎ = 84,190 % and 79,520 %, 2 = 88,230 % and ℎ = 91,050 for Capinópolis and Viçosa, respectively; and ℎ %, for the joint data. Phenotypic Pearson Correlation were . = -0,664 and ç = -0,587. From the markers studied, ss56, ss62, ss115 and ss190 showed significant association to protein content in Capinopolis; and ss56, ss62 and ss190 for this same trait in Viçosa. The markers ss62, ss115 and ss190 showed significant association to oil content for both locations. SNP ss190 caught the attention by presenting 2 in a range of 25-29 % for both contents in all locations. Pleiotropic effect of all markers was observed, except for ss56. Its use in marker assisted selection promotes the increase of protein content without hampering oil rates. The transgressive lines for high protein were the ones that gathered all or the most of favorable alleles. The lines 61-9, 78-38, 84-13 e 84-33 were superior for protein content and field performance, and gathered favorable alleles identified through marker assisted selection of the significantly associated SNPs, warranting their use in the breeding program

Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans

The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications

Defence‐related enzymes in soybean resistance to target spot

Target spot, caused by the fungus Corynespora cassiicola, has become a serious foliar disease in soybean production in the Brazilian Cerrado. Information in the literature regarding the biochemical defence responses of soybean to C. cassiicola infection is rare. Therefore, the objective of this study was to determine the biochemical features associated with soybean resistance to target spot. The activities of chitinases (CHI), β‐1‐3‐glucanases (GLU), phenylalanine ammonia‐lyases (PAL), peroxidases (POX), polyphenol oxidases (PPO) and lipoxygenases (LOX), as well as the concentrations of total soluble phenolics (TSP) and lignin‐thioglycolic acid (LTGA) derivatives, were determined in soybean leaves from both a resistant (FUNDACEP 59) and a susceptible (TMG 132) cultivar. The target spot severity, number of lesions per cm2 of leaflet and area under the disease progress curve were significantly lower for plants from cv. FUNDACEP 59 compared to plants from cv. TMG 132. The GLU, CHI, PAL, POX and PPO activities and the concentration of LTGA derivatives increased significantly, whereas LOX activity decreased significantly on the leaves infected by C. cassiicola. Inoculated plants from cv. FUNDACEP 59 showed a higher PPO activity and concentrations of TSP and LTGA derivatives at 4 and 6 days after inoculation compared to plants from cv. TMG 132. In conclusion, the results of this study demonstrated that the defence‐related enzyme activities increased upon C. cassiicola infection, regardless of the basal level of resistance of the cultivar studied. The increases in PPO activity and concentrations of TSP and LTGA derivatives, but lower LOX activity, at early stages of C. cassiicola infection were highly associated with soybean resistance to target spot

Changes in the antioxidant system in soybean leaves infected by Corynespora cassiicola

Considering the importance of target spot, caused by the fungus Corynespora cassiicola, to reduce soybean yield in Brazil and that more basic information regarding the soybean−C. cassiicola interaction is needed, the present study aimed to investigate whether the cellular damage caused by C. cassiicola infection could activate the antioxidant system and whether a more efficient antioxidant system could be associated with an increase in soybean resistance to target spot. The activities of the antioxidant enzymes superoxide dismutase, catalase, peroxidase, ascorbate peroxidase, glutathione peroxidase, glutathione reductase, glutathione S-transferase as well as the concentrations of ascorbate (AsA), hydrogen peroxide (H2O2), superoxide (O2•−), and malondialdehyde (MDA) were measured in soybean plants from two cultivars differing in resistance to the pathogen. The number of lesions per square centimeter was significantly reduced by 14% in plants from cultivar Fundacep 59 compared with plants from cultivar TMG 132. The area under the disease progress curve was significantly lower, by 15%, in plants from Fundacep 59 than in plants from TMG 132. Generally, antioxidant enzyme activities and AsA concentration significantly increased in response to C. cassiicola infection in plants from both cultivars, however more prominent increases were recorded for plants from Fundacep 59. The concentrations of MDA, H2O2, and O2•− also increased, particularly for plants from TMG 132. The results from this study highlight the importance of a more efficient antioxidative system in the removal of reactive oxygen species generated in soybean plants during C. cassiicola infection, contributing to the resistance to target spot

A set of standard area diagrams to assess severity of frogeye leaf spot on soybean

A set of standard area diagrams (SADs) was developed and validated to aid visual assessment of severity of frogeye leaf spot (FLS) caused by Cercospora sojina. The SAD has eight color images of diseased leaflets with severity values that ranged from 0.1 to 39.9 %. The SAD was validated by a group of 20 raters [10 experienced (ER) and 10 inexperienced (IR)], who assessed the same set of 50 images twice, the first without SADs and the second using SADs as an aid. The SADs significantly improved accuracy [coefficients of bias (C b ) were 0.64 and 0.99 for IR and 0.98 and 0.99 for ER, without and with SADs, respectively], precision [correlation coefficients (r) were 0.89 and 0.95 for IR and 0.94 and 0.97 for ER, without and with SADs, respectively] and overall agreement [Lin’s concordance correlation coefficients (ρ c ) were 0.57 and 0.94 for IR and 0.92 and 0.97 for ER without and with SADs, respectively]. The estimates of severity of FLS were more reliable when using SADs. Both the inter-rater reliability (coefficient of determination, R:^2 ) and intra-class coefficient (ρ) were significantly increased by using SADs. Therefore, it is believed that the SADs proposed in the present study will be a useful tool to aid accurate, precise and reliable estimates of severity of FLS in experiments (e.g., fungicide screening, assessment of partial resistance of soybean genotypes to FLS) and to aid in decision-making purposes