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

Improving predictive ability in sparse testing designs in soybean populations

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments

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

Use of family structure information in interaction with environments for leveraging genomic prediction models

The characterization of genomes with great detail offered by the modern genotyping platforms have opened a venue for accurately predicting the genotype-by-environment interaction (GE) effects of untested genotypes in different environmental conditions. Already developed statistical models have shown the advantages of including the GE interaction component in the prediction context using molecular markers, pedigree, or both. In order to leverage the family information of highly structured populations when pedigree data is not available, we developed a model that uses the family membership instead. The proposed model extends the reaction norm model by including the interaction between families and environments (FE). A representative fraction of a soybean Nested Association Mapping population (16,187 grain yield records) comprising 38 bi-parental families (1358 genotypes) observed in 18 environments (2011, 2012, and 2013) was used to contrast the proposed model with three conventional prediction models. Two cross- validation scenarios (prediction of tested [CV2] and untested [CV1] genotypes) with a twofold design (50% for training and testing sets) were used for mimicking prediction situations that breeders face in fields. Results showed that the family factor in interaction with environments explains a sizable amount of the phenotypic variability. This helped to improve the predictive ability with respect to the main effects model (GBLUP) around 41% (CV2) and 49% (CV1), and about 17% with respect to the conventional reaction norm model. The inclusion of the FE term not only improved the global results but also significantly increased the prediction accuracy of those environments where the conventional models showed a very poor performance. These results show the importance of taking into consideration the family structure existing in breeding programs for improving the selection strategies in multi-parental populations

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model

Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction

The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model

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

Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data

Genomic selection (GS) has revolutionized breeding strategies by predicting the rank performance of post-harvest traits via implementing genomic prediction (GP) models. However, predicting pre-harvest traits in unobserved environments might produce serious biases. In soybean, days to maturity (DTM) represents a crucial stage with a significant impact on yield potential; thus, genotypes must be carefully selected to ensure latitudinal adaptation in this photoperiod-sensitive crop species. This research assessed the use of daylength for predicting DTM in unobserved environments (CV00). A soybean dataset comprising 367 genotypes spanning nine families of the Soybean Nested Association Mapping Panel (SoyNAM) and tested in 11 environments (year-by-location combinations) was considered in this study. The proposed method (CB) returned a root-mean-square error (RMSE) of 5.2 days, a Pearson correlation (PC) of 0.66, and the predicted vs. observed difference in the environmental means (PODEM) ranged from −3.3 to 4.5 days; however, in the absence of daylength data, the conventional GP implementation produced an RMSE of 9 days, a PC of 0.66, and a PODEM range from −14.7 to 7.9 days. These results highlight the importance of dissecting phenotypic variability (G × E) based on photoperiod data and non-predictable environmental stimuli for improving the predictive ability and accuracy of DTM in soybeans

Improving predictive ability in sparse testing designs in soybean populations.

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments

Assessing the Genotype-by-Environment G×E Interaction in Desi Chickpea via the Bayesian Additive Main Effects and Multiplicative Interaction Model

Chickpea is the second most important legume crop in pulses, and its performance is greatly influenced by environmental factors inducing a change in the response patterns, complicating the selection of the best cultivar(s). The genotype-by-environment (G×E) patterns of a chickpea dataset (yield and days to emergence DTE) of 36 lines evaluated in 12 environments in Pakistan were assessed in this study. The interaction patterns were evaluated using the Bayesian Additive Main Effects and Multiplicative Interaction (AMMI) model. For yield, the 95% highest posterior density (HPD) regions showed that none of the genotypes (G) were found to be stable since these did not include the null point (0, 0), while for the environments, only E-8 and E-10 included the origin. In contrast, for DTE 11, genotypes included the null point being considered stable for this trait; however, none of the environments included the origin. These results suggest that considering both traits, the genotypes G2, G6, and G17 are the best genotypes across environments, while environments E-8 and E-10 were identified as favorable to all genotypes. Based on the obtained results, the abovementioned genotypes can be forwarded for further processing to be released as commercial varieties