Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model

Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included

Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Secondly, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7,608 non-phenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for non-phenotyped accessions, with no non-phenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions

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

Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Secondly, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7,608 non-phenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for non-phenotyped accessions, with no non-phenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models—six non-G × E and three G × E models—on three basic cross-validation (CV) schemes— in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)— in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0- Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits— grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)—, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains

Combining phenotypic and genomic data to improve prediction of binary traits

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here ‘main traits’) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or ‘phenotypes’) that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any phenotypic variables. We develop a new statistical technique that ensures appropriate representation from both the secondary traits and the phenotypic variables for optimal prediction. When two data types (markers and secondary traits) are available, we achieve improved prediction of a binary trait by two steps that are designed to ensure that a significant intrinsic effect of a phenotype is incorporated in the relation before accounting for extra effects of genotypes. First, we sparsely regress the secondary traits on the markers and replace the secondary traits by their residuals to obtain the effects of phenotypic variables as adjusted by the genotypic variables. Then, we develop a sparse logistic classifier using the markers and residuals so that the adjusted phenotypes may be selected first to avoid being overwhelmed by the genotypes due to their numerical advantage. This classifier uses forward selection aided by a penalty term and can be computed effectively by a technique called the one-pass method. It compares favorably with other classifiers on simulated and real data

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska–Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14–71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27–123%, 27–148%, and 65–165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14–33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration