Jessica Rutkoski

Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing

Genomic selection (GS) uses genomewide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping‐by‐sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS we discovered 41,371 single nucleotide polymorphisms (SNPs) in a set of 254 advanced breeding lines from CIMMYTs semiarid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate‐normal expectation‐maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, although no significant differences were observed in the accuracy of genomic‐estimated breeding values (GEBVs) among imputation methods. Genomic‐estimated breeding value prediction accuracies with GBS were 0.28 to 0.45 for grain yield, an improvement of 0.1 to 0.2 over an established marker platform for wheat. Genotyping‐by‐sequencing combines marker discovery and genotyping of large populations, making it an excellent marker platform for breeding applications even in the absence of a reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low cost of GBS make this an ideal approach for genomics‐assisted breeding.

Imputation of unordered markers and the impact on genomic selection accuracy.

Genomic selection, a breeding method that promises to accelerate rates of genetic gain, requires dense, genome-wide marker data. Genotyping-by-sequencing can generate a large number of de novo markers. However, without a reference genome, these markers are unordered and typically have a large proportion of missing data. Because marker imputation algorithms were developed for species with a reference genome, algorithms suited for unordered markers have not been rigorously evaluated. Using four empirical datasets, we evaluate and characterize four such imputation methods, referred to as k-nearest neighbors, singular value decomposition, random forest regression, and expectation maximization imputation, in terms of their imputation accuracies and the factors affecting accuracy. The effect of imputation method on the genomic selection accuracy is assessed in comparison with mean imputation. The effect of excluding markers with a large proportion of missing data on the genomic selection accuracy is also examined. Our results show that imputation of unordered markers can be accurate, especially when linkage disequilibrium between markers is high and genotyped individuals are related. Of the methods evaluated, random forest regression imputation produced superior accuracy. In comparison with mean imputation, all four imputation methods we evaluated led to greater genomic selection accuracies when the level of missing data was high. Including rather than excluding markers with a large proportion of missing data nearly always led to greater GS accuracies. We conclude that high levels of missing data in dense marker sets is not a major obstacle for genomic selection, even when marker order is not known.

Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat

Fusarium head blight (FHB) resistance is quantitative and difficult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used U.S. cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (MLR) (fixed effects). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including genomewide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. Genomic selection accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON, marker plus trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genomewide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be used to improve accuracies in some cases.

Genomic selection for durable stem rust resistance in wheat

Inheritance of stem rust (caused by Puccinia graminis f. sp. tritici) resistance in wheat can be either qualitative or quantitative. While quantitative disease resistance is believed to be more durable, it is more difficult to evaluate if it is expressed only in mature plants, i.e. adult plant resistance (APR). Marker-assisted selection (MAS) methods for APR would be useful; however, the multigenic nature of APR impedes the use of MAS efforts that aim to pyramid only a few target genes. A promising alternative is genomic selection (GS), which utilizes genome-wide marker coverage to predict genotypic values for quantitative traits. In turn, GS can reduce the selection cycle length of a breeding program for traits like APR that could take several seasons to generate reliable phenotypes. In this paper, we describe the GS process for use in crop improvement, both specifically for APR and in general. We also propose a GS–based wheat breeding scheme for quantitative resistance to stem rust that, when compared to current breeding schemes, can reduce cycle time by up to twofold and facilitates pyramiding of major genes with APR genes. Thus, GS could be an important tool for achieving the Borlaug Global Rust Initiative’s (BGRI) goal of developing durable stem rust resistance in wheat.

Impact of Marker Ascertainment Bias on Genomic Selection Accuracy and Estimates of Genetic Diversity

Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS) is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology) and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS) accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis.

Genomic selection for quantitative adult plant stem rust resistance in wheat

Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome‐wide profile using genotyping‐by‐sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G‐BLUP), Bayesian Lasso (BL), and Bayes Cπ (BCπ). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G‐BLUP was more accurate than MLR with Sr2 linked markers (p‐value = 0.12), and ordinary G‐BLUP (p‐value = 0.15). Incorporating seedling phenotype information as fixed effects in G‐BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.

Genetic gain from phenotypic and genomic selection for quantitative resistance to stem rust of wheat

Stem rust of wheat (Triticum aestivum L.) caused by Puccinia graminis f. sp. tritici Eriks. and E. Henn. is a globally important disease that can cause severe yield loss. Breeding for quantitative stem rust resistance (QSRR) is important for developing cultivars with durable resistance. Genomic selection (GS) could increase rates of genetic gain for quantitative traits, but few experiments comparing GS and phenotypic selection (PS) have been conducted. Our objectives were to (i) compare realized gain from GS based on markers only with that of PS for QSRR in spring wheat using equal selection intensities; (ii) determine if gains agree with theoretical expectations; and (iii) compare the impact of GS and PS on inbreeding, genetic variance, and correlated response for pseudo‐black chaff (PBC), a correlated trait. Over 2 yr, two cycles of GS were performed in parallel with one cycle of PS, with each method replicated twice. For GS, markers were generated using genotyping‐by‐sequencing, the prediction model was initially trained using historical data, and the model was updated before the second GS cycle. Overall, GS and PS led to a 31 ± 11 and 42 ± 12% increase in QSRR and a 138 ± 22 and 180 ± 70% increase in PBC, respectively. Genetic gains were not significant but were in agreement with expectations. Per year, gains from GS and PS were equal, but GS led to significantly lower genetic variance. This shows that while GS and PS can lead to equal rates of short‐term gains, GS can reduce genetic variance more rapidly. Further work to develop efficient GS implementation strategies in spring wheat is warranted.

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.

Efficient Use of Historical Data for Genomic Selection: A Case Study of Stem Rust Resistance in Wheat

Genomic selection (GS) is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP) with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat (Triticum aestivum L.) to investigate the utility of a historical TP (TPH) compared with a population‐specific TP (TPPS), the potential for TPH optimization, and the utility of TPH data when close relative data is available for training. We found that, depending on the population size, a TPPS was 1.5 to 4.4 times more accurate than a TPH, and TPH optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TPH subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.

Harnessing Diversity in Wheat to Enhance Grain Yield, Climate Resilience, Disease and Insect Pest Resistance and Nutrition Through Conventional and Modern Breeding Approaches

Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen and pests pose a major concern for increasing wheat production globally. Triticeae species comprising of primary, secondary, and tertiary gene pools represent a rich source of genetic diversity in wheat. The conventional breeding strategies of direct hybridization, backcrossing and selection have successfully introgressed a number of desirable traits associated with grain yield, adaptation to abiotic stresses, disease resistance, and bio-fortification of wheat varieties. However, it is time consuming to incorporate genes conferring tolerance/resistance to multiple stresses in a single wheat variety by conventional approaches due to limitations in screening methods and the lower probabilities of combining desirable alleles. Efforts on developing innovative breeding strategies, novel tools and utilizing genetic diversity for new genes/alleles are essential to improve productivity, reduce vulnerability to diseases and pests and enhance nutritional quality. New technologies of high-throughput phenotyping, genome sequencing and genomic selection are promising approaches to maximize progeny screening and selection to accelerate the genetic gains in breeding more productive varieties. Use of cisgenic techniques to transfer beneficial alleles and their combinations within related species also offer great promise especially to achieve durable rust resistance.