Marker-assisted selection (MAS) exploits the markers associated with traits of interest for selecting lines with superior alleles for developing improved lines. However use of MAS is restricted to simple traits due to its inability to handle complex traits. Advancements in genomics technologies have been able to dramatically reduce the cost of genotyping, enabling the use of genome-wide marker data for selecting lines with higher breeding value. Genomic selection (GS), a modern breeding approach that uses genome-wide marker data to estimate the breeding value and has the potential to address the complex traits. GS exploits the genotyping and phenotyping data on a training population to train the prediction models to calculate the genomic estimated breeding value (GEBV). GS has the capability to reduce selection cycle duration and increase selection accuracy, intensity, efficiency, and gains per unit of time, thereby enhancing the rate of genetic gains. Availability of cost-effective genotyping platforms has enabled the cost-effective generation of large-scale genotyping data, facilitating the deployment of GS in several crop species. This chapter provides an introduction to the book, highlighting the basic and advanced principles of GS breeding and its applications for crop improvement

Roorkiwal, M

Sorrells, M E

Varshney, R K

ICRISAT Open Access Repository

Chapter 1Genomic Selection for Crop Improvement:An IntroductionRajeev K. Varshney, Manish Roorkiwal, and Mark E. Sorrells1.1 IntroductionProducing sufficient food to meet the demand of vastly growing population anderadication of rural poverty is one of the critically important issues that the world isfacing. At the current pace, the world population is expected to cross the mark ofnine billion people by 2050 adding further pressure to already exhausted foodproduction systems. Considering the increasingly volatile climate, it will be diffi-cult to maintain the crop production in conjugation with the demand, resulting inincreased food prices affecting people who already spend the highest percentage oftheir disposable income on food. In addition to climate change, limited waterresource availability and poor soil health have the potential to restrict food cropproduction. Furthermore, with increases in the world population, the availability ofagricultural land is decreasing. Under these constraints, to meet the rising demandfor food, agricultural production must increase by an estimated 50% without greatlyincreasing water usage or expanding the total land area dedicated to agriculture.Smallholder farmers, especially from underdeveloped and developing countrieswith limited access to agricultural inputs or agricultural markets, are likely to beaffected by rising production costs and climate volatility. As per the UnitedR.K. Varshney (*) • M. RoorkiwalInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru,Telangana, Indiae-mail: R.K.Varshney@CGIAR.ORGM.E. SorrellsDepartment of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853,USA© Springer International Publishing AG 2017R.K. Varshney et al. (eds.), Genomic Selection for Crop Improvement,DOI 10.1007/978-3-319-63170-7_11Nations’ estimates, more than 790 million people globally do not have access tosufficient nutritious food (https://sustainabledevelopment.un.org/sdg2), posing athreat to achieving the Sustainable Development Goal (SDG) target of zero hunger(universal access to safe, nutritious, and sufficient food at all times of the year).In the event of these challenges, there is a need to look for new ways of breedingfor food crops and other plant species by using modern technologies. Modernbreeding approaches that have the capability to reduce breeding cycle time providemore precision in selection, and more efficient use of genetic variation can beexploited to increase the rate of genetic gains in breeding programs. The rapiddecline in the cost of sequencing and genotyping has led to the development of newtools and strategies that can transform the way we breed plant species. In the past,the cost of genotyping restricted the regular use of markers in breeding. In mostcases a limited number of markers for the target regions were used for selecting thelines based on presence or absence of agriculturally important alleles. Developmentof crop varieties using conventional breeding approaches has been effective buttime-consuming and labor-intensive. Recent advances in the next-generationsequencing (NGS) technologies have been able to reduce the cost of genotypingand sequencing. This has enabled the use of the high-throughput and cost-effectivehigh-density genotyping. These low-cost genotyping platforms have accelerated theuse of markers in the breeding programs using genome-wide approaches (Varshneyet al. 2014).Integrating genomic tools with conventional breeding can have a major impactfor dealing with current and future environmental challenges more efficiently. Insuch conditions, germplasm, genetic, and genomic resources are mandatory in allplant species for rapid genetic gains in productivity of these species using decisionsupport tools. First-generation molecular breeding approaches (marker-assistedbackcrossing, marker-assisted recurrent selection) followed a lengthy process fordeveloping mapping populations for identification of markers linked to quantitativetrait loci (QTL) for a few simple traits. The majority of economically importanttraits such as drought tolerance and yield are polygenic in nature and controlled bymultiple genes with small effects. In order to improve complex traits, such asdrought tolerance and yield, the modern breeding approach, genomic selection(GS) (Meuwissen et al. 2001), can be deployed which specifically aims at improv-ing quantitative traits by using genome-wide marker data without requiring iden-tification of markers associated with QTL for traits of interest. GS uses a “trainingpopulation” of individuals that have been both phenotyped and genotyped to train aprediction model for calculating genomic estimated breeding values (GEBVs).Subsequently by using this model, GEBVs can be calculated for untested individ-uals from a “candidate population”, and selection candidates (SCs) for makingcrosses or for advanced yield trials can be identified. Although GEBVs do notidentify the function of the underlying QTLs/genes for the trait, they are anexcellent selection criterion (Jannink et al. 2010). GS attempts to capture the totaladditive genetic variance with genome-wide marker coverage and effect estimates(Rutkoski et al. 2011). Therefore, selection of an individual without phenotypicdata can be performed based on the individual’s predicted breeding value.2 R.K. Varshney et al.The models can be used to calculate GEBVs that help the breeder to identifyoffspring that will be good parents in the next generation, based solely on genotypicinformation about an existing line. The use of GEBVs in the context of genome-wide prediction promises to help accelerate the rate of genetic gain in breeding.The purpose of this book is to bring up-to-date information on GS breeding andits application for crop species improvement. The editors believe that this book canserve as ready reference for geneticists and crop breeders. This chapter introducesthe book and provides a summary of different chapters included in the book.1.2 Methodologies and Models for GSThe first step toward deploying GS in crop breeding is to define a training set, whichshould be closely related to the selection candidate population. Chapter 2 entitled“Training population design and resource allocation for genomic selection in cropbreeding” provides detailed information about composition and optimization oftraining population design related to population and trait architecture. In thischapter, Aaron Lorenz and Liana Nice highlight the importance of the trainingpopulation design for predicting the breeding value of lines. The chapter focuses onthe process to select a calibration set (training population) for model training andoptimizes the resource allocation for field trials. With the advent of new technol-ogies, it has become possible to collect phenotyping data in a more precise mannerwith decreased error and increased efficiency and in larger quantities. NGS tech-nologies are contributing to a continuing decrease in the genotyping cost and areenabling the prediction of breeding value using genome-wide marker profiling. Thischapter also discusses the possible resource allocation in terms of the number ofreplications for calibration of GS models vs allocation of more plots for modeltraining and allocation of plots within and across environment replication.The Chap. 3 entitled “Derivation of linear models for quantitative traits byBayesian estimation with Gibbs sampling” contributed by Akihiro Nakaya andSachiko Isobe provides detailed information about construction of a predictionmodel using a linear model. Model parameters are determined using the Bayesianestimation with Gibbs sampling providing a theoretical background sufficient toimplement practical software for the model construction. The chapter also providesa sample output by the implemented software. The chapter describes the differentprediction models including linear models, one-marker model, two-marker modelwithout interactions, and two-marker model with interactions to predict the traitvalue of a sample using the environment types and genotypes. Prediction of the traitvalues of samples based on their genetic and environmental factors is explainedusing a prediction model that describes the relationship between the explanatoryfactors observed in the samples and the trait values. This chapter suggests thatdefining a prediction model for the target trait enables the selection to be based onthe predicted trait values, making it an essential part of genomic selection. Whenthe number of markers is greater than the number of samples, the prediction model1 Genomic Selection for Crop Improvement: An Introduction 3will be distorted. In order to address the issue related to model overfitting, detailedinspection of the prediction model is necessary, and strategy based on the linearmixed models and the Bayesian estimation may be useful in the prediction of traitvalues of samples.Montesinos-Lo´pez and colleagues highlighted recent advances in models forgenomic-enabled prediction developed for ordinal categorical and count data inChap. 4 entitled “Bayesian genomic-enabled prediction models for ordinal andcount data”. Authors used these two models on simulated as well as a real datasetusing Bayesian framework suggesting that models used are a good alternative foranalyzing ordinal and count data in the context of genomic-enabled prediction.Tested models have an advantage to perform an exact logistic or probit ordinalregression without having to do approximations to perform a logistic ordinalregression. Genotype (G) and environment (E) interaction is expected to affectthe prediction accuracies, and therefore modelling G E in the context of genomic-enabled prediction plays a central role in crop breeding for the selection ofcandidate genotypes. In order to best use GS models, understanding the data typebeing analyzed is important before deciding on the modelling approach to beemployed.1.3 GS in Field Crop BreedingGS has been used or is being used in several crop breeding programs. This bookincludes three chapters on applications offering both constraints and opportunitiesof GS in crop breeding. The Chap. 5 entitled “Genomic selection for small grainsimprovement” by Rutkoski and colleagues presents an overview of GS efforts beingundertaken in the small grain cereals. Authors in the chapter have explaineddifferent approaches for implementation of GS in applied breeding programs.A total of 40 GS studies have been undertaken so far in small grains includingwheat, barley, oat, rye, durum wheat, perennial ryegrass, and intermediate wheat-grass. This chapter also discusses the factors affecting the GS prediction accuraciesin small grains and highlights the applicability of GS for analyzing and predictingG  E. They have discussed various scenarios affecting gain from selection andcost relative to conventional breeding. Authors discussed the cost-benefit ratio fordeploying GS in cereal crops.In Chap. 6 entitled “Current status and prospects of genomic selection inlegumes”, Jain and colleagues from ICRISAT provide an update on molecularbreeding in legumes and describe the ongoing GS efforts in some legume-breedingprograms including soybean, alfalfa, pea, chickpea, and groundnut. Legumes havewitnessed significant progress in the field of genomics and genetics in the pastdecade, and efforts to deploy MAS have yielded some success for developingsuperior legume varieties. However, as expected, MAS has not been that successfulfor addressing complex traits such as drought and yield and therefore, efforts todeploy GS in legume breeding were initiated. Authors have suggested that it is time4 R.K. Varshney et al.for other legumes to start deployment of GS in those breeding programs to achieve ahigher rate of genetic gain.Hybrid breeding has been successful over varietal improvement in several crops.Schulthess and colleagues from the Leibniz Institute of Plant Genetics and CropPlant Research (IPK), Germany, describe the basic concepts of hybrid breeding anddeployment of GS methods to simplify the philosophy underlying GS with hybridbreeding in Chap. 7, “Genomic selection in hybrid breeding”. Authors haveexplained the basic concepts relevant to hybrid breeding including dominance,heterosis, combining abilities, and heterotic groups and patterns. The chapter alsodescribes the deployment of GS for hybrid genotypes using cross-validated predic-tion accuracy, accommodating dominance effects within the GS model and otherGS approaches employed in hybrid breeding. Deployment of GS in hybrid breedingis very challenging as many variables impact in hybrid breeding as compared topureline breeding. Authors propose an integrated plan with multidisciplinary skillsof breeders, scientists, and technicians before implementing GS in hybrid breeding.1.4 GS for Improvement of Clonal Crops and Tree SpeciesBreeding in clonal crops and tree species is different from field crops. Therefore,Gemenet and Khan in Chap. 8 entitled “Opportunities and challenges toimplementing genomic selection in clonally propagated crops” discuss issuesrelated to deployment of GS for improving the rate of genetic gain in clonalcrops. Authors highlight conventional breeding approaches for clonal crops thatinvolve crossing and planting of true seed plants in different generations followedby evaluation of clones for several generations, making it a time- and resource-consuming process. Therefore, GS-based selection of true seed plants can expeditethe breeding process. The chapter also describes the challenges including modellingof genetic effects and heritability, linkage disequilibrium between markers andQTLs, genetic architecture of traits, size of training population, and number ofgenerations following training model to deploy GS in clonal crops. For instance, GSmodels generally handle additive effects and assume dominance and epistaticeffects as part of the residual which is not the case for clonally propagated crops,as dominance and epistatic effects play an important role along with additive effectsand need special consideration. Therefore, for clonal crops, GS models with thecapability to include additive, dominance, and epistatic genetic effects need to beemployed for analysis.For tree species, Dario Grattapaglia from EMBRAPA Genetic Resources andBiotechnology, Brazil, provides perspectives of genomic selection and a compre-hensive discussion on the factors relevant to GS in tree breeding in Chap. 9, “Statusand perspectives of genomic selection in forest tree breeding”. The chapter high-lights the potential of GS in enhancing the rate of genetic gain in a tree breedingprogram by reducing the selection cycle. In the case of a tree breeding program, thelong generation time typically necessary to complete a full breeding cycle can be1 Genomic Selection for Crop Improvement: An Introduction 5reduced by genotyping young seedlings and predicting their phenotype instead ofwaiting for long a breeding cycle of 4–20 years or more. The authors have compiledand presented all GS experimental studies in forest trees along with their keyattributes and performance of predictive abilities for different traits in the chapter.1.5 SummaryAs can be seen from the introduction of eight different chapters, GS, a modernbreeding approach, is gaining popularity and becoming the choice for manybreeders for improving complex traits. The book provides up-to-date informationabout models, methodologies, factors affecting prediction accuracy, and someexamples of deployment of GS for crop improvement. This book will serve asreference for users that provides basic as well as advanced understanding aboutGS. The book is expected to serve as a useful review for users that explains thegermplasm to be used, phenotyping, marker genotyping methods, and statisticalmodels involved in GS. It also includes some examples of ongoing activities ofgenomic selection in cereal, legume, clonal crop, and tree species.ReferencesJannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory topractice. Brief Funct Genomics 9:166–177Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829Rutkoski JE, Heffner EL, Sorrells ME (2011) Genomic selection for durable stem rust resistance inwheat. Euphytica 179:161–173Varshney RK, Terauchi R, McCouch SR (2014) Harvesting the promising fruits of genomics:applying genome sequencing technologies to crop breeding. PLoS Biol 12(6):e10018836 R.K. Varshney et al.

Genomic Selection for Crop Improvement: An Introduction

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