Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six‐Row Barley

Lorenz, Aaron J.; Smith, Kevin P.; Jannink, Jean‐Luc

doi:10.2135/cropsci2011.09.0503

Cited by 219 publications

(243 citation statements)

References 43 publications

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“…This was expected from theoretical simulations (Meuwissen 2009), as well as from empirical work in wheat (Heffner et al 2011a,b), barley (Lorenz et al 2012), and oats . Within an individual biparental population, genomic prediction information can come from (i) variation in relatedness (i.e., Mendelian sampling) captured by the SNPs and (ii) cosegregation of loci linked to QTL (linkage information).…”

Section: Prediction Using Full Sibs (1a)supporting

confidence: 57%

Genomic Predictability of Interconnected Biparental Maize Populations

et al. 2013

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Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.G ENOMIC prediction or selection, initially proposed and rapidly implemented in animal breeding (Meuwissen et al. 2001), is increasingly applied in plant breeding (Bernardo and Yu 2009;Lorenz et al. 2011;Morrell et al. 2011). However, with more investigations in plant breeding applications, new challenges emerge, mainly as the result of the greater possibilities of genetic manipulation and reproduction modes in plants compared to animals. Genomic predictions within diverse populations (Crossa et al. 2010; Riedelsheimer et al. 2012a,b;Windhausen et al. 2012) largely overlap with the scenarios in animal breeding. Predicting crossbred performance in animals also has some similarities with the prediction of maize hybrids from fully homozygous inbred lines drawn from genetically distant heterotic pools (Technow et al. 2012). Apart from using mixtures of close and distant relatives, little overlap with the field of animal breeding however exists in the case of multiple related or unrelated biparental families of inbred lines with a size #200. The latter situation is investigated in this study, because it covers the most rele...

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Section: Prediction Using Full Sibs (1a)supporting

confidence: 57%

Genomic Predictability of Interconnected Biparental Maize Populations

et al. 2013

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“…Early generations usually consist of a large number of genotypes evaluated in a few environments with few replications. Advanced trials, on the other hand, consist of few genotypes evaluated in a large number of environments with more replications (Bernardo, 2010;Lorenz et al, 2012). This creates a high level of unbalance in the data sets that can be properly managed with mixed models using restricted maximum-likelihood estimates (Patterson and Thompson, 1971).…”

Section: Introductionmentioning

confidence: 99%

“…G enomic selection has proven highly successful, and plant breeding programs are implementing GS strategies as part of their breeding routines (Heffner et al, 2010;Lorenz et al, 2012;Hayes et al, 2013;Rutkoski et al, 2015). Genomic selection was first developed in animal breeding (Meuwissen et al, 2001) and has since been transferred into plant breeding (Heffner et al, 2009).…”

mentioning

confidence: 99%

“…Genomic selection was first developed in animal breeding (Meuwissen et al, 2001) and has since been transferred into plant breeding (Heffner et al, 2009). While most of the GS studies have focused on evaluating GS model performance (Bennewitz et al, 2009;Solberg et al, 2009;Heslot et al, 2012;de los Campos et al, 2013) and optimizing the training population in terms of population size (Lorenzana and Bernardo, 2009;Asoro et al, 2011;Lorenz et al, 2012), number of markers (Lorenzana and Bernardo, 2009;Asoro et al, 2011;Heffner et al, 2011), and its structure (Asoro …”

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confidence: 99%

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Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

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“…Prediction methods have been compared empirically for crops; for example, wheat Heffner et al 2011;Perez-Rodriguez et al 2012), maize Albrecht et al 2011;Riedelsheimer et al 2012;Zhao et al 2012;Crossa et al 2013), and barley (Lorenz et al 2012;Endelman et al 2014). Lorenzana and Bernardo (2009) comprehensively compared multiple methods using populations of maize, barley, and Arabidopsis, whereas Heslot et al (2012) used populations of maize, barley, wheat, and Arabidopsis; some of these populations were the same in both studies.…”

Section: Introductionmentioning

confidence: 99%

Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.)

et al. 2014

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Our simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits. Whole-genome prediction is used to predict genetic value from genome-wide markers. The choice of method is important for successful prediction. We compared nine methods using empirical data for eight phenological and morphological traits of Asian rice cultivars (Oryza sativa L.) and data simulated from real marker genotype data. The methods were genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Lasso, elastic net, random forest (RForest), Bayesian lasso (Blasso), extended Bayesian lasso (EBlasso), weighted Bayesian shrinkage regression (wBSR), and the average of all methods (Ave). The objectives were to evaluate the predictive ability of these methods in a cultivar population, to characterize them by exploring the area of applicability of each method using simulation, and to investigate the causes of their different accuracies for empirical traits. GBLUP was the most accurate for one trait, RKHS and Ave for two, and RForest for three traits. In the simulation, Blasso, EBlasso, and Ave showed stable performance across the simulated scenarios, whereas the other methods, except wBSR, had specific areas of applicability; wBSR performed poorly in most scenarios. For each method, the accuracy ranking for the empirical traits was largely consistent with that in one of the simulated scenarios, suggesting that the simulation conditions reflected the factors that affected the method accuracy for the empirical results. This study will be useful for genomic prediction not only in Asian rice, but also in populations from other crops with relatively small training sets and strong linkage disequilibrium structures.

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Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six‐Row Barley

Cited by 219 publications

References 43 publications

Genomic Predictability of Interconnected Biparental Maize Populations

Genomic Predictability of Interconnected Biparental Maize Populations

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.)

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