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Jean-luc Jannink - One of the best experts on this subject based on the ideXlab platform.

  • Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines
    PLoS Genetics, 2015
    Co-Authors: Jennifer Spindel, Bruno Collard, Parminder Virk, Gary Atlin, E. Redoña, Hasina Begum, Deniz Akdemir, Jean-luc Jannink
    Abstract:

    Genomic Selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying Genomic Selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that Genomic Selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.

  • Genomic Selection in Plant Breeding
    Methods of Molecular Biology, 2014
    Co-Authors: Mark A. Newell, Jean-luc Jannink
    Abstract:

    : Genomic Selection (GS) is a method to predict the genetic value of Selection candidates based on the Genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome. Unlike marker-assisted Selection, the GEBV is based on all markers including both minor and major marker effects. Thus, the GEBV may capture more of the genetic variation for the particular trait under Selection.

  • imputation of unordered markers and the impact on Genomic Selection accuracy
    G3: Genes Genomes Genetics, 2013
    Co-Authors: Jessica Rutkoski, Jean-luc Jannink, Jesse Poland, Mark E Sorrells
    Abstract:

    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.

  • multiple trait Genomic Selection methods increase genetic value prediction accuracy
    Genetics, 2012
    Co-Authors: Jean-luc Jannink
    Abstract:

    Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) Genomic Selection does not take advantage of this information. Multivariate Genomic Selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate Genomic Selection when a correlated high-heritability trait was available. Further, multiple-trait Genomic Selection had higher prediction accuracy than single-trait Genomic Selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait Genomic Selection were explored.

  • dynamics of long term Genomic Selection
    Genetics Selection Evolution, 2010
    Co-Authors: Jean-luc Jannink
    Abstract:

    Background Simulation and empirical studies of Genomic Selection (GS) show accuracies sufficient to generate rapid gains in early Selection cycles. Beyond those cycles, allele frequency changes, recombination, and inbreeding make analytical prediction of gain impossible. The impacts of GS on long-term gain should be studied prior to its implementation.

Le J Gouis - One of the best experts on this subject based on the ideXlab platform.

Mark E Sorrells - One of the best experts on this subject based on the ideXlab platform.

  • Genomic Selection in Wheat
    Applications of Genetic and Genomic Research in Cereals, 2019
    Co-Authors: Daniel W. Sweeney, Ella Taagen, Mark E Sorrells
    Abstract:

    Abstract Genomic prediction models have been trained for a large number of economically important traits in wheat, however to date there has only been one study that has reported realized gain from Genomic Selection. There have been several investigations that have modeled genotype by environment interaction and epistatic interactions. Genomic Selection for traits that are difficult or expensive to evaluate such as milling and baking quality is especially promising. The development of climate resilient wheat varieties is of increasing importance and Genomic Selection is a valuable new tool that breeders can use to more efficiently identify genotypes that tolerate environmental extremes. Genomic Selection provides the breeder with the opportunity to impose Selection on any quantitative trait of interest at any stage of the breeding cycle, giving more power and freedom to today’s breeders.

  • Genomic Selection for Crop Improvement: New Molecular Breeding Strategies for Crop Improvement
    2017
    Co-Authors: Rajeev K. Varshney, Manish Roorkiwal, Mark E Sorrells
    Abstract:

    Genomic Selection for Crop Improvement serves as handbook for users by providing basic as well as advanced understandings of Genomic Selection. This useful review explains germplasm use, phenotyping evaluation, marker genotyping methods, and statistical models involved in Genomic Selection. It also includes examples of ongoing activities of Genomic Selection for crop improvement and efforts initiated to deploy the Genomic Selection in some important crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders.

  • Genomic Selection for Small Grain Improvement
    Genomic Selection for Crop Improvement, 2017
    Co-Authors: Jessica Rutkoski, Jesse Poland, Jared Crain, Mark E Sorrells
    Abstract:

    In this chapter we present an overview of Genomic Selection (GS) research in the small grain cereals and interpret some of the results across studies where there is a growing body of information. We also provide the reader with approaches to implementation of GS in applied breeding programs and how various scenarios affect gain from Selection and cost relative to conventional breeding. Training population optimization is discussed as well as the factors that affect prediction accuracy. We conclude with comments on future research directions required to improve the efficiency of GS.

  • imputation of unordered markers and the impact on Genomic Selection accuracy
    G3: Genes Genomes Genetics, 2013
    Co-Authors: Jessica Rutkoski, Jean-luc Jannink, Jesse Poland, Mark E Sorrells
    Abstract:

    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.

  • Genomic Selection for crop improvement
    Crop Science, 2009
    Co-Authors: Elliot L. Heffner, Mark E Sorrells, Jean-luc Jannink
    Abstract:

    Despite important strides in marker technologies, the use of marker-assisted Selection has stagnated for the improvement of quantitative traits. Biparental mating designs for the detection of loci affecting these traits (quantitative trait loci [QTL]) impede their application, and the statistical methods used are ill-suited to the traits' polygenic nature. Genomic Selection (GS) has been proposed to address these deficiencies. Genomic Selection predicts the breeding values of lines in a population by analyzing their phenotypes and high-density marker scores. A key to the success of GS is that it incorporates all marker information in the prediction model, thereby avoiding biased marker effect estimates and capturing more of the variation due to small-effect QTL. In simulations, the correlation between true breeding value and the Genomic estimated breeding value has reached levels of 0.85 even for polygenic low heritability traits. This level of accuracy is sufficient to consider selecting for agronomic performance using marker information alone. Such Selection would substantially accelerate the breeding cycle, enhancing gains per unit time. It would dramatically change the role of phenotyping, which would then serve to update prediction models and no longer to select lines. While research to date shows the exceptional promise of GS, work remains to be done to validate it empirically and to incorporate it into breeding schemes.

Matias Kirst - One of the best experts on this subject based on the ideXlab platform.

  • accelerating the domestication of trees using Genomic Selection accuracy of prediction models across ages and environments
    New Phytologist, 2012
    Co-Authors: Marcio Fr Resende, Dario Grattapaglia, Marcos Deon Vilela De Resende, Juan J Acosta, Patricio Munoz, Gary F Peter, John M Davis, Matias Kirst
    Abstract:

    Summary •Genomic Selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate Genomic Selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. •Here, we evaluated the utility of Genomic Selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single-nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using Genomic random regression best linear unbiased predictor (BLUP). •Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The Selection efficiency per unit time was estimated as 53–112% higher using Genomic Selection compared with phenotypic Selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. •These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating Genomic Selection in breeding programs, as long as models are used at the relevant Selection age and within the breeding zone in which they were estimated.

  • Stability of Genomic Selection prediction models across ages and environments
    BMC Proceedings, 2011
    Co-Authors: Marcio Fr Resende, Dario Grattapaglia, Patricio R Muñoz Del Valle, Juan J Acosta, Marcos Dv Resende, Matias Kirst
    Abstract:

    Background A tree breeding program is characterized by long generation intervals which, over time, result in a much smaller number of breeding cycles when compared to annual crops. Moreover, most economically important traits in a tree-breeding program are quantitatively inherited, display low heritability and are expressed late in the life cycle. Genomic Selection (GS) is expected to be particularly valuable for tree species, leading to shorter generation intervals and improved genetic gain over time. The main factors that affect the accuracy of GS prediction models are the level of linkage disequilibrium (LD) in the training population, the training population size, the heritability of the trait and the number of QTL regulating its variation. However, it is yet largely unknown how stable prediction models are across environments and different ages. This knowledge is critical for tree breeders that wish to use Genomic Selection in their genetic improvement program. Here, we report the first assessment of the utility of Genomic Selection in a conifer species. We developed prediction models for growth traits measured at multiple sites, to evaluate the impact of genotype by environment interactions in their accuracy. Training populations were also measured over multiple ages and models were developed to assess their value in predicting breeding values later in the lifecycle.

Renaud Rincent - One of the best experts on this subject based on the ideXlab platform.