Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
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An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.Abstract:
Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.read more
Citations
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Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction
Arne De Coninck,Bernard De Baets,Drosos Kourounis,Fabio Verbosio,Olaf Schenk,Steven Maenhout,Jan Fostier +6 more
TL;DR: It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework, and it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data.
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Assessing Predictive Properties of Genome-Wide Selection in Soybeans
TL;DR: This study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set, and the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB.
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Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel
J. Martin Sarinelli,J. Paul Murphy,Priyanka Tyagi,James B. Holland,Jerry W. Johnson,Mohamed Mergoum,Richard Esten Mason,Ali Babar,Stephen A. Harrison,Russell Sutton,Carl A. Griffey,Gina Brown-Guedira +11 more
TL;DR: The utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes is demonstrated, with the greatest gains coming from combinations of multiple genes.
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Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species
Laura M. Zingaretti,Salvador A. Gezan,Luís Felipe V. Ferrão,Luis F. Osorio,Amparo Monfort,Patricio R. Munoz,Vance M. Whitaker,Miguel Pérez-Enciso +7 more
TL;DR: It is shown that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance, as well as using a parameterization capable of taking into account these non-linear effects.
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Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).
Akio Onogi,Osamu Ideta,Yuto Inoshita,Kaworu Ebana,Takuma Yoshioka,Masanori Yamasaki,Hiroyoshi Iwata +6 more
TL;DR: Simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits and evaluate the predictive ability of these methods in a cultivar population.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.