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A unified and comprehensible view of parametric and kernel methods for genomic prediction with application to rice

Jacquin Laval, Cao Tuong-Vi, Ahmadi Nourollah. 2016. A unified and comprehensible view of parametric and kernel methods for genomic prediction with application to rice. Frontiers in Genetics, 7 (145), 16 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
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Quartile : Q2, Sujet : GENETICS & HEREDITY

Abstract : One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel “trick” concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available. (Résumé d'auteur)

Mots-clés Agrovoc : Oryza, Génome, Méthode statistique, Technique de prévision, Interaction génique, génomique, Analyse de régression, Modèle mathématique, Oryza sativa

Mots-clés libres : Economic prediction, Parametric, Semi-parametric, Non-parametric, Kernel “trick”, Epistasis

Classification Agris : F30 - Plant genetics and breeding
U10 - Mathematical and statistical methods

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

  • Jacquin Laval, CIRAD-BIOS-UMR AGAP (FRA)
  • Cao Tuong-Vi, CIRAD-BIOS-UMR AGAP (FRA)
  • Ahmadi Nourollah, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-0072-6285

Source : Cirad-Agritrop (https://agritrop.cirad.fr/581439/)

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