Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.

By G. de los Campos, D. Gianola, Guilherme J. M. Rosa, Kent A. Weigel, Jose Crossa. Genetics Research, 2010

318 citations recorded by the source corpus.

Prediction of genetic values is a central problem in quantitative genetics. Over many decades, such predictions have been successfully accomplished using information on phenotypic records and family structure usually represented with a pedigree. Dense molecular markers are now available in the genome of humans, plants and animals, and this information can be used to enhance the prediction of genetic values. However, the incorporation of dense molecular marker data into models poses many statistical and computational challenges, such as how models can cope with the genetic complexity of multi-factorial traits and with the curse of dimensionality that arises when the number of markers exceeds the number of data points. Reproducing kernel Hilbert spaces regressions can be used to address some of these challenges. The methodology allows regressions on almost any type of prediction sets (covariates, graphs, strings, images, etc.) and has important computational advantages relative to many parametric approaches. Moreover, some parametric models appear as special cases. This article provides an overview of the methodology, a discussion of the problem of kernel choice with a focus on genetic applications, algorithms for kernel selection and an assessment of the proposed methods using a collection of 599 wheat lines evaluated for grain yield in four mega environments.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/970347549babca2b9e2de7818baa70f9f2e0ed92

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Citations in the corpus

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP (2011, methodology): “Rather than modeling epistatic interactions directly, which is challenging because of the combinatorial complexity, an alternative approach is to capture them through an appropriate kernel function (Gianola and van Kaam, 2008; Piepho, 2009; de los Campos et al., 2010).”
Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers (2010, background): “Addressing the optimal choice of bandwidth parameter is not within the scope of this study; see de los Campos et al. (2010).”
Genomic Prediction of Genetic Values for Resistance to Wheat Rusts (2012, methodology): “Several nonparametric approaches have been proposed for making phenotypic predictions considering these complexities (de los Campos et al., 2010; Long et al., 2011a; Ober et al., 2011).”
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (2012, background): “…has made it possible to use genomic selection (GS) for the prediction of genetic values (Meuwissen et al., 2001) in plants (Bernardo and Yu, 2007; de los Campos et al., 2009, 2010; Crossa et al., 2010, 2011; Pérez et al., 2010) and animals (Hayes and Goddard, 2010; de los Campos et al., 2009).”
Genomic Selection and Prediction in Plant Breeding (2011, methodology): “Similar results were found by de los Campos et al. (2009a, 2010), perhaps because there is some redundancy between regression on the pedigree and regression on the markers (e.g., Habier, Fernando, & Deckkers 2009).”
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding (2013, methodology): “Alternatively the regression function could be represented using semiparametric approaches (Gianola et al. 2006; de los Campos et al. 2010a) such as reproducing kernel Hilbert spaces (RKHS) regressions or neural networks (NN).”