Kernel-based whole-genome prediction of complex traits: a review

Morota, Gota; Gianola, Daniel

doi:10.3389/fgene.2014.00363

Cited by 135 publications

(144 citation statements)

References 110 publications

(140 reference statements)

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“…However, recovering non-additive interaction among markers is an open field of research and the most successful results have been obtained through kernel-based methods (Howard et al 2014). Morota and Gianola (2014) and Gianola et al (2014) pointed out that most studies carried out so far suggest that whole-genome prediction coupled with combinations of kernels may capture non-additive variation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the linear kernel given by K = X X p [see the definition of G in (1.2)] can be used to reduce the dimensionality of the genotypic data and, hence, the number of parameters to be estimated VanRaden 2008). A comprehensive review of various kernel-based approaches for capturing genetic variation in the context of genomic-enabled prediction was recently published by Morota and Gianola (2014).…”

Section: Introductionmentioning

confidence: 99%

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Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Pérez-Elizalde

Cuevas

Pérez‐Rodríguez

et al. 2015

JABES

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One of the most widely used kernel functions in genomic-enabled prediction is the Gaussian kernel. Selection of the bandwidth parameter for kernel regression has generally been based on cross-validation. We propose a Bayesian method for estimating the bandwidth parameter h of a Gaussian kernel as the modal component of the joint posterior distribution of h and the form parameter ϕ. We present a theory for the Bayesian selection of h in a Transformed Gaussian Kernel (TGK) model and its application in two plant breeding datasets (maize and wheat) that were already predicted using the kernel averaging (KA) model in the context of Reproducing Kernel Hilbert Spaces (RKHS KA). We also compared the prediction accuracy of the proposed method with a model that also uses a Gaussian kernel and estimates the bandwidth parameter using a restricted maximum likelihood method (GK REML). Results for the wheat dataset show that the predictive ability of TGK was at least as good as the predictive ability of model RKHS KA, with TGK showing a significantly smaller Predictive Mean Squared Error (PMSE) than the other two approaches. The TGK model was statistically a better predictor than methods GK REML and RKHS KA in terms of mean PMSE and mean correlations in seven (out of 17) trait-environment combinations in the wheat dataset. Fewer differences were found between models for the maize data; the TGK model generally had similar or inferior prediction accuracy than GK REML and RKHS KA in various analyses. The superiority of GK REML over TGK based on mean PMSE was clear in seven maize traits.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Pérez-Elizalde

Cuevas

Pérez‐Rodríguez

et al. 2015

JABES

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“…Choosing an appropriate kernel, which can be interpreted as a relationship matrix among genotypes (i.e., individuals), is a central element of model specification in RKHS regression . Among all possible kernels, the Gaussian kernel has been extensively used and is assumed to implicitly portray the genetic effects including epistasis (Gianola and Van Kaam 2008;Morota and Gianola 2014). The exponential function involved in the Gaussian kernel is a nonlinear transformation of the additive inputs, which encodes a type of epistasis .…”

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confidence: 99%

Modeling Epistasis in Genomic Selection

Jiang¹,

2015

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Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species.KEYWORDS epistasis; genomic selection; genomic best linear unbiased prediction (G-BLUP); extended G-BLUP (EG-BLUP); reproducing kernel Hilbert space regression (RKHS); GenPred; shared data resource E PISTASIS has long been recognized as an important component in dissecting genetic pathways and understanding the evolution of complex genetic systems (Phillips 2008). It is hence a biologically influential component contributing to the genetic architecture of quantitative traits (Mackay 2014). The influence of epistasis on genome-wide QTL mapping ranges from limited (e.g., Buckler et al. 2009;Tian et al. 2011) to high (e.g., Carlborg et al. 2006;Würschum et al. 2011;Huang et al. 2014). These discrepancies can be explained by the complexities of the examined traits, which are controlled by many loci exhibiting small effects entailing a low QTL detection power. In addition, the estimation of QTL main and interaction effects is very likely biased (Beavis 1994), which makes it challenging to reliably elucidate the role of epistasis through genome-wide QTL mapping studies.Genomic selection has been suggested as an alternative to tackle complex traits that are regulated by many genes, each exhibiting a small effect (Meuwissen et al. 2001). Genomic selection approaches based on additive and dominance effects have been successfully applied to predict complex traits in human (Yang et al. 2010), animal (Hayes et al. 2009, and plant populations (Jannink et al. 2010;Zhao et al. 2015). Moreover, several genomic selection approaches have been developed to model both main and epistatic effects (Xu 2007;Cai et al. 2011;Wittenburg et al. 2011;Wang et al. 2012). While in some studies prediction accuracies increased (Hu et al. 2011), in others modeling epistasis adversely affected prediction accuracies (Lorenzana and Bernardo 2009).Despite these first attempts, epistasis is often ignored in genomic selection approaches using parametric models mainly because of the high associated computational load, especially if a large number of markers are available. An attractive solution to reduce the computational load is to extend genomic best linear unbiased prediction (G-BLUP) models (VanRaden 2008) by adding marker-based epistatic relationship matrices [extended genomic...

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“…MKLMM is based on LMM, similarly to many other popular methods for complex trait prediction (Meuwissen et al 2001;Habier et al 2011;Zhou et al 2013;Morota and Gianola 2014;Speed and Balding 2014;Moser et al 2015). Alternative methods such as decision tree ensembles, support vector machines, and regularized regression techniques (Hastie et al 2009) are also potential candidates for complex trait prediction.…”

Section: Wwwgenomeorgmentioning

confidence: 99%

“…In recent years, LMMs that can model higher-order interactions have also been investigated, typically under the name, reproducing kernel Hilbert space regression (RKHS) (Liu et al 2007(Liu et al , 2008Ober et al 2011;Gianola et al 2014;Morota and Gianola 2014;Tusell et al 2014;Akdemir and Jannink 2015;Jiang and Reif 2015). These works demonstrated improved prediction performance on several plant and animal species compared to simpler methods (Perez-Rodriguez et al 2012;Rutkoski et al 2012;Crossa et al 2013).…”

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confidence: 99%

Multikernel linear mixed models for complex phenotype prediction

Weissbrod

Geiger

2016

Genome Res.

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Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.

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Kernel-based whole-genome prediction of complex traits: a review

Cited by 135 publications

References 110 publications

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Selection of the Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction

Modeling Epistasis in Genomic Selection

Multikernel linear mixed models for complex phenotype prediction

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