An assessment of linkage disequilibrium in <scp>H</scp>olstein cattle using a <scp>B</scp>ayesian network

Morota, Gota; Valente, Bruno D.; Rosa, Guilherme J. M.; Weigel, K.A.; Gianola, Daniel

doi:10.1111/jbg.12002

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…The diffusion kernel defines the distance between two data points on graphs, namely vertices, and projects this information into a more interpretable space. As shown in the context of modeling linkage disequilibrium [40], various graph structures can be used to represent sets of discrete random variables, such as genotypes. Coupled with the representer theorem, the diffusion kernel allows casting underlying graph structures into a regression on the real line under a Hilbert space.…”

Section: Discussionmentioning

confidence: 99%

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

et al. 2013

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BackgroundArguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel.ResultsWe sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible.ConclusionsIt is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance.

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Section: Discussionmentioning

confidence: 99%

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

et al. 2013

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“…Furthermore, S SS encodes the LD patterns between the SNPs as measured by the squared allelic correlation r 2 . This has been shown to be useful in exploring complex LD patterns in an inbred Holstein cattle population, albeit with a discrete BN (Morota et al 2012) and measuring LD in a way that is closer to D and D9 (Falconer and Mackay 1995). Such patterns are reflected in the BN through V, providing an intuitive representation of LD as well as of genetic effects on phenotypes as a single, coherent whole.…”

Section: Methodsmentioning

confidence: 99%

“…Their modular nature makes them ideal for analyzing large marker profiles. As far as SNPs are concerned, BNs have been used to investigate linkage disequilibrium (LD; Mourad et al 2011;Morota et al 2012) and epistasis (Han et al 2012) and to determine disease susceptibility for anemia (Sebastiani et al 2005), leukemia (Chang and Mcgeachie, 2011), and hypertension (Malovini et al 2009). The same BN can simultaneously highlight SNPs potentially involved in determining a trait (e.g., for association purposes) and be used for prediction (e.g., for selection purposes): a network capturing the relationship between genotypes and phenotypes can be used to compute the probability that a new individual with a particular genotype will have the phenotype of interest (Lauritzen and Sheehan 2004;Cowell et al 2007).…”

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Multiple Quantitative Trait Analysis Using Bayesian Networks

et al. 2014

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Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

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“…The latter is a reference data set produced by WTCCC to study genome-wide high-resolution mapping of quantitative trait loci using mice as animal models for human diseases. In this context BNs have been used to investigate dependence patterns between single nucleotide polymorphisms (Morota, Valente, Rosa, Weigel, and Gianola 2012).…”

Section: Simulations On the Real-world Datamentioning

confidence: 99%

Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package

Scutari¹

2017

J. Stat. Soft.

144

117

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It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios.Efficient implementations of score-based structure learning benefit from past and current research in optimization theory, which can be adapted to the task by using the network score as the objective function to maximize. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimization in widespread use, backtracking, leverages the symmetries implied by the definitions of neighborhood and Markov blanket.In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelize constraint-based structure learning algorithms (also implemented in bnlearn) and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.

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An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network

Cited by 15 publications

References 23 publications

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

Multiple Quantitative Trait Analysis Using Bayesian Networks

Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package

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