Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks

Glória, Leonardo Siqueira; Cruz, Cosme Damião; Vieira, Ricardo Augusto Mendonça; Resende, Marcos Deon Vilela de; Lopes, Paulo Sávio; Siqueira, Otávio Henrique Gomes Barbosa Dias de; Silva, Fabyano Fonseca e

doi:10.1016/j.livsci.2016.07.015

Cited by 17 publications

(34 citation statements)

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“…The dominant design of the relatively few applications of NN in GWP is the MLP (e.g., Ehret et al (2015) and Glória et al (2016)). Bellot et al (2018) compared deep learning models on large human SNP data combined with five phenotypes with varying levels of heritability.…”

Section: Discussionmentioning

confidence: 99%

Sparse Convolutional Neural Networks for Genome-Wide Prediction

2020

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Genome-wide prediction (GWP) has become the state-of-the art method in artificial selection. Data sets often comprise number of genomic markers and individuals in ranges from a few thousands to millions. Hence, computational efficiency is important and various machine learning methods have successfully been used in GWP. Neural networks (NN) and deep learning (DL) are very flexible methods that usually show outstanding prediction properties on complex structured data, but their use in GWP is nevertheless rare and debated. This study describes a powerful NN method for genomic marker data that can easily be extended. It is shown that a one-dimensional convolutional neural network (CNN) can be used to incorporate the ordinal information between markers and, together with pooling and ℓ 1-norm regularization, provides a sparse and computationally efficient approach for GWP. The method, denoted CNNGWP, is implemented in the deep learning software Keras, and hyper-parameters of the NN are tuned with Bayesian optimization. Model averaged ensemble predictions further reduce prediction error. Evaluations show that CNNGWP improves prediction error by more than 25% on simulated data and around 3% on real pig data compared with results obtained with GBLUP and the LASSO. In conclusion, the CNNGWP provides a promising approach for GWP, but the magnitude of improvement depends on the genetic architecture and the heritability.

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Section: Discussionmentioning

confidence: 99%

Sparse Convolutional Neural Networks for Genome-Wide Prediction

2020

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“…Hence, one can argue that it is more important to focus on efficient regularization and sparsity than on modelling of complex structures when genomic data consists of SNPs. Glória et al [19] also found that more complex NN designs reduced the predictive accuracy compared to a simple one-layer one-node net when evaluated on simulated genotype/phenotype data.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, the usefulness of deep-learning for GWAS is limited, although some techniques exist for variable importance analysis e.g. [19, 38].…”

Section: Discussionmentioning

confidence: 99%

“…Ehret et al [18] replaced the computationally demanding Levenberg–Marquardt training algorithm in [16] with back-propagation. Glória et al [19] showed how two approximate measures of variable contribution and importance could be used to assess marker effects in genomic NN. Deep-learning has also received attention in the related fields of bioinformatics and systems biology [20, 21].…”

Section: Introductionmentioning

confidence: 99%

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Approximate Bayesian neural networks in genomic prediction

Waldmann

2018

Genet Sel Evol

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BackgroundGenome-wide marker data are used both in phenotypic genome-wide association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies include high-dimensional data with thousands to millions of single nucleotide polymorphisms (SNPs) recorded in hundreds to a few thousands individuals. Different machine-learning approaches have been used in GWAS and GWP effectively, but the use of neural networks (NN) and deep-learning is still scarce. This study presents a NN model for genomic SNP data.ResultsWe show, using both simulated and real pig data, that regularization is obtained using weight decay and dropout, and results in an approximate Bayesian (ABNN) model that can be used to obtain model averaged posterior predictions. The ABNN model is implemented in mxnet and shown to yield better prediction accuracy than genomic best linear unbiased prediction and Bayesian LASSO. The mean squared error was reduced by at least 6.5% in the simulated data and by at least 1% in the real data. Moreover, by comparing NN of different complexities, our results confirm that a shallow model with one layer, one neuron, one-hot encoding and a linear activation function performs better than more complex models.ConclusionsThe ABNN model provides a computationally efficient approach with good prediction performance and in which the weight components can also provide information on the importance of the SNPs. Hence, ABNN is suitable for both GWP and GWAS.

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“…These connections are directed by means of estimated weights, which measure the influence of the predictor variables on the response variable. In addition to weights, the bias ( ), also known as intercept, is estimated (Glória et al, 2016).…”

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confidence: 99%

Discrimination of populations under covariance matrix heterogeneity and non-normal random vectors in genetic diversity studies

Carvalho¹,

Sousa²,

Nascimento³

et al. 2018

Científica

Self Cite

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Genetic diversity analysis has guided the choice of appropriate parents in breeding programs. Multivariate statistical methods such as discriminant analysis are used to obtain the necessary results in these studies. However, to obtain reliable results, one must meet assumptions such as covariance matrix heterogeneity and multivariate normality of the observation vector. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT) and its refinements do not have these assumptions and may be used in the choice of appropriate parents. This study evaluates the robustness of the Fisher's discriminant function under covariance matrix heterogeneity and multivariate non-normal random vectors. The results were compared with those obtained from Quadratic Discriminant Analysis (QDA), ANN, SVM and DT. Scenarios characterized by heterogeneous covariance matrices and multivariate non-normal random vectors were simulated. Considering the apparent error rate (APER), the SVM method (APER-Normal = 0.07; APER-Poisson = 0.13) and quadratic discriminant method (APER-Normal = 0.09; APER-Poisson = 0.09) presented better results for scenarios simulated with covariance matrix heteroscedasticity. For scenarios with multivariate normality and covariance matrix homoscedasticity, the SVM (APER = 0.15) and ANN (APER = 0.06) presented best results. For situations in which the data had multivariate Poisson distribution and covariance matrix homogeneity, the SVM (APER = 0.15), Fisher's discriminant function (APER = 0.19) and ANN (APER = 0.19) presented better performances. Finally, DT refinements (Bagging, Random Forest and Boosting) presented APER values less than 0.25 and are shown to be alternatives.Additional Keywords: quadratic discriminant function; multivariate analysis, simulation. ResumoAnálises de diversidade genética têm orientado a escolha de genitores apropriados em programas de melhoramento. Métodos de Estatística Multivariada, como por exemplo, as análises discriminantes são utilizadas para obtenção dos resultados necessários nesses estudos. Entretanto, a obtenção de resultados confiáveis está associada ao atendimento de pressupostos, como por exemplo a heterogeneidade de matrizes de covariância e normalidade multivariada do vetor de observações. Redes Neurais Artificiais (RNA), Máquina de Vetor Suporte (MVS), Árvores de Decisão (AD) e seus refinamentos, não possuem pressupostos e podem ser utilizadas para esse fim. O objetivo desse trabalho foi avaliar a robustez da função discriminante de Fisher na presença de matrizes de covariâncias heterogêneas e vetores aleatórios não normais multivariados. Os resultados foram comparados com aqueles provenientes da função discriminante quadrática (FDQ), RNA, MVS e AD. Foram simulados cenários caracterizados por matrizes de covariâncias heterogêneas e vetores aleatórios não normais multivariados. Considerando a Taxa de Erro Aparente média (TEA) a MVS (TEA-Normal=0,07; TEA-Poisson=0,13) e FDQ (TEA-Normal=0,09; TEA-Poisson=0,09) apresentaram melhores resultados para os simu...

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Accessing marker effects and heritability estimates from genome prediction by Bayesian regularized neural networks

Cited by 17 publications

References 16 publications

Sparse Convolutional Neural Networks for Genome-Wide Prediction

Sparse Convolutional Neural Networks for Genome-Wide Prediction

Approximate Bayesian neural networks in genomic prediction

Discrimination of populations under covariance matrix heterogeneity and non-normal random vectors in genetic diversity studies

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