Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

Lopes, Fernando Brito; Magnabosco, C. de U.; Passafaro, Tiago Luciano; Brunes, L. C.; Costa, M. F. O. e; Eifert, E. C.; Narciso, Marcelo Gonçalves; Rosa, Guilherme J. M.; Lôbo, Raysildo Barbosa; Baldi, Fernando

doi:10.1111/jbg.12468

Cited by 20 publications

(15 citation statements)

References 41 publications

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“…In this sense, Liu and Wang (2017) found that DL outperformed traditional statistical models (RR-BLUP, BL, and Bayes A) in the genomic prediction of grain yield, in soybean, and stem height, in loblolly pine. In a GP study for meat tenderness, Lopes et al (2020) found similar results to the current study, in which the DL model had higher PA than all models of the Bayesian alphabet (Bayes A, Bayes B, Bayes Cπ, BRR, and BL). Notably, in this study, ReLU was the best activation function used for training DL, because it is faster to learn than sigmoid and hyperbolic tangent functions, and it has better performance during the random grid search.…”

Section: Discussionsupporting

confidence: 86%

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

et al. 2020

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Genomic selection models were investigated to predict several complex traits in breeding populations of Zea mays L. and Eucalyptus globulus Labill. For this, the following methods of Machine Learning (ML) were implemented: (i) Deep Learning (DL) and (ii) Bayesian Regularized Neural Network (BRNN) both in combination with different hyperparameters. These ML methods were also compared with Genomic Best Linear Unbiased Prediction (GBLUP) and different Bayesian regression models [Bayes A, Bayes B, Bayes Cπ, Bayesian Ridge Regression, Bayesian LASSO, and Reproducing Kernel Hilbert Space (RKHS)]. DL models, using Rectified Linear Units (as the activation function), had higher predictive ability values, which varied from 0.27 (pilodyn penetration of 6 years old eucalypt trees) to 0.78 (flowering-related traits of maize). Moreover, the larger mini-batch size (100%) had a significantly higher predictive ability for wood-related traits than the smaller mini-batch size (10%). On the other hand, in the BRNN method, the architectures of one and two layers that used only the pureline function showed better results of prediction, with values ranging from 0.21 (pilodyn penetration) to 0.71 (flowering traits). A significant increase in the prediction ability was observed for DL in comparison with other methods of genomic prediction (Bayesian alphabet models, GBLUP, RKHS, and BRNN). Another important finding was the usefulness of DL models (through an iterative algorithm) as an SNP detection strategy for genome-wide association studies. The results of this study confirm the importance of DL for genome-wide analyses and crop/tree improvement strategies, which holds promise for accelerating breeding progress.

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Section: Discussionsupporting

confidence: 86%

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

et al. 2020

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“…For the deep learning models, two regularization strategies were employed during the training phase, i.e., weight decay and dropout. Besides, previous studies point that the ML methods can be useful even when the number of explanatory variables (p) vastly exceeds the number of available phenotypes (n), as demonstrated in the genome-enabled prediction of complex traits (Lopes et al, 2020;Bargelloni et al, 2021). Hence, the higher number of explanatory variables itself does not fully explain the lower classification performance of the models feed with the MFCC data.…”

Section: Discussionmentioning

confidence: 99%

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

et al. 2021

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This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p < 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

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“…In recent years, the use of ANN statistical models in the studies of genomic selection (prediction) has shown an increasing trend [11] [12] [13] [14] [15] [16]. In the studies of plant and animal breeding, the number of parameters (SNP marker effects) to be estimated is much more than the available sample size and the computational costs of training the ANN applications are high.…”

Section: Introductionmentioning

confidence: 99%

Genomic Prediction With Different Heritability, QTL, and SNP Panel Scenarios Using Artificial Neural Network

et al. 2020

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A simulation study was carried out to determine the genomic prediction performance of Artificial Neural Network model with 1 to 10 neurons (ANN-1-10) using 3361 SNP markers from BovineSNP50 (Infinium BeadChip, Illumina, San Diego, CA) on the first chromosome of Brangus beef cattle as a pilot study for two traits with heritabilities of 25% (ℎ 1 2 = 0.25) and 50% (ℎ 2 2 = 0.5) determined either by 50, 100, 250 or 500 QTL selected randomly from SNP markers. QTL effects were sampled from a multivariate normal distribution. Genomic predictions were carried out by Feed Forward Multi-Layer Perceptron ANN-1-10 with the back-propagation of errors algorithm employing the Levenberg-Marquardt algorithm to locate the optimal solution. Three sets of SNP panels were used for genomic prediction: only QTL (Panel-1), all SNP markers, including the QTL (Panel-2), and all SNP, excluding the QTL (Panel-3). Correlations between true genetic merits (breeding values) and predicted phenotypes from 10-fold disjoint cross-validation were used to assess predictive ability of ANN-1-10. Results indicated that an increase in heritability resulted in an increased predictive performance of ANN-1-10 for all scenarios. SNP Panels had a greater chance of including markers in LD with QTL, allowed the possibility of predicting the effect of each QTL from the collective action of several markers and performed better than the Panel including only QTL. In the other Panels, predictive performance of ANN-1-10 increased inconsistently with the number of neurons, which indicated that a few numbers of neurons were not be enough to learn specification of data and could cause the under fitting problem. Therefore, high number of neurons could be needed to learn relevant details of the data in the applications of ANN.

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Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

Cited by 20 publications

References 41 publications

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network

Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

Genomic Prediction With Different Heritability, QTL, and SNP Panel Scenarios Using Artificial Neural Network

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