Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Silva, Flávia Alves da; Viana, Alexandre Pío; Corrêa, Caio Cézar Guedes; Santos, Eileen Azevedo; Oliveira, Julie Anne Vieira Salgado de; Andrade, José Daniel Gomes; Ribeiro, Rodrigo Moreira; Glória, Leonardo Siqueira

doi:10.1038/s41598-021-93120-z

Cited by 22 publications

(10 citation statements)

References 27 publications

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“…Whittaker et al 22 proved the ridge regression model to be efficient to improve the mean response to selection and reduce the variability of the selection response. da Silva et al 17 used multiple Bayesian models for making genomic breeding values predictions and among them, the Bayesian ridge regression model had the lowest mean error value which is different from the results obtained in this study. A higher correlation of 0.90 between BV and predicted BV, using the Bayesian technique was observed for the prediction of BVs in the Harnali breed of sheep by Bangar et al 23 .…”

Section: Discussioncontrasting

confidence: 99%

“…The model predictions for ridge regression were similar to Bayesian ridge regression but the Bayesian models gave slightly better predictions. da Silva et al 17 also compared Bayes models to report that Bayesian ridge regression performed best for predicting breeding values. The use of penalties in the model for multiple predictors in the regression also makes it an effective technique 18 .…”

Section: Discussionmentioning

confidence: 99%

“…Grid search was employed for arriving at the best n-neighbors for KNN which were specified in a tuple as 2 – 8 , 17 , 44 .…”

Section: Methodsmentioning

confidence: 99%

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Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep

Hamadani

Ganai

Ahmad

et al. 2022

Sci Rep

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As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 12 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through principal component analysis (PCA) was 1. The correlation coefficients between true and predicted values for artificial neural networks, Bayesian ridge regression, classification and regression trees, gradient boosting algorithm, K nearest neighbours, multivariate adaptive regression splines (MARS) algorithm, polynomial regression, principal component regression (PCR), random forests, support vector machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. Among the prediction equations generated using OLS, the highest coefficient of determination was 0.569. A total of 12 machine learning models were developed from the prediction of breeding values in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies for breeding value prediction.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep

Hamadani

Ganai

Ahmad

et al. 2022

Sci Rep

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“…Expectation maximization Bayesian ridge regression (emRR [ 31 ]) assumes that all regression coefficients have equal variance. It introduces the regular term automatically in the estimation process, which finally obtains the posterior distribution of the parameters, avoiding overfitting in the large-scale likelihood estimation.…”

Section: Methodsmentioning

confidence: 99%

An Improved Bayesian Shrinkage Regression Algorithm for Genomic Selection

Zhang

Li²,

Lv³

et al. 2022

Genes

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Currently a hot topic, genomic selection (GS) has consistently provided powerful support for breeding studies and achieved more comprehensive and reliable selection in animal and plant breeding. GS estimates the effects of all single nucleotide polymorphisms (SNPs) and thereby predicts the genomic estimation of breeding value (GEBV), accelerating breeding progress and overcoming the limitations of conventional breeding. The successful application of GS primarily depends on the accuracy of the GEBV. Adopting appropriate advanced algorithms to improve the accuracy of the GEBV is time-saving and efficient for breeders, and the available algorithms can be further improved in the big data era. In this study, we develop a new algorithm under the Bayesian Shrinkage Regression (BSR, which is called BayesA) framework, an improved expectation-maximization algorithm for BayesA (emBAI). The emBAI algorithm first corrects the polygenic and environmental noise and then calculates the GEBV by emBayesA. We conduct two simulation experiments and a real dataset analysis for flowering time-related Arabidopsis phenotypes to validate the new algorithm. Compared to established methods, emBAI is more powerful in terms of prediction accuracy, mean square error (MSE), mean absolute error (MAE), the area under the receiver operating characteristic curve (AUC) and correlation of prediction in simulation studies. In addition, emBAI performs well under the increasing genetic background. The analysis of the Arabidopsis real dataset further illustrates the benefits of emBAI for genomic prediction according to prediction accuracy, MSE, MAE and correlation of prediction. Furthermore, the new method shows the advantages of significant loci detection and effect coefficient estimation, which are confirmed by The Arabidopsis Information Resource (TAIR) gene bank. In conclusion, the emBAI algorithm provides powerful support for GS in high-dimensional genomic datasets.

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“…Individuals 67 and 68 were the least dissimilar, that is, the closest, with 0.93 similarity and 84 alleles in common. Silva et al (2021) evaluated this same population for the traits of soluble solids content, fruit weight, pulp weight, number of fruits per plant and yield per plant. The individual with the highest yield per plant was 53.…”

Section: Dissimilarity By Neighbor Joiningmentioning

confidence: 99%

Genetic-molecular characterization in guava full-sib progeny

et al. 2022

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Brazil is one of the world's largest producers of guava (Psidium guajava L.), a very promising fruit in the northern region of the state of Rio de Janeiro. Despite this, no guava cultivar has been developed for the region. Thus, this study proposed to examine a population of guava full sibs using microsatellite markers and to identify which genotypes are the most divergent for future crosses, to select cultivars better adapted to the soil and climatic conditions of northern Rio Janeiro. Ninety-six superior genotypes were selected according to their agronomic traits, which were characterized using 45 microsatellite markers. The genetic distance between the analyzed genotypes, their clustering pattern and the genetic structure of the population were estimated. Hierarchical cluster analysis by the neighbor joining method indicated the formation of three distinct groups. The use of molecular information revealed the existence of moderate genetic variability between the genotypes of the full-sib families. Bayesian analysis separated the genotypes into only two groups, as the individuals shared most of the analyzed genomic regions. The most genetically divergent guava genotypes, that is, those allocated to different groups, such as genotypes 5 and 85, should be recommended for future crosses to obtain segregating populations, thus giving continuity to the guava breeding program.

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Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

Cited by 22 publications

References 27 publications

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep

An Improved Bayesian Shrinkage Regression Algorithm for Genomic Selection

Genetic-molecular characterization in guava full-sib progeny

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