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

Abstract Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the val...

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Autores principales:	Flavia Alves da Silva, Alexandre Pio Viana, Caio Cezar Guedes Correa, Eileen Azevedo Santos, Julie Anne Vieira Salgado de Oliveira, José Daniel Gomes Andrade, Rodrigo Moreira Ribeiro, Leonardo Siqueira Glória
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff0d0eda78084174bbd951a95836d65d
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spelling	oai:doaj.org-article:ff0d0eda78084174bbd951a95836d65d2021-12-02T16:31:58ZBayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models10.1038/s41598-021-93120-z2045-2322https://doaj.org/article/ff0d0eda78084174bbd951a95836d65d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93120-zhttps://doaj.org/toc/2045-2322Abstract Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to $$\pi$$ π = $${10}^{-5}$$ 10 - 5 ), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual’s genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.Flavia Alves da SilvaAlexandre Pio VianaCaio Cezar Guedes CorreaEileen Azevedo SantosJulie Anne Vieira Salgado de OliveiraJosé Daniel Gomes AndradeRodrigo Moreira RibeiroLeonardo Siqueira GlóriaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Flavia Alves da Silva Alexandre Pio Viana Caio Cezar Guedes Correa Eileen Azevedo Santos Julie Anne Vieira Salgado de Oliveira José Daniel Gomes Andrade Rodrigo Moreira Ribeiro Leonardo Siqueira Glória Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
description	Abstract Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to $$\pi$$ π = $${10}^{-5}$$ 10 - 5 ), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual’s genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.
format	article
author	Flavia Alves da Silva Alexandre Pio Viana Caio Cezar Guedes Correa Eileen Azevedo Santos Julie Anne Vieira Salgado de Oliveira José Daniel Gomes Andrade Rodrigo Moreira Ribeiro Leonardo Siqueira Glória
author_facet	Flavia Alves da Silva Alexandre Pio Viana Caio Cezar Guedes Correa Eileen Azevedo Santos Julie Anne Vieira Salgado de Oliveira José Daniel Gomes Andrade Rodrigo Moreira Ribeiro Leonardo Siqueira Glória
author_sort	Flavia Alves da Silva
title	Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
title_short	Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
title_full	Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
title_fullStr	Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
title_full_unstemmed	Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models
title_sort	bayesian ridge regression shows the best fit for ssr markers in psidium guajava among bayesian models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ff0d0eda78084174bbd951a95836d65d
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Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models

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