Triple categorical regression for genomic selection: application to cassava breeding

Author:

Lima Leísa Pires1ORCID,Azevedo Camila Ferreira1ORCID,Resende Marcos Deon Vilela de2ORCID,Silva Fabyano Fonseca e1ORCID,Viana José Marcelo Soriano1ORCID,Oliveira Eder Jorge de3ORCID

Affiliation:

1. Universidade Federal de Viçosa, Brasil

2. Embrapa Floresta, Brasil

3. Embrapa Mandioca e Fruticultura, Brasil

Publisher

FapUNIFESP (SciELO)

Reference26 articles.

1. Ridge, Lasso and Bayesian additive-dominance genomic models;Azevedo C.F.;BMC Genetics,2015

2. New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program;Azevedo C.F.;Genetics and Molecular Research,2016

3. Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers;Da Y.;PLoS ONE,2014

4. Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation;De los Campos G.;Journal of Animal Science,2009

5. Whole genome regression and prediction methods applied to plant and animal breeding;De los Campos G.;Genetics,2013

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