02695naa a2200337 a 450000100080000000500110000800800410001902400370006010000160009724501130011326000090022652016980023565000150193365000230194865300110197165300110198265300270199365300250202065300230204565300280206865300220209665300250211865300330214365300100217670000190218670000250220570000190223070000200224970000230226977300650229221108792019-10-30       2019    bl uuuu     u00u1 u    #d7 a10.1590/1678-992X-2017-03692DOI1 aLIMA, L. P.  aTriple categorical regression for genomic selectionbapplication to cassava breeding.h[electronic resource]  c2019  aGenome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.  aPrediction  aMarcador Molecular  aBLASSO  aG-BLUP  aGenética quantitativa  aGenomic heritability  aGenomic prediction  aHerdabilidade genômica  aMolecular markers  aPredição genômica  aQuantitative genetics theory  aRidge1 aAZEVEDO, C. F.1 aRESENDE, M. D. V. de1 aSILVA, F. F. e1 aVIANA, J. M. S.1 aOLIVEIRA, E. J. de  tScientia Agricolagv. 76, n. 5, p. 368-375, Sept./Oct. 2019.