Triple categorical regression

1. Article: Triple categorical regression for genomic selection: application to cassava breeding.

Submitted to Scientia Agricola

Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves the efficiency of genetic evaluations by increasing the 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  individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, thus allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, owing to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. In this regard, 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 on the basis of the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with the aim to increase the 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 for estimating variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. In summary, the proposed methods should provide new insights for GWS.

Keywords: Genomic prediction, G-BLUP, BLASSO, genomic heritability.

R code: TCR CODE





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