Triple categorical regression for genomic selection: application to cassava breeding

Abstract

Genome-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.

Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data

“…The description of the scenarios is presented in Table 1. Each type of scenario was simulated ten times to assess the efficiency of the methods, according to Lima et al (2019). Thus, the measures used were calculated in each repetition of the simulation and thereafter the mean and standard error of these values were obtained.…”

Prediction of genetic values according to the dimensionality reduction of SNP's markers in complex models

“…GWS stands out for promoting high selective accuracy and for not requiring the knowledge of the prior location of the QTLs in the chromosomes (de Almeida et al 2010; Almeida et al 2017; Alkimim et al 2020). One of the challenges of GWS is the high dimensionality, for the number of markers is greater than the number of genotyped and phenotyped individuals (de Almeida Filho et al 2019; Lima et al 2019). When there is high dimensionality, it is possible that some traits are controlled by a smaller number of loci and, in some cases, the linkage groups do not contribute with determinant loci in the variation of these traits.…”