Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F 2 populations by using genomic selection models

• Autor(a) principal: Peixoto, L.A.
• Data de Publicação: 2016
• Outros Autores: Bhering, L.L., Cruz, C.D.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://dx.doi.org/10.4238/gmr15048874; http://www.locus.ufv.br/handle/123456789/12215
• topic: Genomic prediction; Heritability; Prediction ability; Breeding; Quantitative genetics

Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F 2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F 2 populations. F 2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F 2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.