Efficiency of Genomic Prediction of Nonassessed Testcrosses

Abstract

In plant breeding, genomic selection has been mainly used to predict untested single crosses and testcrosses. The objectives of this study were to assess the efficiency of prediction of untested testcrosses and the significance of the factors affecting the prediction. We simulated 20,000 testcrosses from two groups of 5000 related doubled haploid lines (DHs) and two unrelated elite inbred lines. The DHs were genotyped for 20,000 single nucleotide polymorphisms (SNPs) and the testcrosses were phenotyped for grain yield. The average SNP density was 0.1 cM. We assumed genetic control by 400 quantitative trait loci (QTLs) and heritability of 25, 50, and 75% for the assessed testcrosses. The training set sizes were 10 and 30% of the available DHs. The process of random sampling of the field assessed and predicted testcrosses were replicated 50 times. We computed the prediction accuracy and the coincidence index, a measure of the selection efficacy of the nonassessed testcrosses. The results evidenced that genomic selection is an efficient process for selecting superior nonassessed testcrosses, if there is sufficient relatedness between the available DHs, linkage disequilibrium in the DHs and genotypic variance between testcrosses, a training set size of at least 10% of the available DHs, and a SNP density of at least 1 cM. The efficacy of selecting the superior nonassessed testcrosses ranged between 0.1 and 0.9, proportional to the heritability of the assessed testcrosses (0.25–1.00). It is important to highlight that the parametric coincidence ranged from 0.1 to 0.8. Furthermore, genomic prediction is much more efficient than pedigree‐based best linear unbiased prediction.

Significance of linkage disequilibrium and epistasis on the genetic variances and covariance between relatives in non-inbred and inbred populations

“…This software uses the quantitative genetics theory that was described in the previous sections and in Viana (2004). REALbreeding has been used to provide simulated data in investigations in the areas of genomic selection (Viana et al 2019), GWAS (Pereira et al 2018), QTL mapping (Viana et al 2017b), linkage disequilibrium (Andrade et al 2019), population structure (Viana et al 2013), and heterotic grouping/genetic diversity (Viana et al 2020).…”

Factors affecting heterotic grouping with cross‐pollinating crops

“…The genotyping and phenotyping processes of the populations, interpopulation crosses, selfed populations, doubled haploid (DH) lines, and single crosses were simulated using REALbreeding software (available by request). REALbreeding has been used to provide simulated data in studies involving genomic selection (J. M. S. Viana, Pereira, Piepho, & Silva, 2019), GWAS (Pereira, Viana, Andrade, Silva, & Paes, 2018), QTL mapping (J. M. S. Viana, Silva, Mundim, Azevedo, & Jan, 2017), linkage disequilibrium (Andrade, Viana, Pereira, Pinto, & Fonseca, 2019), and population structure (Viana, Valente, Silva, Mundim, & Paes, 2013).…”

Significance of Linkage Disequilibrium and Epistasis on the Genetic Variances in Non-Inbred and Inbred Populations

“…This software uses the quantitative genetics theory that was described in the previous sections and in JMS Viana [24]. REALbreeding has been used to provide simulated data in investigations in the areas of genomic selection [27], GWAS [28], QTL mapping [29], linkage disequilibrium [30], population structure [31], and heterotic grouping/genetic diversity [32].…”