Open AccessFactors affecting the accuracy of genomic predictions in testcrosses of maize biparental population
Author(s) -
Vlatko Galić,
Maja Mazur,
Andrija Brkić,
Mirna Volenik,
Antun Jambrović,
Zvonimir Zdunić,
Domagoj Šimić
Publication year - 2020
Publication title -
poljoprivreda
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.265
H-Index - 11
eISSN - 1848-8080
pISSN - 1330-7142
DOI - 10.18047/poljo.26.1.2
Subject(s) - heritability , trait , population , biology , selection (genetic algorithm) , correlation , population size , genetic correlation , statistics , quantitative trait locus , genomic selection , genetic variation , mathematics , genetics , demography , genotype , computer science , gene , machine learning , geometry , sociology , single nucleotide polymorphism , programming language
Genomic prediction accuracy (r_MP) is affected by many factors, such as the trait heritability, training population size and structure, and the number of markers. This study’s objective was to investigate the factors associated with r_MP for the ear height and the plant height in two planting densities in testcrosses of maize (Zea mays L.) IBM population. Genetic correlations between the training and validation populations were calculated. The high heritability estimates and correlations between the traits were observed. The non-zero estimates of r_MP for all trait-density combinations implied an efficiency of genomic selection. The lower than expected values of genetic correlations were observed between the training and validation populations. However, a strong correlation was observed between a genetic correlation of training and the validation population and r_MP in all three sizes of training populations assessed (20-40%, 40-60%, and 60-80%), suggesting that the size of the training population can be kept low by an appropriate selection while maintaining a high r_MP. Further studies of relationships between the training and validation populations with larger effective population sizes are suggested, as reducing the size of training population while maintaining a high r_MP can facilitate a more effective allocation of resources in a maize breeding program.