Genomic Prediction of Maize Inbred Lines Using a Small Combined Training Population, and Evaluation of Untested Germplasms for Resistance to Pythium Root and Stalk Rot

Japan Agricultural Research Quarterly
ISSN 00213551
NII recode ID (NCID) AA0068709X
Genotypic studies using molecular markers, such as genomic prediction (GP), have been implemented in maize (Zea mays L.) breeding, leading to a better understanding of significant traits. Pythium root and stalk rot (RSR) resistance is an important trait in maize bred in Japan. The study aims to (1) develop a GP model for Pythium RSR resistance in maize bred within the Japanese public sector and (2) utilize GP to analyze untested maize germplasms for Pythium RSR resistance via a combined training set derived from different heterotic groups (dent and flint). Through 1000 repetitions of sampling and five-fold cross-validation, a high average prediction accuracy (r = 0.695, 95% confidence interval: 0.682-0.708) was achieved across populations. Prediction accuracy improved as the number of markers increased, but it eventually reached a plateau that exceeded 1000 markers. The population component and linkage disequilibrium between markers confirmed previous reports. These findings show the feasibility of GP, even with a small population (N = 41) and marker size (approximately 1000). Several old inbred lines were identified with lower predicted RSR scores, indicating their potential as breeding materials. This is the first report on the prediction of maize Pythium RSR resistance using GP and emphasizes new possibilities for addressing Pythium RSR resistance in maize breeding.
Date of issued
Creator Shohei MITSUHASHI Hiroyuki TAMAKI
Subject best linear unbiased prediction Zea mays L.
Publisher Japan International Research Center for Agricultural Sciences
Received Date 2023-08-07
Accepted Date 2023-12-09
Volume 58
Issue 3
Language eng

Related Publication