ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Functional and Applied Plant Genomics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1614457
Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability of haploid inducers in maize
Provisionally accepted
- 1Department of Agronomy, College of Agriculture and Life Sciences, Iowa State University, Ames, United States
- 2Iowa State University, Ames, Iowa, United States
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Ridge regression BLUP (rrBLUP) is a widely used model for genomic selection. Different genomic prediction (GP) models have their own niches depending on the genetic architecture of traits and computational complexity. Haploid inducers have unique trait performances, relevant for doubled haploid (DH) technology in maize (Zea mays L.). We herein compared the performance of single-trait (ST) and multi-trait (MT) GP models (rrBLUP, BayesB, Random Forest, and xGBoost) and employed multi-trait and de novo GWAS in the ridge regression BLUP model for four traits of interest (days to flowering, DTF; haploid induction rate, HIR; plant height, PHT; primary branch length, PBL) of multifamily DH inducers (DHIs), and next tested the GP models in multi-parent advanced generation inter-cross (MAGIC) DHIs. The average predictive abilities (PA) of different GP methods across traits were 0.51 to 0.69 when using five-fold cross-validation. ST/MT de novo GWAS rrBLUP methods increased PA of HIR. In addition, MT GP models improved PA by 12% on average across traits relative to ST GP models in MAGIC DHIs. These results provide empirical evidence that employing multitrait and de novo GWAS in rrBLUP model in genomic selection could benefit the genetic improvement of haploid inducers.
Keywords: genomic selection, Haploid inducer, Multi-Trait, Ridge regression BLUP, de novo GWAS, predictive ability
Received: 18 Apr 2025; Accepted: 18 Jul 2025.
Copyright: © 2025 Chen, Frei and Lübberstedt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yu-Ru Chen, Department of Agronomy, College of Agriculture and Life Sciences, Iowa State University, Ames, United States
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