Affiliation:
1. Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
2. State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract
Maize breeding is greatly affected by hybrid vigor, a phenomenon that hybrids exhibit superior performance than parental lines. The immortalized F2 population (IMF2) is ideal for the genetic dissection and prediction of hybrid performance. Here, in this study, we conducted the QTL mapping and genomic prediction of six traits related to plant architecture using an IMF2 population. Broad-sense heritability of these traits ranged from 0.85 to 0.94. Analysis of genetic effects showed that additive variance was the main contributor to phenotypic variations. The mapping of quantitative trait loci (QTLs) revealed 10 to 16 QTLs (including pleiotropic loci and epistatic QTLs) for the six traits. Additionally, we identified 15 fine-tuning QTLs for plant height (PH). For genomic prediction (GP), the model of additive and dominance (AD) exhibited higher prediction accuracy than those fitting general combining ability (GCA) and its combination with special combining ability (SCA) effects for all tested traits. And adding the epistasis (E) effect into the AD model did not significantly increase its prediction accuracy. Moreover, the identified 15 fine-tuning QTLs of PH, which exerted large genomic prediction effects, were verified by the marker effect of GP. Our results not only provide an approach for the fine-mapping of fine-tuning QTLs but also serve as references for GP breeding in crops.
Funder
Key Scientific and Technological Research Project of Henan Province
Joint Research on Agricultural Improved Seed of Henan Province
Shennong Laboratory First-rate Research Subject
Key Public Welfare Project of Henan Province
Key Scientific and Technological Research Project of Xinxiang City
Agricultural Science and Technology Innovation Program of CAAS