Genetic analysis of the seed dehydration process in maize based on a logistic model
Shuangyi Yin,
Jun Liu,
Tiantian Yang,
Pengcheng Li,
Yang Xu,
Huimin Fang,
Shuhui Xu,
Jie Wei,
Lin Xue,
Derong Hao,
Zefeng Yang,
Chenwu Xu
Affiliations
Shuangyi Yin
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Jun Liu
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Tiantian Yang
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Pengcheng Li
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Yang Xu
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Huimin Fang
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Shuhui Xu
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Jie Wei
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
Lin Xue
Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong 226541, Jiangsu, China
Derong Hao
Jiangsu Yanjiang Institute of Agricultural Sciences, Nantong 226541, Jiangsu, China
Zefeng Yang
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China; Corresponding authors.
Chenwu Xu
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China; Corresponding authors.
Seed moisture at harvest is a critical trait affecting maize quality and mechanized production, and is directly determined by the dehydration process after physiological maturity. However, the dynamic nature of seed dehydration leads to inaccurate evaluation of the dehydration process by conventional determination methods. Seed dry weight and fresh weight were recorded at 14 time points after pollination in a recombinant inbred line (RIL) population derived from two inbred lines with contrasting seed dehydration dynamics. The dehydration curves of RILs were determined by fitting trajectories of dry weight accumulation and dry weight/fresh weight ratio change based on a logistic model, allowing the estimation of eight characteristic parameters that can be used to describe dehydration features. Quantitative trait locus (QTL) mapping, taking these parameters as traits, was performed using multiple methods. Single-trait QTL mapping revealed 76 QTL associated with dehydration characteristic parameters, of which the phenotypic variation explained (PVE) was 1.03% to 15.24%. Multiple-environment QTL analysis revealed 21 related QTL with PVE ranging from 4.23% to 11.83%. Multiple-trait QTL analysis revealed 58 QTL, including 51 pleiotropic QTL. Combining these mapping results revealed 12 co-located QTL and the dehydration process of RILs was divided into three patterns with clear differences in dehydration features. These results not only deepen general understanding of the genetic characteristics of seed dehydration but also suggest that this approach can efficiently identify associated genetic loci in maize. Keywords: Maize, Dehydration, Logistic model, Characteristic parameter, QTL