Molecular mapping of quantitative trait loci for grain moisture at harvest and field grain drying rate in maize (Zea mays L.)

Abstract

Maize (Zea mays L.) grain moisture (GM) at harvest is an important trait that affects seed preservation during storage, grain quality and artificial drying costs. To date, most of the work on understanding GM dynamics in maize has focused on the grain filling period, while the period of postmaturity grain drying remains unexplored. The field grain drying rate (FDR) is one of the most important factors in determining GM at harvest. Therefore, understanding the genetic basis of FDR will be useful for obtaining low-GM varieties. In this study, a single-cross population (330 F 2:3 -generation plants) derived from a cross of two divergent inbred lines was evaluated in two planting environments with a measurement method -Area under the Dry Down Curve (AUDDC). A high-density genetic linkage map of 2491 single nucleotide polymorphism (SNP) loci covering 2415.56 cM was constructed. Using composite interval mapping, four quantitative trait loci (QTL), q45dGM1-1, qHTGM2-2, qAUDDC2-1 and qAUDDC10-1, which were detected on chromosomes 1, 2 and 10, were stable across environments and could explain more than 10% of phenotypic variance. These may be the major QTLs, with non-significant environmental interactions for GM at 45 days, GM at harvest and FDR, respectively. Additionally, several predicted candidate genes for FDR were identified, including several transcription factors, hormone responsive genes, energy-related and DNA replication-related genes. These results will provide useful information for our understanding of the genetic basis of FDR, as well as providing tools for marker-assisted selection in maize breeding.

The genetic architecture of the dynamic changes in grain moisture in maize

“…The dry‐down rate of grain is attributable mainly to temperature changes if the initial grain moisture exceeds 30%; otherwise, the dry‐down rate is mainly affected by relative humidity (Schmidt and Hallauer, 1966). In the past decade, biparental population‐based linkage analysis was commonly used to explore the genetic determinants of grain moisture in maize, and quantitative trait loci (QTLs) controlling the trait have been detected in various populations (Austin et al ., 2000; Capelle et al ., 2010; Li et al ., 2014; Liu et al ., 2020; Mihaljevic et al ., 2005; Sala et al ., 2006; Wang et al ., 2012; Zhang et al ., 2020). Due to technical limitations, however, most experiments have involved indirect measurements of grain moisture levels at harvest based on estimated biomass, without considering the effects of the inner and outer environments on grain moisture.…”

“…Breeders often use the oven‐drying method to measure grain moisture content, but this test is time‐consuming and destructive. A mobile, flexible moisture measuring instrument has emerged as a simple, reliable tool for measuring this important trait (Austin et al ., 2000; Capelle et al ., 2010; Li et al ., 2014; Liu et al ., 2020; Liu et al ., 2020; Mihaljevic et al ., 2005; Sala et al ., 2006; Wang et al ., 2012; Zhang et al ., 2020). Here, we used GE’s BLD5604, a digital timber‐moisture meter (Yang et al ., 2010), to efficiently measure the moisture content of maize kernels in the field in real time to assess the dynamics of moisture content across kernel development (Figure 1a).…”

Molecular mapping of quantitative trait loci for 3 husk traits using genotyping by sequencing in maize (Zea mays L.)

“…The parents have been publicly released by the national technical system of the maize industry. PD80 with slow field grain drying rate exhibits more HN, longer HL, and wider HW than related traits of PHJ65 with fast grain dehydration rate ( Zhang et al 2020 ). In 2020, the RIL population and parents were planted at 3 field environments in China: Zhoukou (E1, 108°E, 18°N), Xinxiang (E2, 113°E, 35°N), and Anyang (E3, 114°E, 36°N).…”

Genetic dissection of grain water content and dehydration rate related to mechanical harvest in maize

“…With 330 F 2:3 families, 10 GWC at 45 DAP QTL, 10 GWC at harvest QTL and 10 AUDDC (area under the dry down curve) QTL were detected. Four of them, namely q45dGM1-1, qHTGM2-2, qAUDDC2-1 and qAUDDC10-1, were stable across environments and could explain more than 10% of phenotypic variance [46]. In a genome-wide association study, 13 chromosomal segments significantly associated with GDR were identified, of which seven were located within the previously mapped GDR QTL [47].…”