Detection of quantitative trait loci for ear row number in F2 populations of maize

Abstract

ABSTRACT. Ear row number (ERN) is not only a key trait involved in maize (Zea mays L.) evolution but is also an important component that is directly related to grain yield. In this study, quantitative trait loci (QTLs) for ERN were detected across two F 2 populations that were derived from a same cross between B73 with 16 rows (N = 233) and SICAU1212 with four rows (N = 231). As a result, 33 QTLs were associated with 12 agronomic traits: three plant traits, four ear-related traits, and five kernel-related traits. The total phenotypic variation explained by the QTLs for each trait ranged from 8.60 to 72.67%, and four QTLs were identified for ERN in the two populations. Each QTL explained between 6.78 and 36.76% of the ERN variation. Notably, three of the four QTLs (qERN2-1, qERN4-2, and qERN8-1) were associated with ERN, and qERN8-1 simultaneously influenced grain yield, plant diameter, ear diameter, and kernel length. In addition, only one significant epistatic interaction was detected in all 33 QTLs. This study should provide a foundation for further fine-mapping and map-based cloning of these consistent QTLs, and for controlling maize ERN by marker-assisted breeding.

Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize

“…Although many QTLs for ED have been detected in recent years [ 4 , 5 , 6 , 7 , 8 ], most of them are not consistent, due to the use of different maize populations in different environments in these studies. Therefore, only a few QTLs and putative candidate genes have been applied in maize breeding, if any.…”

“…These findings indicate that the SNPs sites, QTLs, and candidate genes of ED carried by these donor parents have positive effects on increasing GY and GY_MPH in maize. Although many QTLs for ED have been detected in recent years [4][5][6][7][8], most of them are not consistent, due to the use of different maize populations in different environments in these studies. Therefore, only a few QTLs and putative candidate genes have been applied in maize breeding, if any.…”

“…Yu et al (2014) reported a total of six ED-related QTLs using an F 2 segregating population from a sweet maize inbred line and its space-induced mutant [5]. Yang et al (2015) detected two ED-related QTLs (q10sED8-1 and q10sED9-1) on chromosomes 8 and 9, respectively, using a segregating population of 233 families derived from maize inbred lines B73 and SICAU1212 [6]. Mendes-Moreira et al (2015) discovered 10 ED-related QTLs using an F 2 population derived from maize inbred lines PB260 and PB266 [7].…”

Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review

“…Karen et al (2008) used the compositive interval mapping in F 2:3 families derived from L-08-05F and L-14-4B and identified 10 QTLs for KRN, with a PVE ranging from 2.40 to 16.90% [44]. Yang et al (2015) used an F 2 population of maize derived from B73 and SICAU1212 using the CIM method and detected seven QTLs related to the number of ear rows in two environments, explaining 6.78% to 36.76% of the phenotypic variance [45]. In their study, Chen et al (2016) [46] established a four-way cross-mapping population using hybrids of D276 and D72, as well as hybrids of A188 and Jiao51.…”

Meta-QTL analysis and candidate genes identification for lodging resistance in maize: Ⅰ. stalk related traits

“…Quantitative trait locus (QTL) analysis provides information on molecular markers associated with speci c traits in chromosomal regions that can be applied for marker-assisted selection (MAS) breeding (Scheben et al 2018). Hundreds of QTLs for stalk-lodging-related traits, including plant height (PH) and ear height (EH), plant diameter (PD), internode length (IL), and rind penetrometer resistance (RPR), have been identi ed in maize (Flint-Garcia et al 2003;Fei et al 2022;Hou et al 2022; Ku et al 2015; Yang et al 2015). However, because of large con dence intervals (CI) for any given QTL, and differences in the positions of QTLs that have the same effect in different mapping populations and environments, QTL ne mapping and gene validation is di cult and timeconsuming.…”