Quantitative trait loci mapping for kernel row number using chromosome segment substitution lines in maize

Abstract

ABSTRACT. Unveiling the genetic architecture of grain yield and yieldrelated traits is useful for guiding the genetic improvement of crop plants. Kernel row number (KRN) per ear is an important yield component, which directly affects the grain yield of maize. In this study, we constructed a set of 130 chromosome segment substitution lines (CSSLs), using Nongxi531 as the donor parent and H21 as recipient parent, by continuous backcrossing and selfing. In total, 11 quantitative trait loci (QTL) were detected for KRN by stepwise regression under 3 environmental settings, with 9.87-19.44% phenotypic variation being explained by a single QTL. All 11 QTL were also detected by single-factor ANOVA across the 3 environments tested. Of these 11 QTL, 4 were identified across more than 2 environments, indicating that they are authentically expressed under different environments to control the formation and development of KRN in female maize inflorescences. The CSSLs harbored a greater number of favorable alleles for KRN compared to the H21 line, and could be employed as improved H21 lines in maize breeding programs.

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

“…With the rapid development of molecular genetic marker technology and quantitative genetics, segregation populationshave been used to detect QTLs for ERN, and have contributed to maize domestication and diversification (Doebley and Stec, 1991;Lauter and Doebley, 2002;Li et al, 2011;Lu et al, 2011;Lemmon and Doebley, 2014;Li et al, 2014;Yang et al, 2015). The most consistent result obtained from previous studies is that certain regions on the short segment of chromosome 2 have a large effect on ERN during domestication.…”

“…ERN is one of the key traits that distinguish maize and teosinte (Doebley, 2004). QTL mapping has been conducted in many studies, and a large number of QTLs have been identified for ERN across generations and environments in all10 maize chromosomes (Doebley et al, 1990;Doebley andStec, 1991, 1993;Veldboom and Lee, 1994;Austin and Lee, 1996;Szabó and Burr, 1996;Upadyayula et al, 2006;Yan et al, 2006;Briggs et al, 2007;Li et al, 2007;Guo et al, 2008;Karen Sabadin et al, 2008;Li et al, 2011;Lu et al, 2011;Cai et al, 2014;Lemmon and Doebley, 2014;Li et al, 2014;Tian et al, 2014;Yang et al, 2015). In this study, three QTLs (qERN2-1, qERN4-2, and qERN8-1) were consistently associated with ERN.…”

Identification of QTL for ear row number and two-ranked versus many-ranked ear in maize across four environments

“…An interesting case is the QTL detected on bin 4.08, which has been repeatedly reported to significantly affect ERN (Austin and Lee 1996;Li et al 2014a;Lee 1994, 1996;Yan et al 2006) and also contained QTLs for other agronomic traits, such as grain yield , tassel branch number (Chuck et al 2014), ear diameter ), kernel number per row (Lu et al 2011), kernel weight, width and thickness . Therefore, bin 4.08 is noteworthy for genetic improvement of morphological traits in maize.…”

“…For decades, with the rapid development of molecular genetic marker technology and quantitative genetics, diverse maize populations have been used to detect QTLs for ERN, which contribute to maize diversification, such as F 2 (Yu et al 2014), F 2:3 (Cai et al 2014;Choe and Rocheford 2012;Karen Sabadin et al 2008;Lu et al 2011;Veldboom and Lee 1994;Yan et al 2006), F 2:4 (Beavis et al 1994), BC 1 S 1 (Upadyayula et al 2006), BC 2 F 2 (Li et al 2007, BC 3 F 2:3 , BC 5 F 2:3 (Tian et al 2014), IF 2 (Tang et al 2010), CSSLs (Li et al 2014a) and RILs (Austin and Lee 1996;Guo et al 2008;Liu et al 2010). To date, few consistent QTL having large effects on ERN have been detected across diverse populations or environments.…”

Genetic analysis of maize kernel thickness by quantitative trait locus identification

“…Numerous studies on QTL identification in crops have been conducted, including in maize (Riedelsheimer et al, 2012;Almeida et al, 2014;Herzog et al, 2014), rice (Chen et al, 2014;Xu et al, 2014;Zhou et al, 2014), wheat (Bennett et al, 2012;Ishikawa et al, 2014;Katkout et al, 2014), sorghum (Rajkumar et al, 2013;Alam et al, 2014), and soybean (Liang et al, 2014;Mamidi et al, 2014). Regarding maize kernel-related traits, most studies that have used QTL mapping have focused on kernel number per ear Zhang et al, 2013;Li et al, 2014) or kernel weight (Tang et al, 2010;Ding et al, 2011;Prado et al, 2014). There have only been a few studies conducted on kernel thickness.…”