QTL Identification for Brace‐Root Traits of Maize in Different Generations and Environments

Abstract

Elucidating the correlations among maize (Zea mays L.) brace‐root traits and identifying the quantitative trait loci (QTL) that control the traits are important for genetic improvement of brace‐root traits. Two maize inbred lines, Yi17 (well‐developed root system) and Yi16 (poorly developed root system), an F2 population derived from their cross containing 276 individuals, and an F2:3 population containing 241 families were used to analyze the correlations among brace‐root traits and determine the QTL for brace‐root traits at Xiema and Hechuan in 2014 and 2015. All brace‐root traits were highly significantly correlated with each other. In particular, brace‐root diameter was highly correlated with brace‐root fresh weight (r = 0.730), brace‐root dry weight (r = 0.729), root fresh weight (r = 0.734), and root dry weight (r = 0.754). A total of 212 simple sequence repeat (SSR) markers were used to develop a genetic map based on the F2 population. The total length of the genetic map was 1558.9 cM, with a mean interval of 7.35 cM between adjacent markers. Ninety‐three QTL controlling the brace‐root traits were detected in generations F2 at Xiema in 2014 and F2:3 at Xiema and Hechuan in 2015. However, only two consistent major QTL were identified in F2:3 generation—qBRTN8b for brace‐root tier number and qBRD8b for brace‐root diameter. The qBRTN8b was located in the mmc0181 to bnlg1031 interval (bin 8.06) on chromosome 8, which explained 32.64% (at Xiema) and 16.18% (at Hechuan) of phenotypic variation. The qBRD8b was mapped in the umc2367 to umc1846 interval (bin 8.05) on chromosome 8, which explained 14.28% (at Xiema) and 10.41% (at Hechuan) of phenotypic variation. Moreover, three new important chromosomal regions harboring QTL for brace‐root traits were detected—bins 5.04, 6.06, and 8.05 to 8.06. These results could provide a very important reference for evaluating root traits under field conditions and for fine mapping QTL of brace‐root traits in maize.

Maize brace roots provide stalk anchorage

“…Although optimal brace root traits for root lodging-resistance have been identified, there are limited reports to define the genetic basis of these traits. The studies that have been reported primarily focus on the total number of brace root whorls [Zhang et al, 2018a, Ku et al, 2012, Gu et al, 2017, Zhang et al, 2018b, with only one study differentiating between the number of brace root whorls that enter the soil and the total number of brace root whorls [Ku et al, 2012]. In this study, an analysis of recombinant inbred line (ril) and immortalized F2 (if2) populations identified both shared and independent quantitative trait loci (qtl) for brace root whorls in the soil and the total number of brace root whorls [Ku et al, 2012].…”

Maize brace roots provide stalk anchorage

“…Although optimal brace root traits for root lodging-resistance have been identified, there are limited reports to define the genetic basis of these traits. The studies that have been reported primarily focus on the total number of brace root whorls (Gu et al, 2017;Ku et al, 2012;Zhang, et al, 2018a;Zhang, et al, 2018b), with only one study differentiating between the number of brace root whorls that enter the soil and the total number of brace root whorls (Ku et al, 2012). In this study, an analysis of recombinant inbred line (RIL) and immortalized F2 (IF2) populations identified both shared and independent quantitative trait loci (QTL) for brace root whorls in the soil and the total number of brace root whorls (Ku et al, 2012).…”

Mapping QTLs using High-Density SNPs Genotyped by Sequencing Reveals Novel Potential Regions Underlying Maize Root Morphological Traits at Seedling Stage

“…Recently, several studies have attempted to examine the genetic basis of maize root system architectures in both field (Zaidi et al 2016;Gu et al 2017;Zhang et al 2018) and controlled conditions (Pace et al 2015a, b;Zurek et al 2015;Song et al 2016;Liu et al 2017;Sanchez et al 2018). However, most of those studies used relatively short time-point intervals and low-density marker genetic maps, resulting in large inter-marker intervals (Song et al 2016).…”