Identification of quantitative trait loci for leaf‐related traits in an IBM Syn10 DH maize population across three environments

Abstract

Leaf‐related traits (leaf length, leaf width, leaf area and leaf angle) are very important for the yield of maize (Zea mays L) due to their influence on plant type. Therefore, it is necessary to identify quantitative trait loci (QTLs) for leaf‐related traits. In this report, 221 doubled haploid lines (DHLs) of an IBM Syn10 DH population were provided for QTL mapping. In total, 54 QTLs were detected for leaf‐related traits in single environments using a high‐density genetic linkage map. Among them, only eight common QTLs were identified across two or three environments, and the common QTLs for the four traits explained 4.38%–19.99% of the phenotypic variation. qLL‐2‐1 (bin 2.09), qLW‐2‐2 (bin 2.09), qLW‐6‐3 (bin 6.07) and qLA‐5‐2 (bin 2.09) were detected in previous studies, and qLL‐1‐1, qLAr‐1‐1, qLAr‐2‐1 and qLA‐7‐1 may be new QTLs. Notably, qLW‐6‐3 and qLA‐5‐2 were found to be major QTLs explaining 19.99% and 10.96% of the phenotypic variation, respectively. Interestingly, we found three pairs of QTLs (qLW‐2‐2 and qLAr‐2‐1, qLW‐8‐1 and qLL‐8‐2, qLL‐3‐3 and qLAr‐3‐3) that control different traits and that were located on the same chromosome or in a nearby location. Moreover, nine pairs of loci with epistatic effects were identified for the four traits. These results may provide the foundation for QTL fine mapping and for an understanding of the genetic basis of variation in leaf‐related traits.

Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content

“…The IBM population has a high recombination rate and extensive phenotypic variability (Zhang et al 2010), which contributed to the identification of important genes. In our study, KMC in the IBM population ranged from 11.81 to 25.42% with coefficients of variation (CV) being 9.26-12.22%, which showed greater variation than the other traits in the population (Zhang et al 2016, Ma et al 2018.…”

“…QTL Cartographer version 1.17f was used for linkage analysis based on composite interval mapping (CIM) (Liu et al 2017), with LOD = 2.5 as the threshold. Two QTL with a distance <10 cM were treated as the same QTL (Ma et al 2018). Here, QTL were named according to the following rules: q + trait abbreviation‐chromosome number‐identified QTL serial number.…”

Genetic dissection of stalk lodging-related traits using an IBM Syn10 DH population in maize across three environments (Zea mays L.)

“…The genetic linkage map was composed of 5935 bin markers, and the total length of the map was 1767.45 cM. The mean genetic distance between the two adjacent bin markers was 0.29 cM (Ma et al 2018), which was suitable for QTL fine mapping. In this study, many smaller intervals of QTL were detected, including qLEDCK3-1 (PC: 3.5-3.8 Mb), qBSHT1-1 (PC: 290.9-291.2 Mb), qRFWT9-2 (PC: 154.3-154.6 Mb), qWIDT7-1 (PC:…”

Genome‐wide association studies and QTL mapping uncover the genetic architecture of ear tip‐barrenness in maize

“…An association panel of 332 inbred lines used for GWAS was collected from the Southwest China breeding program (Zhang et al ). The 274 doubled haploid (DH) lines from the intermated B73 × Mo17 (IBM) Syn10 DH population used for QTL mapping were constructed by six generations of intercrossing of the IBM Syn4 population (Ma et al ). As described in our previous study (Liu et al ), the association panel was grown in three locations in 2016: Hongya in the Sichuan province (HY, E104 ° 29′, N29 ° 59′), Ya'an in the Sichuan province (YA, E103 ° 14′, N30 ° 08′) and Xishuangbanna in the Yunnan province (XSBN, E100 ° 79′, N22 ° 0′).…”