QTL verification of grain protein content and its correlation with oil content by using connected RIL populations of high-oil maize

Yang, Guohu; Dong, Yuchen; Li, Y.L.; Wang, Q.L.; Shi, Qingling; Zhou, Qiang

doi:10.4238/2014.february.14.18

Cited by 14 publications

(20 citation statements)

References 37 publications

(59 reference statements)

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“…In addition, in this study, two QTLs for oil concentration were detected on chromosomes 8 and 10, including qOIL08-01 and qOIL10-01. In addition, two QTLs for protein concentration were detected on chromosomes 1 and 5, including qPRO01-01 and qPRO05-01, which have not previously been reported in maize, demonstrating that the QTLs had a population-specific effect and were strongly influenced by different genetic backgrounds Yang et al, 2014). Therefore, the new QTLs detected in this study supplement the QTLs reported for kernel oil and protein concentration in previous reports.…”

Section: Discussionsupporting

confidence: 79%

Detection of quantitative trait loci for kernel oil and protein concentration in a B73 and Zheng58 maize cross

Yang

Zhang

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Maize (Zea mays L.) is one of the most important food crops throughout the world, and provides oil and proteins to humans and livestock. Kernel oil and protein content in maize are two complex quantitative traits. In order to identify quantitative trait loci (QTL) controlling oil and protein concentration in maize kernels, and to evaluate their genetic effects, QTL analysis was conducted on an F 3:4 population derived from a cross between an inbred line with a low oil and protein concentration (Zheng58) and an inbred line with a higher oil and protein concentration (B73). A total of 189 polymorphic simple sequence repeat markers were used to construct a linkage map. Eleven QTLs for kernel oil concentration were detected on nine chromosomes, except for chromosome 9. A single QTL explained 4.6 to 11.1% of the phenotypic variance. Ten QTLs for kernel protein concentration were also detected on nine chromosomes, except for chromosome 9. A single QTL explained 4.2 to 11.4% of the phenotypic variance. Interestingly, novel QTLs for oil concentration (qOIL08-01 and qOIL10-01) and QTLs for protein concentration (qPRO01-01 and qPRO05-01) were specific to the population studied, which could explain 7.1 to 11.1% of the phenotypic variance. These results will provide better understanding of the genetic basis of oil and protein concentrations in maize. The markers closely linked with the QTLs will facilitate breeding of maize varieties with high oil and protein concentrations through molecular marker-assisted selection.

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Section: Discussionsupporting

confidence: 79%

Detection of quantitative trait loci for kernel oil and protein concentration in a B73 and Zheng58 maize cross

Yang

Zhang

et al. 2016

Genet. Mol. Res.

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“…() and Yang et al. () identified a common QTL on chromosome 8. Five common QTL on chromosomes 1, 2 (2), 5 and 7 were identified for two traits across three environments in one population, Wassom et al.…”

Section: Discussionmentioning

confidence: 99%

“…, Yang et al. , ). However, little is known about the causative genetic factors for the QTL of kernel composition.…”

Section: Discussionmentioning

confidence: 99%

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Dissection of the genetic architecture for grain quality‐related traits in three RIL populations of maize (Zea mays L.)

Wang

Liu

et al. 2015

Plant Breeding

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To manipulate the composition of the maize kernel to meet future needs, an understanding of the molecular regulation of kernel quality‐related traits is required. In this study, the quantitative trait loci (QTL) for the concentrations of grain protein, starch and oil were identified using three sets of RIL populations in three environments. The genetic maps and the initial QTL were integrated using meta‐analyses. A total of 38 QTL were identified, including 15 in population 1, 12 in population 2 and 11 in population 3. The individual effects ranged from 2.87% to 13.11% of the phenotypic variation, with seven QTL each contributing over 10%. One common QTL was found for the concentrations of grain protein and starch in bin 3.09 in the three environments and the three RIL populations. Of the 38 initial QTL, 22 were integrated into eight mQTL by meta‐analysis. mQTL3 and mQTL8 of the key mQTL in which the initial QTL displayed R2 > 10% included six and three initial QTL for grain protein and starch concentrations from two or three populations, respectively. These results will provide useful information for marker‐assisted selection to improve the quality of the maize kernel.

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“…Flowering-related traits of maize have been extensively studied in China, and in other countries, using different populations and methods (Wang et al, 2010;Yang, 2012;Zheng et al, 2011Zheng et al, , 2012Wei et al, 2014). F 2:3 populations and recombinant inbred lines (RILs) are useful in the study of flowering traits, plant types, yield, and resistance (Lima et al, 2006;Salgado et al, 2008;Ku et al, 2012;Yang et al, 2014a). Genetic mapping studies on flowering traits of maize are influenced by the genetic background, environment, population, marker numbers, and mapping methods used (Austin and Lee, 1996;Li et al, 2007Li et al, , 2011.…”

Section: Introductionmentioning

confidence: 99%

Comparative quantitative trait locus mapping of maize flowering-related traits in an F2:3 and recombinant inbred line population

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Flowering-related traits in maize are affected by complex factors and are important for the improvement of cropping systems in the maize zone. Quantitative trait loci (QTLs) detected using different materials and methods usually vary. In the present study, 266 maize (Zea mays) F 2:3 families and 301 recombinant inbred lines (RIL) derived from a cross between 08-641 (founding parent from southeast China) and Ye478 (founding parent from China) were evaluated for four flowering-related traits, including days to tasseling (DTT), days to pollen shedding (DPS), days to silking (DTS), and anthesis-silking interval. Sixty-six QTLs controlling the target traits were detected in the F 2:3 and RIL populations via single environment analysis and joint analysis across all environments (JAAE). The QTLs explained 0.8-13.47% of the phenotypic variation, with 12 QTLs explaining more than 10%. The results of meta-QTL (MQTL) analysis indicated that 41 QTLs could be integrated into 14 MQTLs. One MQTL included 2.9 QTLs, ranging from two to ten QTLs for one to three traits. QTLs, including MQTL1-1 and MQTL9-1, were detected across the F 2:3 and RIL populations via SAE and JAAE. Among the MQTLs, nine QTLs were integrated into MQTL9-1 and affected DTT, DPS, and DTS, with the favored allele being derived from 08-641. MQTL3-2 showed high phenotypic variation and was suitable for fine mapping to determine the genetic mechanisms of flowering. MQTL3-2 could be applied to improve inbred lines using marker-assisted selection.

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QTL verification of grain protein content and its correlation with oil content by using connected RIL populations of high-oil maize

Cited by 14 publications

References 37 publications

Detection of quantitative trait loci for kernel oil and protein concentration in a B73 and Zheng58 maize cross

Detection of quantitative trait loci for kernel oil and protein concentration in a B73 and Zheng58 maize cross

Dissection of the genetic architecture for grain quality‐related traits in three RIL populations of maize (Zea mays L.)

Comparative quantitative trait locus mapping of maize flowering-related traits in an F2:3 and recombinant inbred line population

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