Genomics Assisted Improvement of Drought Tolerance in Maize: QTL Approaches

Tsonev, Stefan; Todorovska, Elena; Avramova, Viktoriya; Kolev, Stanislav; Abumhadi, N.; Christov, N.

doi:10.2478/v10133-009-0004-8

Cited by 30 publications

(22 citation statements)

References 28 publications

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“…Subsequently, large number of QTLs for maize complex traits were detected and mapped on all 10 maize chromosomes based on these linkage maps (Tsonev et al, 2009; Qiu et al, 2011). However, low marker density on these maps limits QTL mapping accuracy, which leads to low QTL mapping resolution (Beavis et al, 1994; Veldboom et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

Wang²,

Gong³

et al. 2017

Front. Plant Sci.

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Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F2 individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F2 progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F2 individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays.AGPv3.29 for each F2 individual. A total of 68,882 raw SNPs were discovered in the F2 population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies.

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Section: Introductionmentioning

confidence: 99%

High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

Wang²,

Gong³

et al. 2017

Front. Plant Sci.

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“…While direct selection for grain yield under DS has resulted in admirable gains in grain yield under stress (Bänziger et al, 2006;Cairns et al, 2013), further improvement requires the incorporation of additional selection traits (Cairns et al, 2012a(Cairns et al, , 2012b. In recent years, genetic and phenotypic markers have been searched extensively for drought tolerance of maize by high-throughput genomic and phenotyping approaches, respectively (Tuberosa and Salvi, 2006;Wen et al, 2011;Araus et al, 2012;Cairns et al, 2013;Prasanna et al, 2013;Araus and Cairns, 2014;Tsonev et al, 2014). Moreover, metabolic markers started to draw attention due to their close relationship with yield phenotypes (Fernie and Schauer, 2009;Redestig et al, 2011;Riedelsheimer et al, 2012aRiedelsheimer et al, , 2012bWitt et al, 2012;Degenkolbe et al, 2013).…”

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confidence: 99%

Metabolite profiles of maize leaves in drought, heat and combined stress field trials reveal the relationship between metabolism and grain yield

et al. 2015

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The development of abiotic stress-resistant cultivars is of premium importance for the agriculture of developing countries. Further progress in maize (Zea mays) performance under stresses is expected by combining marker-assisted breeding with metabolite markers. In order to dissect metabolic responses and to identify promising metabolite marker candidates, metabolite profiles of maize leaves were analyzed and compared with grain yield in field trials. Plants were grown under well-watered conditions (control) or exposed to drought, heat, and both stresses simultaneously. Trials were conducted in 2010 and 2011 using 10 tropical hybrids selected to exhibit diverse abiotic stress tolerance. Drought stress evoked the accumulation of many amino acids, including isoleucine, valine, threonine, and 4-aminobutanoate, which has been commonly reported in both field and greenhouse experiments in many plant species. Two photorespiratory amino acids, glycine and serine, and myoinositol also accumulated under drought. The combination of drought and heat evoked relatively few specific responses, and most of the metabolic changes were predictable from the sum of the responses to individual stresses. Statistical analysis revealed significant correlation between levels of glycine and myoinositol and grain yield under drought. Levels of myoinositol in control conditions were also related to grain yield under drought. Furthermore, multiple linear regression models very well explained the variation of grain yield via the combination of several metabolites. These results indicate the importance of photorespiration and raffinose family oligosaccharide metabolism in grain yield under drought and suggest single or multiple metabolites as potential metabolic markers for the breeding of abiotic stress-tolerant maize.

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“…Currently, QTL mapping has been applied for crop improvement (Kumar and Kumar, 2009), and a large number of molecular markers linked with traits have been obtained; moreover, some QTLs controlling agronomic traits have been successfully cloned (Zhou et al, 2011). In maize, numerous QTLs have been identified that control agronomic traits, and these QTLs have been mapped to 10 chromosomes in maize (Tsonev et al, 2009;Qiu et al, 2011). For the traits related to plant architecture, PH and EH have been frequently studied (Kraja and Dudley, 2000;Sibov et al, 2003;Lima et al, 2006;Malosetti et al, 2008;Bai et al, 2010), and a large number of QTLs were found to be located in different chromosomal regions.…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of agronomic traits associated with plant architecture by QTL mapping in maize

Zheng

Liu²

2013

Genet. Mol. Res.

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ABSTRACT. Maize (Zea mays L.) is one of the most important cereal crops worldwide, and increasing the grain yield and biomass has been among the most important goals of maize production. The plant architecture can determine the grain yield and biomass to some extent; however, the genetic basis of the link between the plant architecture and grain yield/biomass is unclear. In this study, an immortal F 9 recombinant inbred line population, derived from the cross Mo17 x Huangzao4, was used to detect quantitative trait loci (QTLs) for 3 traits associated with plant architecture under two nitrogen regimes: plant height, ear height, and leaf number. As a result, 8 and 10 QTLs were identified under the high nitrogen regime and low nitrogen regime, respectively. These QTLs mapped to chromosomes 1 (six QTLs), 2 (one QTL), 3 (one QTL), 7 (two QTLs), and 9 (eight QTLs), and had different genetic distances to their closest markers, ranging from 0 to 22.0 cM, explaining 4.7 to 20.5% of the phenotypic variance. Because of an additive effect, 9 and 9 could make the phenotypic values of traits increase and decrease to some extent, respectively. These results are beneficial for understanding the genetic basis of agronomic traits associated with plant architecture and for performing marker-assisted selection in maize breeding programs.

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Genomics Assisted Improvement of Drought Tolerance in Maize: QTL Approaches

Cited by 30 publications

References 28 publications

High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

Metabolite profiles of maize leaves in drought, heat and combined stress field trials reveal the relationship between metabolism and grain yield

Genetic analysis of agronomic traits associated with plant architecture by QTL mapping in maize

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