Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize

Zhang, Hao; Li, Xinhai; Liu, Xiulin; Xie, Chuanxiao; Li, Mingshun; Zhang, Degui; Zhang, Shihuang

doi:10.1007/s10681-009-0091-5

Cited by 90 publications

(78 citation statements)

References 56 publications

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“…However, QTL identified in a given environment and under given genetic backgrounds are specific to those conditions. In addition, as various authors pointed out the number of distinct QTL which can be detected with usual experimental designs is limited (Hao et al 2010;Veyrieras et al 2007;Wisser et al 2006). As Chardon et al (2004) stated, synthesizing this information provides an unprecedented opportunity to understand the genetic variation of a trait under a wide set of conditions.…”

Section: Discussionmentioning

confidence: 99%

Quantitative trait loci associated with grain moisture at harvest for line per se and testcross performance in maize: a meta-analysis

2012

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Associations between quantitative trait loci (QTL) and corn (Zea mays L.) percentage grain moisture at harvest (GM) have been reported in several studies; however, stability of QTL across breeding populations has depended upon the genetic backgrounds evaluated, the generation of progeny evaluated (inbred lines per se (LP) or test crosses (TC)), and/or the environments within which populations were evaluated. Marker assisted breeding for improved GM in corn should be aided greatly by identification of QTL for GM that are stable across environments, genetic backgrounds and generations. One aid toward this end is Meta-analysis. QTL associated with GM were identified in a TC population planted at five locations. Meta-analysis was performed by evaluating 184 QTL assembled from LP (146 QTL) and TC (38 QTL) data consolidated from literature. Eight MetaQTL were associated with GM in both LP and TC populations when LP and TC data were evaluated independently. Thirty-four MetaQTL were associated with GM when LP and TC data were evaluated jointly. MetaQTL identified in this study should be stable in marker assisted breeding programs since QTL were associated with improved GM across multiple environments, genotypes and populations.

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Section: Discussionmentioning

confidence: 99%

Quantitative trait loci associated with grain moisture at harvest for line per se and testcross performance in maize: a meta-analysis

2012

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“…In contrast, in a water-stressed environment, 3 QTLs were mapped on chromosomes 1, 2, and 9. Until now, most ecological environments used for QTL mapping of maize traits have been focused on different water contents in the soil, involving different water regimes in the same field or different experimental regions with different rainfall rates (Messmer et al, 2009;Zhang et al, 2010;Hao et al, 2011). However, low nitrogen (N) conditions, for example, during agricultural drought, represent a major source of loss in maize yield.…”

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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“…Meta-QTL analysis has also been successfully conducted in several other crops like maize Truntzler et al, 2010;Hao et al, 2010;Li et al, 2011;Wang et al, 2006;Coque et al, 2008), cotton (Rong et al, 2007), rice (Ballini et al, 2008;Norton et al, 2008;Khowaja et al, 2009), rapeseed (Shi et al, 2009), potato (Danan et al, 2011), cocoa (Lanaud et al, 2009), soybean (Guo et al, 2006;Sun et al, 2012) and apricot (Marandel et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of QTLs Involved in Pre-harvest Sprouting Tolerance and Dormancy in Bread Wheat

Tyagi¹,

Gupta²

2012

tgg

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In common wheat, meta-analysis of quantitative trait loci (QTL) associated with pre-harvest sprouting tolerance (PHST) and dormancy was carried out using results of 15 studies involving 15 different mapping populations. The study was restricted to only four chromosomes including three chromosomes of homoeologous group 3 (3A, 3B and 3D) and the chromosome 4A, since QTLs for PHST and dormancy on these four chromosomes were reported in several earlier studies, thus making these chromosomes suitable for meta-QTL analysis. The software BioMercator 2.1 was used to build a consensus map, and QTLs were projected on to this map for conducting meta-analysis. Using 36 of the 50 original QTLs, 8 meta-QTLs (MQTLs) were identified: 7 MQTLs were located on chromosomes of homoeologous group 3 including 3A (2 MQTL), 3B (3 MQTL) and 3D (2 MQTL), 1 MQTL was located on chromosome 4A. Confidence intervals (C.I.) for each of these 8 MQTLs were particularly narrow. The mapping information for 50 QTLs was also used for "overview" analysis to visualize important genomic regions carrying the MQTLs for PHST and dormancy. Co-localizations between candidate genes for dormancy/PHST (taVP1 and TaGA20-ox1) and MQTL positions appeared globally significant, although these candidate genes deserve further investigation. Markers associated with 8 MQTLs identified during the present study should prove helpful for introgression of tolerance against pre-harvest sprouting (PHS) into high yielding wheat varieties through marker-assisted selection (MAS).

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Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize

Cited by 90 publications

References 56 publications

Quantitative trait loci associated with grain moisture at harvest for line per se and testcross performance in maize: a meta-analysis

Quantitative trait loci associated with grain moisture at harvest for line per se and testcross performance in maize: a meta-analysis

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

Meta-analysis of QTLs Involved in Pre-harvest Sprouting Tolerance and Dormancy in Bread Wheat

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