Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452 × 'AC Domain'

McCartney, Curt A.; Somers, Daryl J.; Humphreys, D. G.; Lukow, O. M.; Ames, Nancy; Noll, J. S.; Cloutier, Sylvie; McCallum, Brent D.

doi:10.1139/g05-055

Cited by 220 publications

(206 citation statements)

References 56 publications

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“…A region involving three QTL at position 33-36 cM on 3D detected in E1, E3 and E4 (Fig. 6) co-incided with previous studies (McCartney et al 2005;Somers et al 2004). Two QTL for TKW on 7A shared the same marker interval with QTL reported in previous studies, suggesting the existence of a locus for TKW in this region (Fig.…”

Section: Relationship Between Stay-green and Agronomic Traitssupporting

confidence: 83%

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Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Shi

Azam

et al. 2017

Euphytica

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Wheat (Triticum aestivum L.) yield is directly proportional to physio-morphological traits. A high-density genetic map consisting of 2575 markers was used for mapping QTL controlling stay-green and agronomic traits in wheat grown under four diverse water regimes. A total of 108 additive QTL were identified in target traits. Among them, 28 QTL for chlorophyll content (CC) were detected on 11 chromosomes, 43 for normalized difference vegetation index (NDVI) on all chromosomes except 5B, 5D, and 7D, five for spikes per plant (NSP) on different chromosomes, nine for plant height (PH) on four chromosomes, and 23 for thousand-kernel weight (TKW) on 11 chromosomes. Considering all traits, the phenotypic variation explained (PVE) ranged from 3.61 to 41.62%. A major QTL, QNDVI.cgb-5A.7, for NDVI with a maximum PVE of 20.21%, was located on chromosome 5A. A stable and major PH QTL was observed on chromosome 4D with a PVE close to 40%. Most distances between QTL and corresponding flanking markers were less than 1 cM, and approximately one-third of the QTL coincided with markers. Each of 16 QTL clusters on 10 chromosomes controlled more than one trait and therefore could be regarded as pleiotropic regions in response to different water regimes. Forty-one epistatic QTL were identified for all traits having PVE of 6.00 to 25.07%. Validated QTL closely linked to flanking markers will be beneficial for marker-assisted selection in improving drought-tolerance in wheat.

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Section: Relationship Between Stay-green and Agronomic Traitssupporting

confidence: 83%

“…A cluster for PH and TKW on 4D (Fig. 3) was reported in other studies (McCartney et al 2005;Huang et al 2006;Wang et al 2009). There were one QTL for NDVI and four QTL for CC at this locus, harboring QTL for plant height.…”

Section: Qtl Clustering and Pleiotropysupporting

confidence: 81%

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Shi

Azam

et al. 2017

Euphytica

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“…Comparative analysis with the known major QTL for yield in wheat: A survey of the literature reporting QTL for GY and related agronomic traits in both tetraploid and hexaploid wheat (Li et al 2002;Peng et al 2003;Elouafi and Nachit 2004;Huang et al 2004Huang et al , 2006McCartney et al 2005;Quarrie et al 2005Quarrie et al , 2007Marza et al 2006;Narasimhamoorthy et al 2006;Kuchel et al 2007;Kumar et al 2007;Ma et al 2007) revealed rather different scenarios for the two major GY QTL identified in our study. Our comparative analysis was carried out by relating the (LOD À 1) supporting interval of the QTL to the Ta-SSR-2004 consensus map (Somers et al 2004).…”

Section: Discussionmentioning

confidence: 55%

“…2BL (Bö rner et al 2002;Marza et al 2006), and QTL for GY, kernel weight, and number of kernels per spike have been reported on chr. 2AL (Bö rner et al 2002;Peng et al 2003;Huang et al 2004;McCartney et al 2005), but not in the distal region. Conversely, major QTL for GY have been identified in two different genetic backgrounds (Bö rner et al 2002;Campbell et al 2003) on the short distal regions of chr.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Trait Loci for Grain Yield and Adaptation of Durum Wheat (Triticum durum Desf.) Across a Wide Range of Water Availability

et al. 2008

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Grain yield is a major goal for the improvement of durum wheat, particularly in drought-prone areas. In this study, the genetic basis of grain yield (GY), heading date (HD), and plant height (PH) was investigated in a durum wheat population of 249 recombinant inbred lines evaluated in 16 environments (10 rainfed and 6 irrigated) characterized by a broad range of water availability and GY (from 5.6 to 58.8 q ha À1 ). Among the 16 quantitative trait loci (QTL) that affected GY, two major QTL on chromosomes 2BL and 3BS showed significant effects in 8 and 7 environments, with R 2 values of 21.5 and 13.8% (mean data of all 16 environments), respectively. In both cases, extensive overlap was observed between the LOD profiles of GY and PH, but not with those for HD. QTL specific for PH were identified on chromosomes 1BS, 3AL, and 7AS. Additionally, three major QTL for HD on chromosomes 2AS, 2BL, and 7BS showed limited or no effects on GY. For both PH and GY, notable epistasis between the chromosome 2BL and 3BS QTL was detected across several environments.

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“…5. A QTL for GY was mapped to 4AS near marker wmc48 (McCartney et al 2005b). Our complete map (supplemental Figure S4 at http://www.genetics.org/ supplemental/) shows wmc48 to be located 13.4 cM from wPt2788 ( Figure 2d) and significantly associated with GY.…”

Section: Discussionmentioning

confidence: 94%

Association Analysis of Historical Bread Wheat Germplasm Using Additive Genetic Covariance of Relatives and Population Structure

et al. 2007

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Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker-trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype 3 environment interaction, thereby making possible the identification of markers contributing to both additive and additive 3 additive interaction effects of traits.A useful new tool for crop genetic improvement is the identification of polymorphic markers associated with phenotypic variation for important traits by means of linkage disequilibrium (LD) between loci ( Thornsberry et al. 2001;Flint-Garcia et al. 2003). A major advantage of this approach over conventional linkage mapping is that it does not require the timeconsuming and expensive generation of specific genetic populations. LD is determined by the physical distance of the loci across chromosomes and has proven useful for dissecting complex traits because it offers fine-scale mapping due to the inclusion of historical recombination (Lynch and Walsh 1998). However, false positive correlation between markers and traits can arise in the absence of physical proximity due to population structure caused by admixture, mating system, and genetic drift or by artificial or natural selection during evolution, domestication, or plant improvement ( Jannink and Walsh 2002). False associations can also be caused by alleles occurring at very low frequencies in the initial population (Breseghello and Sorrells 2006a,b). These factors create LD between loci that are not physically linked and cause a high rate of false positives when relating polymorphic markers to phenotypic trait variation. Thus, separating LD due to physical linkage from LD due to population structure is a critical prerequisite in association analyses.Population structure can be quantified using Bayesian analysis, which has been effective for assigning individuals to subpopulations (Q matrix) using unlinked markers (Pritchard et al. 2000). Other multivariate statistical analyses such as classification (clustering) and ordination (scaling) can al...

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Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452 × 'AC Domain'

Cited by 220 publications

References 56 publications

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Quantitative Trait Loci for Grain Yield and Adaptation of Durum Wheat (Triticum durum Desf.) Across a Wide Range of Water Availability

Association Analysis of Historical Bread Wheat Germplasm Using Additive Genetic Covariance of Relatives and Population Structure

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