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Worland, A. J.; Börner, Andreas; Korzun, Viktor; Li, W.M.; Petrović, Sonja; Sayers, E. J.

doi:10.1023/a:1018327700985

Cited by 215 publications

(73 citation statements)

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“…Another point worth mentioning is that since markers represent attributes of lines, an appropriate model would include lines nested within markers (Malosetti et al 2007) and within subpopulations. When markers are explicitly included in LMM1 and LMM2 as fixed (or random) effects, it is not clear how the effect of relationship matrix A (or Other loci associated with grain yield in wheat are Eps (earliness per se) (Worland 1996;Van Beem et al 2005), Ppd (photoperiod insensitivity) (Worland 1996;Worland et al 1998;Van Beem et al 2005), Rht (semidwarf plant height) Ellis et al 2002), and Vrn (vernalization) (Worland 1996;Barret et al 2002;Yan et al 2003;Van Beem et al 2005).…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2007

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“…Photoperiod is the day length and number of long days that a wheat cultivar must reach (a threshold) for floral initiation [38]. The duration of exposure to light can be categorized into three groups namely, short-day (SD, 11-14 h), long-day (LD, 18 h), and day-neutral (DN) or facultative [39].…”

Section: The Influence Of Photoperiod Response Genes On the Floweringmentioning

confidence: 99%

“…The winter wheat and spring wheat varieties can be photoperiod-sensitive or photoperiod-insensitive. Photoperiod-insensitive varieties are early flowering both under SD and LD conditions, in contrast to the photoperiod-sensitive varieties that require exposure to LD for weeks before they can initiate flowering [38,40]. Several genes controlling photoperiod response have been successfully identified in wheat ( Table 2).…”

Section: The Influence Of Photoperiod Response Genes On the Floweringmentioning

confidence: 99%

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Impact of Growth Habit and Architecture Genes on Adaptation and Performance of Bread Wheat

Khumalo¹,

Maphosa²,

Tsilo³

2017

Wheat Improvement, Management and Utilization

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In bread wheat (Triticum aestivum L.), flowering time and plant stature are important phenological and agronomic traits for adaptation, yield potential, and yield stability. Timely flowering is critical for production, and the flowering window has to be late enough to avoid early season frosts but early enough to avoid late season stresses such as heat and terminal drought. Flowering time is controlled mainly by vernalization, photoperiod response, and earliness per se genes, which can be exploited to fine-tune growth and tailor flowering time for the production of desirable wheat cultivars. Tailoring flowering time could help reduce preharvest sprouting problems by escaping high temperatures and late season rainfall, which promote preharvest sprouting, hence yield loss. Concisely summarizing available information on flowering time and identifying research gaps could provide direction for future research. This chapter, therefore, discusses: (i) the progress made in discovering genes involved and the impact of their extensive allelic variation on flowering time, (ii) the potential benefits of tailoring wheat's flowering time to improve yield, and (iii) the benefits of introgressing genes for other complimentary traits, such as semidwarf and preharvest sprouting resistance on advanced lines to achieve higher yield, thus, sustainable food security.

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“…Photoperiod response is closely associated with adaptability and grain yield in wheat varieties (11,31,32,33). Photoperiod insensitive wheat varieties are widespread worldwide.…”

Section: Articlementioning

confidence: 99%

Allele Variation in Loci for Adaptive Response and Plant Height and its Effect on Grain Yield in Wheat

Kolev

Ganeva

Kostov³

et al. 2010

Biotechnology & Biotechnological Equipment

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Cited by 215 publications

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

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

Impact of Growth Habit and Architecture Genes on Adaptation and Performance of Bread Wheat

Allele Variation in Loci for Adaptive Response and Plant Height and its Effect on Grain Yield in Wheat

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