scholarly journals Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Nikwan Shariatipour ◽  
Bahram Heidari ◽  
Ahmad Tahmasebi ◽  
Christopher Richards
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Grain Quality ◽  
Agronomic Traits ◽  
Meta Analysis ◽  
Crop Improvement ◽  
Comparative Genomic ◽  
Trait Loci ◽  
Stable Qtls ◽  
Improvement Programs

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment

2010 ◽  
Vol 37 (2) ◽  
pp. 85 ◽  
Author(s):  
Richard A. Richards ◽  
Greg J. Rebetzke ◽  
Michelle Watt ◽  
A. G. (Tony) Condon ◽  
Wolfgang Spielmeyer ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Water Productivity ◽  
Crop Improvement ◽  
Phenotypic Selection ◽  
Trait Loci ◽  
Sampling Cost ◽  
Major Bottleneck ◽  
Improvement Programs ◽  
Genomic Regions

Consistent gains in grain yield in dry environments have been made by empirical breeding although there is disturbing evidence that these gains may have slowed. There are few examples where an understanding of the physiology and the genetics of putative important drought-related traits has led to improved yields. Success will first depend on identifying the most important traits in the target regions. It will then depend on accurate and fast phenotyping, which, in turn, will lead to: (1) trait-based selection being immediately transferable into breeding operations and (2) being able to identify the underlying genes or the important genomic regions (quantitative trait loci), perhaps leading to efficient marker-based selection (MBS). Genetic complexity, extent of genotype × environment (G × E) interaction and sampling cost per line will determine value of phenotyping over MBS methods. Here, we review traits of importance in dry environments and review whether molecular or phenotypic selection methods are likely to be the most effective in crop improvement programs and where the main bottlenecks to selection are. We also consider whether selection for these traits should be made in dry environments or environments where there is no soil water limitation. The development of lines/populations for trait validation studies and for varietal development is also described. We firstly conclude that despite the spectacular improvements in molecular technologies, fast and accurate phenotyping remains the major bottleneck to enhancing yield gains in water-limited environments. Secondly, for most traits of importance in dry environments, selection is generally conducted most effectively in favourable moisture environments.

scholarly journals Quantitative Trait Loci: A Meta-analysis

Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 463-473
Author(s):  
Bruno Goffinet ◽  
Sophie Gerber
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Quantitative Trait ◽  
Meta Analysis ◽  
Trait Loci ◽  
Akaike Criterion ◽  
Using Data

Abstract This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.

Quantitative Trait Loci Influencing Mycotoxin Contamination of Maize: Analysis by Linkage Mapping, Characterization of Near-Isogenic Lines, and Meta-Analysis

Crop Science ◽  
2014 ◽  
Vol 54 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Santiago X. Mideros ◽  
Marilyn L. Warburton ◽  
Tiffany M. Jamann ◽  
Gary L. Windham ◽  
W. Paul Williams ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Linkage Mapping ◽  
Meta Analysis ◽  
Near Isogenic Lines ◽  
Mycotoxin Contamination ◽  
Isogenic Lines ◽  
Trait Loci

Genes and Quantitative Trait Loci Mapping for Major Agronomic Traits in Brassica napus L.

2018 ◽  
pp. 41-85 ◽  
Author(s):  
Régine Delourme ◽  
Anne Laperche ◽  
Anne-Sophie Bouchet ◽  
Mélanie Jubault ◽  
Sophie Paillard ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Quantitative Trait Loci Mapping ◽  
Brassica Napus L ◽  
Trait Loci

scholarly journals Wheat genetic resources – how to exploit?

2011 ◽  
Vol 47 (Special Issue) ◽  
pp. S43-S48 ◽  
Author(s):  
A. Börner ◽  
K. Neumann ◽  
B. Kobiljski
Keyword(s):  
Quantitative Trait Loci ◽  
Genetic Resources ◽  
Quantitative Trait ◽  
Molecular Mapping ◽  
Agronomic Traits ◽  
Wide Spectrum ◽  
Plant Genetic Resources ◽  
Germplasm Collections ◽  
Food And Agriculture ◽  
Trait Loci

It is estimated that world-wide existing germplasm collections contain about 7.5 million accessions of plant genetic resources for food and agriculture. Wheat (Triticum and Aegilops) represents the biggest group comprising 900 000 accessions. However, such a huge number of accessions is hindering a successful exploitation of the germplasm. The creation of core collections representing a wide spectrum of the genetic variation of the whole assembly may help to overcome the problem. Here we demonstrate the successful utilisation of such a core collection for the identification and molecular mapping of genes (Quantitative Trait Loci) determining the agronomic traits flowering time and grain yield, exploiting a marker-trait-association based technique. Significant marker-trait associations were obtained and are presented. The intrachromosomal location of many of these associations coincided with those of already identified major genes or quantitative trait loci, but others were detected in regions where no known genes have been located to date.

Genetics of Soybean Agronomic Traits: II. Interactions between Yield Quantitative Trait Loci in Soybean

Crop Science ◽  
1999 ◽  
Vol 39 (6) ◽  
pp. 1652-1657 ◽  
Author(s):  
J. H. Orf ◽  
K. Chase ◽  
F. R. Adler ◽  
L. M. Mansur ◽  
K. G. Lark
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Trait Loci

scholarly journals Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions

2013 ◽  
Vol 33 (2) ◽  
pp. 249-265 ◽  
Author(s):  
Elsayed Mansour ◽  
Ana M. Casas ◽  
M. Pilar Gracia ◽  
José Luis Molina-Cano ◽  
Marian Moralejo ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Trait Loci ◽  
Barley Population ◽  
Mediterranean Conditions
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