scholarly journals I.4 Screening Experimental Designs for Quantitative Trait Loci, Association Mapping, Genotype-by Environment Interaction, and Other Investigations

2012 ◽  
Vol 3 ◽  
Author(s):  
Walter T. Federer ◽  
José Crossa
Keyword(s):  
Quantitative Trait Loci ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Genotype By Environment Interaction ◽  
Experimental Designs ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Trait Loci

scholarly journals Quantitative Trait Loci Controlling Light and Hormone Response in Two Accessions ofArabidopsis thaliana

Genetics ◽  
2002 ◽  
Vol 160 (2) ◽  
pp. 683-696 ◽  
Author(s):  
Justin O Borevitz ◽  
Julin N Maloof ◽  
Jason Lutes ◽  
Tsegaye Dabi ◽  
Joanna L Redfern ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Natural Variation ◽  
Red Light ◽  
Light Response ◽  
Environment Interaction ◽  
Isogenic Line ◽  
Hormone Response ◽  
New Genes ◽  
Trait Loci

AbstractWe have mapped quantitative trait loci (QTL) responsible for natural variation in light and hormone response between the Cape Verde Islands (Cvi) and Landsberg erecta (Ler) accessions of Arabidopsis thaliana using recombinant inbred lines (RILs). Hypocotyl length was measured in four light environments: white, blue, red, and far-red light and in the dark. In addition, white light plus gibberellin (GA) and dark plus the brassinosteroid biosynthesis inhibitor brassinazole (BRZ) were used to detect hormone effects. Twelve QTL were identified that map to loci not previously known to affect light response, as well as loci where candidate genes have been identified from known mutations. Some QTL act in all environments while others show genotype-by-environment interaction. A global threshold was established to identify a significant epistatic interaction between two loci that have few main effects of their own. LIGHT1, a major QTL, has been confirmed in a near isogenic line (NIL) and maps to a new locus with effects in all light environments. The erecta mutation can explain the effect of the HYP2 QTL in the blue, BRZ, and dark environments, but not in far-red. LIGHT2, also confirmed in an NIL, has effects in white and red light and shows interaction with GA. The phenotype and map position of LIGHT2 suggest the photoreceptor PHYB as a candidate gene. Natural variation in light and hormone response thus defines both new genes and known genes that control light response in wild accessions.

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

2021 ◽  
Author(s):  
Simon Rio ◽  
Deniz Akdemir ◽  
Tiago Carvalho ◽  
Julio Isidro y Sánchez
Keyword(s):  
Genomic Prediction ◽  
Genotype By Environment Interaction ◽  
Experimental Designs ◽  
Coefficient Of Determination ◽  
Environment Interaction ◽  
Genetic Covariance ◽  
Genotype By Environment ◽  
Wheat Population ◽  
Prediction Reliability ◽  
Genetic Value

Abstract Key message New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions. Abstract In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (G$$\times$$ × E). With genomic prediction, G$$\times$$ × E can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of G$$\times$$ × E. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.

scholarly journals Rapid and robust association mapping of expression quantitative trait loci

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Alex C Lam ◽  
Michael Schouten ◽  
Yurii S Aulchenko ◽  
Chris S Haley ◽  
Dirk-Jan de Koning
Keyword(s):  
Quantitative Trait Loci ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Expression Quantitative Trait Loci ◽  
Trait Loci

scholarly journals Association Mapping of Malting Quality Quantitative Trait Loci in Winter Barley: Positive Signals from Small Germplasm Arrays

2011 ◽  
Vol 4 (3) ◽  
pp. 256-272 ◽  
Author(s):  
Lucía Gutiérrez ◽  
Alfonso Cuesta-Marcos ◽  
Ariel J. Castro ◽  
Jarislav von Zitzewitz ◽  
Mark Schmitt ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Winter Barley ◽  
Malting Quality ◽  
Trait Loci

scholarly journals Characterization of natural provenances of Acacia mangium Willd. and Acacia auriculiformis A. Cunn. ex Benth. in Malaysia based on phenotypic traits

2017 ◽  
Vol 63 (No. 12) ◽  
pp. 562-576
Author(s):  
Asif Muhammad Javed ◽  
Dorairaj Deivaseeno ◽  
Wickneswari Ratnam
Keyword(s):  
Quantitative Trait Loci ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Acacia Mangium ◽  
Acacia Auriculiformis ◽  
Phenotypic Traits ◽  
Homogeneous Population ◽  
Stem Straightness ◽  
Trait Loci ◽  
Planting Materials

Acacia mangium Willdenow and Acacia auriculiformis A. Cunningham ex Bentham and their hybrid have become important planting species in Malaysia. Due to their high demand and consumption, development of high quality planting materials is desired. Conventional breeding of Acacia Miller is slow but the utilization of marker-assisted selection breeding can expedite the breeding process. Markers associated with quantitative trait loci (QTLs) required pedigreed populations whereas association mapping can be used directly on diverse germplasm. This study was conducted to screen provenances of A. mangium and A. auriculiformis of different geographical origins for their performance under the Malaysian environment. A. mangium exhibited superior traits compared to A. auriculiformis. More trait variation was observed within and between provenances of A. auriculiformis. Provenances from Queensland (QLD) were superior to those from Papua New Guinea (PNG) and Northern Territory. The best performing provenance with all three superior traits was from Claude River QTL of A. mangium and the worst was Bensbach Western Province, PNG belonging to A. auriculiformis. For individual traits like DBH, Morehead, PNG was superior. For plant height, Morehead, PNG was the superior provenance for A. mangium and Morehead River, QLD was from A. auriculiformis. For stem straightness the A. auriculiformis provenance Jardines Garden, QTL was superior to West of Morehead (PNG) for A. mangium. Multivariate analysis grouped provenances together based on similar traits and genetic similarity. These provenances can be used for seed families which can be treated as a homogeneous population for association mapping or for the development of segregating hybrid populations for Acacia breeding. For the purpose of utilization, provenances of A. mangium can be used for sawn timber. For fuelwood and charcoal industries, A. auriculiformis provenances should be preferred by selecting multi-stemmed trees. The most variable provenances with superior phenotypic traits can be integrated with the genotypic data e.g. single nucleotide polymorphism markers for association mapping to identify quantitative trait loci for marker-assisted breeding.

scholarly journals Quantitative Trait Loci × Maternal Cytoplasmic Environment Interaction for Development Rate inOncorhynchus mykiss

Genetics ◽  
2006 ◽  
Vol 175 (1) ◽  
pp. 335-347 ◽  
Author(s):  
Krista M. Nichols ◽  
Karl W. Broman ◽  
Kyle Sundin ◽  
Jennifer M. Young ◽  
Paul A. Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Development Rate ◽  
Environment Interaction ◽  
Trait Loci
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