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Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat.

Sharma S, Xu S, Ehdaie B, Hoops A, Close TJ, Lukaszewski AJ, Waines JG - Theor. Appl. Genet. (2010)

Bottom Line: It provides a simple and powerful approach to detect even small QTL effects using fewer progeny.This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect.A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat.

View Article: PubMed Central - PubMed

Affiliation: Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA.

ABSTRACT
A high-resolution chromosome arm-specific mapping population was used in an attempt to locate/detect gene(s)/QTL for different root traits on the short arm of rye chromosome 1 (1RS) in bread wheat. This population consisted of induced homoeologous recombinants of 1RS with 1BS, each originating from a different crossover event and distinct from all other recombinants in the proportions of rye and wheat chromatin present. It provides a simple and powerful approach to detect even small QTL effects using fewer progeny. A promising empirical Bayes method was applied to estimate additive and epistatic effects for all possible marker pairs simultaneously in a single model. This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect. A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat. Epistatic interactions were further partitioned into inter-genomic (wheat and rye alleles) and intra-genomic (rye-rye or wheat-wheat alleles) interactions affecting various root traits. Four common regions were identified involving all the QTL for root traits. Two regions carried QTL for almost all the root traits and were responsible for all the epistatic interactions. Evidence for inter-genomic interactions is provided. Comparison of mean values supported the QTL detection.

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Three-dimensional graphic representation of QTL effects for six different root traits of the 1RS-1BS recombinant bread wheat population. a Number of roots greater than 30 cm (NR > 30), b longest root length (LRL), c total root length of roots greater than 30 cm (TRL > 30), d shallow root weight (SRW), e deep root weight (DRW), and f dry root biomass (TRW). The main (additive) effects are on the diagonals and the epistatic effects are on the left triangle of the 3D plots (the graphical scales for LOD scores in individual graphs are different, as generated by the software program)
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Fig1: Three-dimensional graphic representation of QTL effects for six different root traits of the 1RS-1BS recombinant bread wheat population. a Number of roots greater than 30 cm (NR > 30), b longest root length (LRL), c total root length of roots greater than 30 cm (TRL > 30), d shallow root weight (SRW), e deep root weight (DRW), and f dry root biomass (TRW). The main (additive) effects are on the diagonals and the epistatic effects are on the left triangle of the 3D plots (the graphical scales for LOD scores in individual graphs are different, as generated by the software program)

Mentions: All root characters measured showed significant QTL effects on the short arm of chromosome 1 of rye and a total of 15 QTL effects were found. Six of these were additive and nine showed epistatic interactions (Table 1). Of the nine epistatic interactions, five were inter-genomic interactions between wheat and rye alleles and the rest were intra-genomic interactions. The highest single additive effect explained 57% of the phenotypic variation for NR; the same effect explained 56% of the total phenotypic variation for TRL (Table 1). This QTL is tightly linked to marker Pm8, a powdery mildew resistance locus. The highest intra-genomic epistatic effect explained 31% of the phenotypic variance for DRW with a LOD score of 7.61 (Table 1; Fig. 1e). It was detected between two adjacent regions marked on the map by loci Pm8 (13) and Gli-1, Glu-3 (14) (Figs. 1e, 2a, b). The highest inter-genomic epistatic effect was detected for NR. This inter-genomic interaction involved Pm8 (13) and Xucr_2 (2) (Fig. 2a, c) and explained 26% of the phenotypic variation with LOD score of 4.69 (Table 1; Fig. 1a).Fig. 1


Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat.

Sharma S, Xu S, Ehdaie B, Hoops A, Close TJ, Lukaszewski AJ, Waines JG - Theor. Appl. Genet. (2010)

Three-dimensional graphic representation of QTL effects for six different root traits of the 1RS-1BS recombinant bread wheat population. a Number of roots greater than 30 cm (NR > 30), b longest root length (LRL), c total root length of roots greater than 30 cm (TRL > 30), d shallow root weight (SRW), e deep root weight (DRW), and f dry root biomass (TRW). The main (additive) effects are on the diagonals and the epistatic effects are on the left triangle of the 3D plots (the graphical scales for LOD scores in individual graphs are different, as generated by the software program)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3037480&req=5

Fig1: Three-dimensional graphic representation of QTL effects for six different root traits of the 1RS-1BS recombinant bread wheat population. a Number of roots greater than 30 cm (NR > 30), b longest root length (LRL), c total root length of roots greater than 30 cm (TRL > 30), d shallow root weight (SRW), e deep root weight (DRW), and f dry root biomass (TRW). The main (additive) effects are on the diagonals and the epistatic effects are on the left triangle of the 3D plots (the graphical scales for LOD scores in individual graphs are different, as generated by the software program)
Mentions: All root characters measured showed significant QTL effects on the short arm of chromosome 1 of rye and a total of 15 QTL effects were found. Six of these were additive and nine showed epistatic interactions (Table 1). Of the nine epistatic interactions, five were inter-genomic interactions between wheat and rye alleles and the rest were intra-genomic interactions. The highest single additive effect explained 57% of the phenotypic variation for NR; the same effect explained 56% of the total phenotypic variation for TRL (Table 1). This QTL is tightly linked to marker Pm8, a powdery mildew resistance locus. The highest intra-genomic epistatic effect explained 31% of the phenotypic variance for DRW with a LOD score of 7.61 (Table 1; Fig. 1e). It was detected between two adjacent regions marked on the map by loci Pm8 (13) and Gli-1, Glu-3 (14) (Figs. 1e, 2a, b). The highest inter-genomic epistatic effect was detected for NR. This inter-genomic interaction involved Pm8 (13) and Xucr_2 (2) (Fig. 2a, c) and explained 26% of the phenotypic variation with LOD score of 4.69 (Table 1; Fig. 1a).Fig. 1

Bottom Line: It provides a simple and powerful approach to detect even small QTL effects using fewer progeny.This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect.A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat.

View Article: PubMed Central - PubMed

Affiliation: Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA.

ABSTRACT
A high-resolution chromosome arm-specific mapping population was used in an attempt to locate/detect gene(s)/QTL for different root traits on the short arm of rye chromosome 1 (1RS) in bread wheat. This population consisted of induced homoeologous recombinants of 1RS with 1BS, each originating from a different crossover event and distinct from all other recombinants in the proportions of rye and wheat chromatin present. It provides a simple and powerful approach to detect even small QTL effects using fewer progeny. A promising empirical Bayes method was applied to estimate additive and epistatic effects for all possible marker pairs simultaneously in a single model. This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect. A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat. Epistatic interactions were further partitioned into inter-genomic (wheat and rye alleles) and intra-genomic (rye-rye or wheat-wheat alleles) interactions affecting various root traits. Four common regions were identified involving all the QTL for root traits. Two regions carried QTL for almost all the root traits and were responsible for all the epistatic interactions. Evidence for inter-genomic interactions is provided. Comparison of mean values supported the QTL detection.

Show MeSH