Literature DB >> 24162195

Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley.

I Romagosa1, S E Ullrich, F Han, P M Hayes.   

Abstract

The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the 'Steptoe' x 'Morex' cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.

Entities:  

Year:  1996        PMID: 24162195     DOI: 10.1007/BF00225723

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  6 in total

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Authors:  C S Haley; S A Knott
Journal:  Heredity (Edinb)       Date:  1992-10       Impact factor: 3.821

2.  A comparison of Hordeum bulbosum-mediated haploid production efficiency in barley using in vitro floret and tiller culture.

Authors:  F Q Chen; P M Hayes
Journal:  Theor Appl Genet       Date:  1989-05       Impact factor: 5.699

3.  Empirical threshold values for quantitative trait mapping.

Authors:  G A Churchill; R W Doerge
Journal:  Genetics       Date:  1994-11       Impact factor: 4.562

4.  Quantitative trait locus effects and environmental interaction in a sample of North American barley germ plasm.

Authors:  P M Hayes; B H Liu; S J Knapp; F Chen; B Jones; T Blake; J Franckowiak; D Rasmusson; M Sorrells; S E Ullrich; D Wesenberg; A Kleinhofs
Journal:  Theor Appl Genet       Date:  1993-11       Impact factor: 5.699

5.  A molecular, isozyme and morphological map of the barley (Hordeum vulgare) genome.

Authors:  A Kleinhofs; A Kilian; M A Saghai Maroof; R M Biyashev; P Hayes; F Q Chen; N Lapitan; A Fenwick; T K Blake; V Kanazin; E Ananiev; L Dahleen; D Kudrna; J Bollinger; S J Knapp; B Liu; M Sorrells; M Heun; J D Franckowiak; D Hoffman; R Skadsen; B J Steffenson
Journal:  Theor Appl Genet       Date:  1993-07       Impact factor: 5.699

6.  MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations.

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Journal:  Genomics       Date:  1987-10       Impact factor: 5.736

  6 in total
  11 in total

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Journal:  Plant Mol Biol       Date:  2002 Mar-Apr       Impact factor: 4.076

2.  Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars.

Authors:  Arnold T W Kraakman; Rients E Niks; Petra M M M Van den Berg; Piet Stam; Fred A Van Eeuwijk
Journal:  Genetics       Date:  2004-09       Impact factor: 4.562

3.  Mapping environment-specific quantitative trait loci.

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Journal:  Genetics       Date:  2010-08-30       Impact factor: 4.562

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Authors:  C A Hackett; J Russell; L Jorgensen; S L Gordon; R M Brennan
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5.  Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat.

Authors:  Bing Song Zheng; Jacques Le Gouis; Martine Leflon; Wen Ying Rong; Anne Laperche; Maryse Brancourt-Hulmel
Journal:  Theor Appl Genet       Date:  2010-08-10       Impact factor: 5.699

6.  Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize.

Authors:  Laurence Moreau; Alain Charcosset; André Gallais
Journal:  Theor Appl Genet       Date:  2004-11-12       Impact factor: 5.699

7.  An expectation and maximization algorithm for estimating Q X E interaction effects.

Authors:  Fuping Zhao; Shizhong Xu
Journal:  Theor Appl Genet       Date:  2012-05       Impact factor: 5.699

8.  Precision-mapping and statistical validation of quantitative trait loci by machine learning.

Authors:  Justin Bedo; Peter Wenzl; Adam Kowalczyk; Andrzej Kilian
Journal:  BMC Genet       Date:  2008-05-02       Impact factor: 2.797

9.  Genome-wide association mapping for kernel and malting quality traits using historical European barley records.

Authors:  Inge E Matthies; Marcos Malosetti; Marion S Röder; Fred van Eeuwijk
Journal:  PLoS One       Date:  2014-11-05       Impact factor: 3.240

10.  Epistasis interaction of QTL effects as a genetic parameter influencing estimation of the genetic additive effect.

Authors:  Jan Bocianowski
Journal:  Genet Mol Biol       Date:  2013-03-04       Impact factor: 1.771

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