Literature DB >> 9560414

Approximate analysis of QTL-environment interaction with no limits on the number of environments.

A B Korol1, Y I Ronin, E Nevo.   

Abstract

An approach is presented here for quantitative trait loci (QTL) mapping analysis that allows for QTL x environment (E) interaction across multiple environments, without necessarily increasing the number of parameters. The main distinction of the proposed model is in the chosen way of approximation of the dependence of putative QTL effects on environmental states. We hypothesize that environmental dependence of a putative QTL effect can be represented as a function of environmental mean value of the trait. Such a description can be applied to take into account the effects of any cosegregating QTLs from other genomic regions that also may vary across environments. The conducted Monte-Carlo simulations and the example of barley multiple environments experiment demonstrate a high potential of the proposed approach for analyzing QTL x E interaction, although the results are only approximated by definition. However, this drawback is compensated by the possibility to utilize information from a potentially unlimited number of environments with a remarkable reduction in the number of parameters, as compared to previously proposed mapping models with QTL x E interactions.

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Mesh:

Year:  1998        PMID: 9560414      PMCID: PMC1460115     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  11 in total

1.  Interval mapping of quantitative trait loci employing correlated trait complexes.

Authors:  A B Korol; Y I Ronin; V M Kirzhner
Journal:  Genetics       Date:  1995-07       Impact factor: 4.562

2.  Multiple trait analysis of genetic mapping for quantitative trait loci.

Authors:  C Jiang; Z B Zeng
Journal:  Genetics       Date:  1995-07       Impact factor: 4.562

3.  Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

Authors:  E S Lander; D Botstein
Journal:  Genetics       Date:  1989-01       Impact factor: 4.562

4.  [Linkage between quantitative and marker loci. V. Joint analysis of various marker and quantitative traits].

Authors:  A B Korol'; I A Preĭgel'; N I Bocharnikova
Journal:  Genetika       Date:  1987-08

5.  Evolution of genetic variability in a spatially heterogeneous environment: effects of genotype-environment interaction.

Authors:  S Via; R Lande
Journal:  Genet Res       Date:  1987-04       Impact factor: 1.588

6.  Maximum likelihood techniques for the mapping and analysis of quantitative trait loci with the aid of genetic markers.

Authors:  J I Weller
Journal:  Biometrics       Date:  1986-09       Impact factor: 2.571

7.  Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments.

Authors:  A H Paterson; S Damon; J D Hewitt; D Zamir; H D Rabinowitch; S E Lincoln; E S Lander; S D Tanksley
Journal:  Genetics       Date:  1991-01       Impact factor: 4.562

8.  Effect of ignoring genotype-environment interaction on segregation analysis of quantitative traits.

Authors:  L Tiret; L Abel; R Rakotovao
Journal:  Genet Epidemiol       Date:  1993       Impact factor: 2.135

9.  High resolution of quantitative traits into multiple loci via interval mapping.

Authors:  R C Jansen; P Stam
Journal:  Genetics       Date:  1994-04       Impact factor: 4.562

10.  Precision mapping of quantitative trait loci.

Authors:  Z B Zeng
Journal:  Genetics       Date:  1994-04       Impact factor: 4.562

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  14 in total

1.  Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits.

Authors:  A B Korol; Y I Ronin; A M Itskovich; J Peng; E Nevo
Journal:  Genetics       Date:  2001-04       Impact factor: 4.562

Review 2.  Evolution of genome-phenome diversity under environmental stress.

Authors:  E Nevo
Journal:  Proc Natl Acad Sci U S A       Date:  2001-05-22       Impact factor: 11.205

3.  Studying the genetic basis of drought tolerance in sorghum by managed stress trials and adjustments for phenological and plant height differences.

Authors:  P K Sabadin; M Malosetti; M P Boer; F D Tardin; F G Santos; C T Guimarães; R L Gomide; C L T Andrade; P E P Albuquerque; F F Caniato; M Mollinari; G R A Margarido; B F Oliveira; R E Schaffert; A A F Garcia; F A van Eeuwijk; J V Magalhaes
Journal:  Theor Appl Genet       Date:  2012-05       Impact factor: 5.699

4.  Using probe genotypes to dissect QTL × environment interactions for grain yield components in winter wheat.

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Journal:  Theor Appl Genet       Date:  2010-08-10       Impact factor: 5.699

5.  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

6.  A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data.

Authors:  H P Piepho
Journal:  Genetics       Date:  2000-12       Impact factor: 4.562

7.  Heritability and identification of QTLs and underlying candidate genes associated with the architecture of the grapevine cluster (Vitis vinifera L.).

Authors:  J Correa; M Mamani; C Muñoz-Espinoza; D Laborie; C Muñoz; M Pinto; P Hinrichsen
Journal:  Theor Appl Genet       Date:  2014-02-21       Impact factor: 5.699

8.  Quantitative trait locus-by-environment interaction for milk yield traits on Bos taurus autosome 6.

Authors:  Marie Lillehammer; Mike E Goddard; Heidi Nilsen; Erling Sehested; Hanne Gro Olsen; Sigbjørn Lien; Theo H E Meuwissen
Journal:  Genetics       Date:  2008-06-18       Impact factor: 4.562

9.  No genetic diversity at molecular markers and strong phenotypic plasticity in populations of Ranunculus nodiflorus, an endangered plant species in France.

Authors:  Florence Noel; Nathalie Machon; Emmanuelle Porcher
Journal:  Ann Bot       Date:  2007-04-27       Impact factor: 4.357

10.  Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML versus a Bayesian model in multi-environmental field trials.

Authors:  Andrea Michaela Bauer; F Hoti; M von Korff; K Pillen; J Léon; M J Sillanpää
Journal:  Theor Appl Genet       Date:  2009-04-11       Impact factor: 5.699

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