Literature DB >> 24202767

A mixed model for analyses of data on multiple genetic markers.

M E Goddard1.   

Abstract

Data on a genetic marker linked to a gene affecting an important trait could help us to estimate breeding values for that trait more accurately. The accuracy is enhanced if many genetic markers are used and if important genes are bracketed by two markers. A mixed model for analysis of this type of data is presented. The model is appropriate for an arbitrary pedigree structure in an outbreeding species. It uses a "relationship" matrix among marked chromosome segments or QTL alleles. By using an analysis analogous to a reduced animal model, the number of effects to be estimated can be greatly reduced. A grouping strategy that can account for crossbreeding and linkage disequilibrium between markers and QTL alleles is included in the model. For analyses of a cross between inbred lines the model can be simplified. This simplification shows clearly the relationship of the mixed model analyses to multiple regression models used previously. The simplified model may also be useful for some experiments in outbreeding populations.

Year:  1992        PMID: 24202767     DOI: 10.1007/BF00226711

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


  3 in total

1.  Genetic polymorphism in varietal identification and genetic improvement.

Authors:  M Soller; J S Beckmann
Journal:  Theor Appl Genet       Date:  1983-11       Impact factor: 5.699

2.  Efficiency of marker-assisted selection in the improvement of quantitative traits.

Authors:  R Lande; R Thompson
Journal:  Genetics       Date:  1990-03       Impact factor: 4.562

3.  Detection of linkage between marker loci and loci affecting quantitative traits in crosses between segregating populations.

Authors:  J S Beckmann; M Soller
Journal:  Theor Appl Genet       Date:  1988-08       Impact factor: 5.699

  3 in total
  17 in total

1.  Accounting for relatedness in family based genetic association studies.

Authors:  P F McArdle; J R O'Connell; T I Pollin; M Baumgarten; A R Shuldiner; P A Peyser; B D Mitchell
Journal:  Hum Hered       Date:  2007-06-14       Impact factor: 0.444

2.  A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. I. Methodology.

Authors:  G Thaller; I Hoeschele
Journal:  Theor Appl Genet       Date:  1996-11       Impact factor: 5.699

3.  Exclusion probabilities for pedigree testing farm animals.

Authors:  K G Dodds; M L Tate; J C McEwan; A M Crawford
Journal:  Theor Appl Genet       Date:  1996-06       Impact factor: 5.699

4.  Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge.

Authors:  I Hoeschele; P M Vanraden
Journal:  Theor Appl Genet       Date:  1993-02       Impact factor: 5.699

5.  Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence.

Authors:  I Hoeschele; P M Vanraden
Journal:  Theor Appl Genet       Date:  1993-02       Impact factor: 5.699

6.  Derivation of single-locus relationship coefficients conditional on marker information.

Authors:  H Simianer
Journal:  Theor Appl Genet       Date:  1994-07       Impact factor: 5.699

7.  Computer simulation of marker-assisted selection utilizing linkage disequilibrium.

Authors:  W Zhang; C Smith
Journal:  Theor Appl Genet       Date:  1992-04       Impact factor: 5.699

8.  An improved method for quantitative trait loci detection and identification of within-line segregation in F2 intercross designs.

Authors:  Lars Rönnegård; Francois Besnier; Orjan Carlborg
Journal:  Genetics       Date:  2008-04       Impact factor: 4.562

9.  A gene frequency model for QTL mapping using Bayesian inference.

Authors:  Wei He; Rohan L Fernando; Jack Cm Dekkers; Helene Gilbert
Journal:  Genet Sel Evol       Date:  2010-06-11       Impact factor: 4.297

10.  Linear models for joint association and linkage QTL mapping.

Authors:  Andrés Legarra; Rohan L Fernando
Journal:  Genet Sel Evol       Date:  2009-09-29       Impact factor: 4.297

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