Literature DB >> 12872915

Estimating allelic number and identity in state of QTLs in interconnected families.

Jean-Luc Jannink1, Xiao-Lin Wu.   

Abstract

When multiple related families derived from inbred lines are jointly analysed to detect quantitative trait loci (QTLs), the analysis should estimate allelic effects as accurately as possible and estimate the probability that different parents carry alleles that are identical in state. Analyses exist that assume that all parents carry unique alleles or that all parents but one carry the same allele. In practice, many configurations are possible that group different parents according to their identity-in-state condition at a putative QTL allele. Here, we propose a variable model Bayesian analysis that selects among possible identity-in-state configurations and jointly estimates the allelic effects of identical-in-state parents. We contrast this analysis with a fixed model analysis that estimates unique allelic effects for all parents. We analyse two simulated mating designs: an experimental design in which three inbred parents were crossed to generate two families of 150 doubled haploid lines; and a breeding design in which 20 inbred parents were crossed to generate 60 families of 20 doubled haploid lines, with each parent contributing to six families. In all cases where some parents were simulated to carry alleles of identical effect (that is, they were identical in state), the variable analysis estimated allelic effects with lower mean-squared error than the fixed analysis. The variable analysis showed that, unless each family contains many individuals (more than 100), there is insufficient information in DNA-marker and phenotypic data to determine with high probability the QTL allelic number.

Mesh:

Year:  2003        PMID: 12872915     DOI: 10.1017/s0016672303006153

Source DB:  PubMed          Journal:  Genet Res        ISSN: 0016-6723            Impact factor:   1.588


  21 in total

1.  Optimal sampling of a population to determine QTL location, variance, and allelic number.

Authors:  Xiao-Lin Wu; Jean-Luc Jannink
Journal:  Theor Appl Genet       Date:  2004-01-23       Impact factor: 5.699

2.  QTL detection power of multi-parental RIL populations in Arabidopsis thaliana.

Authors:  J R Klasen; H-P Piepho; B Stich
Journal:  Heredity (Edinb)       Date:  2012-02-15       Impact factor: 3.821

3.  A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait.

Authors:  Daniel Gianola; Xiao-Lin Wu; Eduardo Manfredi; Henner Simianer
Journal:  Genetica       Date:  2010-08-25       Impact factor: 1.082

4.  Nested association mapping for identification of functional markers.

Authors:  Baohong Guo; David A Sleper; William D Beavis
Journal:  Genetics       Date:  2010-06-15       Impact factor: 4.562

5.  Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize.

Authors:  G Blanc; A Charcosset; B Mangin; A Gallais; L Moreau
Journal:  Theor Appl Genet       Date:  2006-05-20       Impact factor: 5.699

6.  Overview of QTL detection in plants and tests for synergistic epistatic interactions.

Authors:  Jean-Luc Jannink; Laurence Moreau; Gilles Charmet; Alain Charcosset
Journal:  Genetica       Date:  2008-08-10       Impact factor: 1.082

7.  Bayesian model averaging for evaluation of candidate gene effects.

Authors:  Xiao-Lin Wu; Daniel Gianola; Guilherme J M Rosa; Kent A Weigel
Journal:  Genetica       Date:  2010-01-05       Impact factor: 1.082

8.  In silico genotyping of the maize nested association mapping population.

Authors:  Baohong Guo; William D Beavis
Journal:  Mol Breed       Date:  2010-09-26       Impact factor: 2.589

Review 9.  Quantitative trait loci from identification to exploitation for crop improvement.

Authors:  Jitendra Kumar; Debjyoti Sen Gupta; Sunanda Gupta; Sonali Dubey; Priyanka Gupta; Shiv Kumar
Journal:  Plant Cell Rep       Date:  2017-03-28       Impact factor: 4.570

10.  Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data.

Authors:  Kiranmoy Das; Jiahan Li; Guifang Fu; Zhong Wang; Runze Li; Rongling Wu
Journal:  Stat Med       Date:  2012-08-17       Impact factor: 2.373

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