Literature DB >> 18344528

Defining the assumptions underlying modeling of epistatic QTL using variance component methods.

Lars Rönnegård1, Ricardo Pong-Wong, Orjan Carlborg.   

Abstract

Variance component models are commonly used to detect quantitative trait loci (QTL) in general pedigrees. The variance-covariance structure of the random QTL effect is given by the identity by descent (IBD) between genotypes. Epistatic effects have previously been modeled, both for unlinked and linked loci, as a random effect with a variance-covariance structure given by the Hadamard product between the IBD matrices of the direct QTL effects. In the original papers, the model was given but not derived. Here, we identify the underlying assumptions of this previously proposed model. It assumes that either an unlinked QTL or a fully informative marker (i.e., all marker alleles are unique in the base generation) is located between the loci. We discuss the need of developing a general algorithm to estimate the variance-covariance structure of the random epistatic effect for linked loci.

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Year:  2008        PMID: 18344528     DOI: 10.1093/jhered/esn017

Source DB:  PubMed          Journal:  J Hered        ISSN: 0022-1503            Impact factor:   2.645


  6 in total

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2.  Complex genetic effects in quantitative trait locus identification: a computationally tractable random model for use in F(2) populations.

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Journal:  Genetics       Date:  2008-04       Impact factor: 4.562

4.  Concepts, estimation and interpretation of SNP-based heritability.

Authors:  Jian Yang; Jian Zeng; Michael E Goddard; Naomi R Wray; Peter M Visscher
Journal:  Nat Genet       Date:  2017-08-30       Impact factor: 38.330

5.  Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.

Authors:  Lorin Crawford; Ping Zeng; Sayan Mukherjee; Xiang Zhou
Journal:  PLoS Genet       Date:  2017-07-26       Impact factor: 5.917

6.  Genetic interactions contribute less than additive effects to quantitative trait variation in yeast.

Authors:  Joshua S Bloom; Iulia Kotenko; Meru J Sadhu; Sebastian Treusch; Frank W Albert; Leonid Kruglyak
Journal:  Nat Commun       Date:  2015-11-05       Impact factor: 14.919

  6 in total

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