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A revised Fisher model on analysis of quantitative trait loci with multiple alleles

Zeng et al. (2005) proposed a general two-allele (G2A) model to model bi-allelic quantitative trait loci (QTL). Comparing with the classical Fisher model, the G2A model can avoid using redundant parameters and be fitted directly using standard least square (LS) approach. In this study, we further ex...

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Detalles Bibliográficos
Autor principal: Wang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174749/
https://www.ncbi.nlm.nih.gov/pubmed/25309580
http://dx.doi.org/10.3389/fgene.2014.00328
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author Wang, Tao
author_facet Wang, Tao
author_sort Wang, Tao
collection PubMed
description Zeng et al. (2005) proposed a general two-allele (G2A) model to model bi-allelic quantitative trait loci (QTL). Comparing with the classical Fisher model, the G2A model can avoid using redundant parameters and be fitted directly using standard least square (LS) approach. In this study, we further extend the G2A model to general multi-allele (GMA) model. First, we propose a one-locus GMA model for phase known genotypes based on modeling the inheritance of paternal and maternal alleles. Next, we develop a one-locus GMA model for phase unknown genotypes by treating it as a special case of the phase known one-locus GMA model. Thirdly, we extend the one-locus GMA models to multiple loci. We discuss how the genetic variance components can be analyzed using these GMA models in equilibrium as well as disequilibrium populations. Finally, we apply the GMA model to a published experimental data set.
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spelling pubmed-41747492014-10-10 A revised Fisher model on analysis of quantitative trait loci with multiple alleles Wang, Tao Front Genet Genetics Zeng et al. (2005) proposed a general two-allele (G2A) model to model bi-allelic quantitative trait loci (QTL). Comparing with the classical Fisher model, the G2A model can avoid using redundant parameters and be fitted directly using standard least square (LS) approach. In this study, we further extend the G2A model to general multi-allele (GMA) model. First, we propose a one-locus GMA model for phase known genotypes based on modeling the inheritance of paternal and maternal alleles. Next, we develop a one-locus GMA model for phase unknown genotypes by treating it as a special case of the phase known one-locus GMA model. Thirdly, we extend the one-locus GMA models to multiple loci. We discuss how the genetic variance components can be analyzed using these GMA models in equilibrium as well as disequilibrium populations. Finally, we apply the GMA model to a published experimental data set. Frontiers Media S.A. 2014-09-25 /pmc/articles/PMC4174749/ /pubmed/25309580 http://dx.doi.org/10.3389/fgene.2014.00328 Text en Copyright © 2014 Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wang, Tao
A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title_full A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title_fullStr A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title_full_unstemmed A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title_short A revised Fisher model on analysis of quantitative trait loci with multiple alleles
title_sort revised fisher model on analysis of quantitative trait loci with multiple alleles
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4174749/
https://www.ncbi.nlm.nih.gov/pubmed/25309580
http://dx.doi.org/10.3389/fgene.2014.00328
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