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Comparison of three multitrait methods for QTL detection

A comparison of power and accuracy of estimation of position and QTL effects of three multitrait methods and one single trait method for QTL detection was carried out on simulated data, taking into account the mixture of full and half-sib families. One multitrait method was based on a multivariate f...

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Detalles Bibliográficos
Autores principales: Gilbert, Hélène, Le Roy, Pascale
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732700/
https://www.ncbi.nlm.nih.gov/pubmed/12729550
http://dx.doi.org/10.1186/1297-9686-35-3-281
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author Gilbert, Hélène
Le Roy, Pascale
author_facet Gilbert, Hélène
Le Roy, Pascale
author_sort Gilbert, Hélène
collection PubMed
description A comparison of power and accuracy of estimation of position and QTL effects of three multitrait methods and one single trait method for QTL detection was carried out on simulated data, taking into account the mixture of full and half-sib families. One multitrait method was based on a multivariate function as the penetrance function (MV). The two other multitrait methods were based on univariate analysis of linear combination(s) (LC) of the traits. One was obtained by a principal component analysis (PCA) performed on the phenotypic data. The second was based on a discriminate analysis (DA). It calculates a LC of the traits at each position, maximising the ratio between the genetic and the residual variabilities due to the putative QTL. Due to its number of parameters, MV was less powerful and accurate than the other methods. In general, DA better detected QTL, but it had lower accuracy for the QTL effect estimation when the detection power was low, due to higher bias than the other methods. In this case, PCA was better. Otherwise, PCA was slightly less powerful and accurate than DA. Compared to the single trait method, power can be improved by 30% to 100% with multitrait methods.
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spelling pubmed-27327002009-08-27 Comparison of three multitrait methods for QTL detection Gilbert, Hélène Le Roy, Pascale Genet Sel Evol Research A comparison of power and accuracy of estimation of position and QTL effects of three multitrait methods and one single trait method for QTL detection was carried out on simulated data, taking into account the mixture of full and half-sib families. One multitrait method was based on a multivariate function as the penetrance function (MV). The two other multitrait methods were based on univariate analysis of linear combination(s) (LC) of the traits. One was obtained by a principal component analysis (PCA) performed on the phenotypic data. The second was based on a discriminate analysis (DA). It calculates a LC of the traits at each position, maximising the ratio between the genetic and the residual variabilities due to the putative QTL. Due to its number of parameters, MV was less powerful and accurate than the other methods. In general, DA better detected QTL, but it had lower accuracy for the QTL effect estimation when the detection power was low, due to higher bias than the other methods. In this case, PCA was better. Otherwise, PCA was slightly less powerful and accurate than DA. Compared to the single trait method, power can be improved by 30% to 100% with multitrait methods. BioMed Central 2003-05-15 /pmc/articles/PMC2732700/ /pubmed/12729550 http://dx.doi.org/10.1186/1297-9686-35-3-281 Text en Copyright © 2003 INRA, EDP Sciences
spellingShingle Research
Gilbert, Hélène
Le Roy, Pascale
Comparison of three multitrait methods for QTL detection
title Comparison of three multitrait methods for QTL detection
title_full Comparison of three multitrait methods for QTL detection
title_fullStr Comparison of three multitrait methods for QTL detection
title_full_unstemmed Comparison of three multitrait methods for QTL detection
title_short Comparison of three multitrait methods for QTL detection
title_sort comparison of three multitrait methods for qtl detection
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732700/
https://www.ncbi.nlm.nih.gov/pubmed/12729550
http://dx.doi.org/10.1186/1297-9686-35-3-281
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