Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits

In genetic studies, most Quantitative Trait Loci (QTL) mapping methods presuppose that the continuous trait of interest follows a normal (Gaussian) distribution. However, many economically important traits of agricultural crops have a non-normal distribution. Composite interval mapping (CIM) has bee...

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Autores principales:	Mora,Freddy, Scapim,Carlos Alberto, Baharum,Adam, Teixeira do Amaral Júnior,Antonio
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2010
Materias:	bioinformatics Generalized Linear Model molecular markers Quantitative Trait Loci (QTL)
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202010000300007
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spelling	oai:scielo:S0718-162020100003000072011-02-03Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traitsMora,FreddyScapim,Carlos AlbertoBaharum,AdamTeixeira do Amaral Júnior,Antonio bioinformatics Generalized Linear Model molecular markers Quantitative Trait Loci (QTL) In genetic studies, most Quantitative Trait Loci (QTL) mapping methods presuppose that the continuous trait of interest follows a normal (Gaussian) distribution. However, many economically important traits of agricultural crops have a non-normal distribution. Composite interval mapping (CIM) has been successfully applied to the detection of QTL in animal and plant breeding. In this study we report a generalized CIM (GCIM) method that permits QTL analysis of non-normally distributed variables. GCIM was based on the classic Generalized Linear Model method. We applied the GCIM method to a F2 population with co-dominant molecular markers and the existence of a QTL controlling a trait with Gamma distribution. Computer simulations indicated that the GCIM method has superior performance in its ability to map QTL, compared with CIM. QTL position differed by 5 cM and was located at different marker intervals. The Likelihood Ratio Test values ranged from 52 (GCIM) to 76 (CIM). Thus, wrongly assuming CIM may overestimate the effect of the QTL by about 47%. The usage of GCIM methodology can offer improved efficiency in the analysis of QTLs controlling continuous traits of non-Gaussian distribution.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.37 n.3 20102010-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202010000300007en10.4067/S0718-16202010000300007
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	bioinformatics Generalized Linear Model molecular markers Quantitative Trait Loci (QTL)
spellingShingle	bioinformatics Generalized Linear Model molecular markers Quantitative Trait Loci (QTL) Mora,Freddy Scapim,Carlos Alberto Baharum,Adam Teixeira do Amaral Júnior,Antonio Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
description	In genetic studies, most Quantitative Trait Loci (QTL) mapping methods presuppose that the continuous trait of interest follows a normal (Gaussian) distribution. However, many economically important traits of agricultural crops have a non-normal distribution. Composite interval mapping (CIM) has been successfully applied to the detection of QTL in animal and plant breeding. In this study we report a generalized CIM (GCIM) method that permits QTL analysis of non-normally distributed variables. GCIM was based on the classic Generalized Linear Model method. We applied the GCIM method to a F2 population with co-dominant molecular markers and the existence of a QTL controlling a trait with Gamma distribution. Computer simulations indicated that the GCIM method has superior performance in its ability to map QTL, compared with CIM. QTL position differed by 5 cM and was located at different marker intervals. The Likelihood Ratio Test values ranged from 52 (GCIM) to 76 (CIM). Thus, wrongly assuming CIM may overestimate the effect of the QTL by about 47%. The usage of GCIM methodology can offer improved efficiency in the analysis of QTLs controlling continuous traits of non-Gaussian distribution.
author	Mora,Freddy Scapim,Carlos Alberto Baharum,Adam Teixeira do Amaral Júnior,Antonio
author_facet	Mora,Freddy Scapim,Carlos Alberto Baharum,Adam Teixeira do Amaral Júnior,Antonio
author_sort	Mora,Freddy
title	Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
title_short	Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
title_full	Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
title_fullStr	Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
title_full_unstemmed	Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
title_sort	generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2010
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202010000300007
work_keys_str_mv	AT morafreddy generalizedcompositeintervalmappingoffersimprovedefficiencyintheanalysisoflociinfluencingnonnormalcontinuoustraits AT scapimcarlosalberto generalizedcompositeintervalmappingoffersimprovedefficiencyintheanalysisoflociinfluencingnonnormalcontinuoustraits AT baharumadam generalizedcompositeintervalmappingoffersimprovedefficiencyintheanalysisoflociinfluencingnonnormalcontinuoustraits AT teixeiradoamaraljuniorantonio generalizedcompositeintervalmappingoffersimprovedefficiencyintheanalysisoflociinfluencingnonnormalcontinuoustraits
_version_	1714202127038939136

Generalized composite interval mapping offers improved efficiency in the analysis of loci influencing non-normal continuous traits

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