QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach

The Female Index (FI) is a relative measure of host suitability of a soybean line for a particular nematode population and often shows a non-normal distribution. Moreover, most quantitative trait loci (QTL) mapping methods assume that the phenotype follows a normal distribution such as composite int...

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Autores: Arriagada, Osvin, Ferreira, Marcia F. S., Cervigni, Gerardo Domingo Lucio, Schuster, Ivan, Scapim, Carlos A., Mora, Freddy
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/7843
Acceso en línea:http://hdl.handle.net/11336/7843
Access Level:acceso abierto
Palabra clave:Female index
Generalized linear model
Glycine max
Heterodera glycines
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
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network_acronym_str AR
network_name_str Argentina
repository_id_str
spelling QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approachArriagada, OsvinFerreira, Marcia F. S.Cervigni, Gerardo Domingo LucioSchuster, IvanScapim, Carlos A.Mora, FreddyFemale indexGeneralized linear modelGlycine maxHeterodera glycineshttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4The Female Index (FI) is a relative measure of host suitability of a soybean line for a particular nematode population and often shows a non-normal distribution. Moreover, most quantitative trait loci (QTL) mapping methods assume that the phenotype follows a normal distribution such as composite interval mapping (CIM). Therefore, a generalized linear modeling (GLM) approach was employed to map QTL for resistance to race 9 of the soybean cyst nematode (SCN) using a total of 83 simple sequence repeat markers (SSR). Two GLM models were tested: model 1, where the FI was treated as a continuous variable, assuming a Gamma distribution with a logarithmic link function; and model 2, where the FI was treated as a categorical trait in a five-item hierarchy, assuming a multinomial distribution with a cumulative logit link function. The FI data of 108 recombinant inbred lines (RIL) confirmed the non-normal distribution for race 9 of the SCN (Shapiro-Wilk?s w=0.86, P<0.0001, skewness=1.52 and kurtosis=2.93). Eight RIL were confirmed to be resistant (FI≤10), and 23 to be highly susceptible (FI≥100). Both GLM models identified one QTL for SCN on the molecular linkage group G, between the markers Satt275 and Satt038 at 48.4 centiMorgans (P=0.017 and 0.033, for models 1 and 2, respectively). Additionally, these results were also compared with the CIM and Bayesian interval mapping (BIM) methods, assuming experimental data with a non-normal response, to determine the robustness and statistical power of these two methods for mapping QTLs. The results make clear that generalized linear modeling approach can be used as an efficient method to map QTLs in a continuous trait with a non-Gaussian distribution. CIM and BIM were robust enough for a reliable mapping of QTLs underlying nonnormally distributed data.Fil: Arriagada, Osvin. Universidad de Talca; ChileFil: Ferreira, Marcia F. S.. Universidade Federal Do Espirito Santo; BrasilFil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); ArgentinaFil: Schuster, Ivan. Central Cooperative for Agricultural Research; BrasilFil: Scapim, Carlos A.. Universidade Estadual de Maringá; BrasilFil: Mora, Freddy. Universidad de Talca; ChileSouthern Cross Publ2015-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7843Arriagada, Osvin; Ferreira, Marcia F. S.; Cervigni, Gerardo Domingo Lucio; Schuster, Ivan; Scapim, Carlos A.; et al.; QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach; Southern Cross Publ; Australian Journal Of Crop Science; 9; 8; 8-2015; 721-7271835-26931835-2707enginfo:eu-repo/semantics/altIdentifier/url/http://www.cropj.com/arriagada_9_8_2015_721_727.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:57:10Zoai:ri.conicet.gov.ar:11336/7843instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 13:57:10.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
title QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
spellingShingle QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
Arriagada, Osvin
Female index
Generalized linear model
Glycine max
Heterodera glycines
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
title_short QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
title_full QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
title_fullStr QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
title_full_unstemmed QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
title_sort QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
dc.creator.none.fl_str_mv Arriagada, Osvin
Ferreira, Marcia F. S.
Cervigni, Gerardo Domingo Lucio
Schuster, Ivan
Scapim, Carlos A.
Mora, Freddy
author Arriagada, Osvin
author_facet Arriagada, Osvin
Ferreira, Marcia F. S.
Cervigni, Gerardo Domingo Lucio
Schuster, Ivan
Scapim, Carlos A.
Mora, Freddy
author_role author
author2 Ferreira, Marcia F. S.
Cervigni, Gerardo Domingo Lucio
Schuster, Ivan
Scapim, Carlos A.
Mora, Freddy
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Female index
Generalized linear model
Glycine max
Heterodera glycines
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
topic Female index
Generalized linear model
Glycine max
Heterodera glycines
https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
description The Female Index (FI) is a relative measure of host suitability of a soybean line for a particular nematode population and often shows a non-normal distribution. Moreover, most quantitative trait loci (QTL) mapping methods assume that the phenotype follows a normal distribution such as composite interval mapping (CIM). Therefore, a generalized linear modeling (GLM) approach was employed to map QTL for resistance to race 9 of the soybean cyst nematode (SCN) using a total of 83 simple sequence repeat markers (SSR). Two GLM models were tested: model 1, where the FI was treated as a continuous variable, assuming a Gamma distribution with a logarithmic link function; and model 2, where the FI was treated as a categorical trait in a five-item hierarchy, assuming a multinomial distribution with a cumulative logit link function. The FI data of 108 recombinant inbred lines (RIL) confirmed the non-normal distribution for race 9 of the SCN (Shapiro-Wilk?s w=0.86, P<0.0001, skewness=1.52 and kurtosis=2.93). Eight RIL were confirmed to be resistant (FI≤10), and 23 to be highly susceptible (FI≥100). Both GLM models identified one QTL for SCN on the molecular linkage group G, between the markers Satt275 and Satt038 at 48.4 centiMorgans (P=0.017 and 0.033, for models 1 and 2, respectively). Additionally, these results were also compared with the CIM and Bayesian interval mapping (BIM) methods, assuming experimental data with a non-normal response, to determine the robustness and statistical power of these two methods for mapping QTLs. The results make clear that generalized linear modeling approach can be used as an efficient method to map QTLs in a continuous trait with a non-Gaussian distribution. CIM and BIM were robust enough for a reliable mapping of QTLs underlying nonnormally distributed data.
publishDate 2015
dc.date.none.fl_str_mv 2015-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/7843
Arriagada, Osvin; Ferreira, Marcia F. S.; Cervigni, Gerardo Domingo Lucio; Schuster, Ivan; Scapim, Carlos A.; et al.; QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach; Southern Cross Publ; Australian Journal Of Crop Science; 9; 8; 8-2015; 721-727
1835-2693
1835-2707
url http://hdl.handle.net/11336/7843
identifier_str_mv Arriagada, Osvin; Ferreira, Marcia F. S.; Cervigni, Gerardo Domingo Lucio; Schuster, Ivan; Scapim, Carlos A.; et al.; QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach; Southern Cross Publ; Australian Journal Of Crop Science; 9; 8; 8-2015; 721-727
1835-2693
1835-2707
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.cropj.com/arriagada_9_8_2015_721_727.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Southern Cross Publ
publisher.none.fl_str_mv Southern Cross Publ
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 15,811543

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