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dc.contributor.authorMalosseti, Marcos
dc.contributor.authorEeuwijk, Fred A. van
dc.contributor.authorBoer, Martin P.
dc.contributor.authorCasas Cendoya, Ana Maria
dc.contributor.authorElía Martínez, Mónica
dc.contributor.authorMoralejo Vidal, Mª Angeles
dc.contributor.authorBhat, Prasanna R.
dc.contributor.authorRamsay, Luke D.
dc.contributor.authorMolina Cano, José Luis
dc.description.abstractQuantitative trait locus (QTL) detection is commonly performed by analysis of designed segregating populations derived from two inbred parental lines, where absence of selection, mutation and genetic drift is assumed. Even for designed populations, selection cannot always be avoided, with as consequence varying correlation between genotypes instead of uniform correlation. Akin to linkage disequilibrium mapping, ignoring this type of genetic relatedness will increase the rate of false-positives. In this paper, we advocate using mixed models including genetic relatedness, or ‘kinship’ information for QTL detection in populations where selection forces operated. We demonstrate our case with a three-way barley cross, designed to segregate for dwarfing, vernalization and spike morphology genes, in which selection occurred. The population of 161 inbred lines was screened with 1,536 single nucleotide polymorphisms (SNPs), and used for gene and QTL detection. The coefficient of coancestry matrix was estimated based on the SNPs and imposed to structure the distribution of random genotypic effects. The model incorporating kinship, coancestry, information was consistently superior to the one without kinship (according to the Akaike information criterion). We show, for three traits, that ignoring the coancestry information results in an unrealistically high number of marker–trait associations, without providing clear conclusions about QTL locations. We used a number of widely recognized dwarfing and vernalization genes known to segregate in the studied population as landmarks or references to assess the agreement of the mapping results with a priori candidate gene expectations. Additional QTLs to the major genes were detected for all traits as well.ca_ES
dc.description.sponsorshipWe want to thank INIA (MICINN) for partially funding this work through different grants. The Centre UdL-IRTA forms part of the Centre CONSOLIDER on Agrigenomics funded by the Spanish Ministry of Education and Science and acknowledges partial funding from grant AGL2005-07195-C02-02. Genotyping of the RIL population with BOPA1 was funded by the Spanish Ministry of Science and Innovation, project GEN2006-28560-E. The work of M.M. and FvE was partially financed by the Generation Challenge Programme, through the Integrated Breeding Platform projects ‘2.2.1. Develop and deploy statistical and genetic analysis methodology for molecular breeding’ and ‘3.2.4. Design and Analysis’. The research of FvE was partly financed by project BB9 of the Centre for Bio-Systems Genomics (CBSG), which is part of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research. This work was carried out within the research programme of the Netherlands Consortium for Systems Biology (NCSB), which is part of the Netherlands Genomics Initiative / Netherlands Organization for Scientific Research.ca_ES
dc.publisherSpringer Verlagca_ES
dc.relation.isformatofReproducció del document publicat a
dc.relation.ispartofTheoretical and Applied Genetics, 2011, vol. 122, núm. 8, p. 1605–1616ca_ES
dc.rightscc-by-nc (c) Marcos Malosetti et al., 2011ca_ES
dc.subjectQuantitative Trait Locusca_ES
dc.subjectRecombinant Inbred Lineca_ES
dc.subjectQuantitative Trait Locus Analysisca_ES
dc.subjectSegregation Distortionca_ES
dc.titleGene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPsca_ES

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cc-by-nc (c) Marcos Malosetti et al., 2011
Except where otherwise noted, this item's license is described as cc-by-nc (c) Marcos Malosetti et al., 2011