A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

M.M. Pastina, M. Malosetti, R. Gazaffi, M. Mollinari, G.R.A. Margarido, K.M. Oliveira, L.R. Pinto, A.P. Souza, F.A. van Eeuwijk, A.A.F. Garcia

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51 Citations (Scopus)


Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane
Original languageEnglish
Pages (from-to)835-849
JournalTheoretical and Applied Genetics
Issue number5
Publication statusPublished - 2012


  • quantitative trait loci
  • spp. commercial cross
  • pseudo-testcross strategy
  • cultivar saccharum spp.
  • genetic-linkage map
  • environment data
  • rust resistance
  • molecular markers
  • flanking markers
  • genome structure

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