Understanding the genetic basis of potato development using a multi-trait QTL analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-07

AUTHORS

P. X. Hurtado-Lopez, B. B. Tessema, S. K. Schnabel, C. Maliepaard, C. G. Van der Linden, P. H. C. Eilers, J. Jansen, F. A. van Eeuwijk, R. G. F. Visser

ABSTRACT

Understanding the genetic basis of plant development in potato requires a proper characterization of plant morphology over time. Parameters related to different aging stages can be used to describe the developmental processes. It is attractive to map these traits simultaneously in a QTL analysis; because the power to detect a QTL will often be improved and it will be easier to identify pleiotropic QTLs. We included complex, agronomic traits together with plant development parameters in a multi-trait QTL analysis. First, the results of our analysis led to coherent insight into the genetic architecture of complex traits in potato. Secondly, QTL for parameters related to plant development were identified. Thirdly, pleiotropic regions for various types of traits were identified. Emergence, number of main stems, number of tubers and yield were explained by 9, 5, 4 and 6 QTL, respectively. These traits were measured once during the growing season. The genetic control of flowering, senescence and plant height, which were measured at regular time intervals, was explained by 9, 10 and 12 QTL, respectively. Genetic relationships between aboveground and belowground traits in potato were observed in 14 pleiotropic QTL. Some of our results suggest the presence of QTL-by-Environment interactions. Therefore, additional studies comparing development under different photoperiods are required to investigate the plasticity of the crop. More... »

PAGES

229-241

References to SciGraph publications

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    URI

    http://scigraph.springernature.com/pub.10.1007/s10681-015-1431-2

    DOI

    http://dx.doi.org/10.1007/s10681-015-1431-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1050400905


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