Functional mapping of quantitative trait loci (QTLs) associated with plant performance in a wheat MAGIC mapping population

Anyela V. Camargo-Rodriguez, Ian J. Mackay, Richard Mott, Jiwan Han, John Doonan, Karen Askew, Fiona Corke, Kevin Williams, Alison Bentley

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Abstract

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can potentially elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding
Original languageEnglish
Article number887
JournalFrontiers in Plant Science
Volume9
DOIs
Publication statusPublished - 09 Jul 2018

Keywords

  • wheat
  • senescence
  • data science
  • Phenology
  • phenotyping
  • Magic

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