Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

Theor Appl Genet. 2018 Feb;131(2):319-332. doi: 10.1007/s00122-017-3003-4. Epub 2017 Nov 2.


This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

Keywords: Genome-wide association study; Multi-locus; Non-additive effect; Sunflower.

Publication types

  • Comparative Study

MeSH terms

  • Flowers / physiology*
  • Genetic Association Studies*
  • Genotype
  • Helianthus / genetics*
  • Helianthus / physiology
  • Hybrid Vigor
  • Linkage Disequilibrium
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci