Molecular diversity of sunflower populations maintained as genetic resources is affected by multiplication processes and breeding for major traits

  • Brigitte Mangin
  • Nicolas Pouilly
  • Marie-Claude Boniface
  • Nicolas B. Langlade
  • Patrick Vincourt
  • Felicity Vear
  • Stéphane Muños
Original Article

DOI: 10.1007/s00122-017-2872-x

Cite this article as:
Mangin, B., Pouilly, N., Boniface, MC. et al. Theor Appl Genet (2017). doi:10.1007/s00122-017-2872-x

Abstract

Key message

SNP genotyping of 114 cultivated sunflower populations showed that the multiplication process and the main traits selected during breeding of sunflower cultivars drove molecular diversity of the populations.

Abstract

The molecular diversity in a set of 114 cultivated sunflower populations was studied by single-nucleotide polymorphism genotyping. These populations were chosen as representative of the 400 entries in the INRA collection received or developed between 1962 and 2011 and made up of land races, open-pollinated varieties, and breeding pools. Mean allele number varied from 1.07 to 1.90. Intra-population variability was slightly reduced according to the number of multiplications since entry but some entries were probably largely homozygous when received. A principal component analysis was used to study inter-population variability. The first 3 axes accounted for 17% of total intra-population variability. The first axis was significantly correlated with seed oil content, more closely than just the distinction between oil and confectionary types. The second axis was related to the presence or absence of restorer genes and the third axis to flowering date and possibly to adaptation to different climates. Our results provide arguments highlighting the effect of the maintenance process on the within population genetic variability as well as on the impact of breeding for major agronomic traits on the between population variability of the collection. Propositions are made to improve sunflower population maintenance procedures to keep maximum genetic variability for future breeding.

Supplementary material

122_2017_2872_MOESM1_ESM.pdf (106 kb)
Supplementary material 1 (PDF 106 KB)
122_2017_2872_MOESM2_ESM.xlsx (61 kb)
Supplementary material 2 (XLSX 61 KB)

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Brigitte Mangin
    • 1
  • Nicolas Pouilly
    • 1
  • Marie-Claude Boniface
    • 1
  • Nicolas B. Langlade
    • 1
  • Patrick Vincourt
    • 1
  • Felicity Vear
    • 2
  • Stéphane Muños
    • 1
  1. 1.LIPM, Université de Toulouse, INRA, CNRSCastanet-TolosanFrance
  2. 2.GDEC, INRA, Université Clermont II Blaise PascalClermont-FerrandFrance