Genome-wide association studies and comparison of models and cross-validation strategies for genomic prediction of quality traits in advanced winter wheat breeding lines

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Dokumenter

DOI

  • Peter S. Kristensen, Nordic Seed A/S
  • ,
  • Ahmed Jahoor, Nordic Seed A/S, Sveriges Lantbruksuniversitet
  • ,
  • Jeppe R. Andersen, Nordic Seed A/S
  • ,
  • Fabio Cericola
  • ,
  • Jihad Orabi, Nordic Seed A/S
  • ,
  • Luc L. Janss
  • Just Jensen

The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number), and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.

OriginalsprogEngelsk
Artikelnummer69
TidsskriftFrontiers in Plant Science
Vol/bind9
Antal sider15
ISSN1664-462X
DOI
StatusUdgivet - 2 feb. 2018

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