Aarhus Universitets segl

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review


  • Hsin-Yuan Tsai, National Sun Yat-sen University
  • ,
  • Luc L Janss
  • Jeppe R Andersen, Nordic Seed A/S
  • ,
  • Jihad Orabi, Nordic Seed A/S
  • ,
  • Jens D Jensen, Nordic Seed A/S
  • ,
  • Ahmed Jahoor, Swedish University of Agricultural Sciences, Nordic Seed A/S
  • ,
  • Just Jensen

Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 spring barley and 1,325 winter wheat breeding lines from a commercial breeding program were genotyped with the Illumina 9 K barley or 15 K wheat SNP-chip, and phenotyped in multiple years and locations. For GWAS, in spring barley, a QTL on chr. 4H associated with powdery mildew and ramularia resistance were found. There were several SNPs on chr. 4H showing genome-wide significance with yield traits. In winter wheat, GWAS identified two SNPs on chr. 6A, and one SNP on chr. 1B, significantly associated with quality trait moisture, as well as one SNP located on chr. 5B associated with starch content in the seeds. The significant SNPs identified by multiple trait GWAS were generally the same as those found in single trait GWAS. GWAS including genotype-location information in the model identified significant SNPs in each tested location, which were not found previously when including all locations in the GWAS. For GP, in spring barley, GP using the Bayesian Power Lasso model had higher accuracy than ridge regression BLUP in powdery mildew and yield traits, whereas the prediction accuracies were similar using Bayesian Power Lasso model and rrBLUP for yield traits in winter wheat.

TidsskriftScientific Reports
Antal sider15
StatusUdgivet - 2020

Se relationer på Aarhus Universitet Citationsformater

ID: 180397363