Abstract

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F-6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype-environment (GxE) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

Keywords

wheat breeding; baking quality; Alveograph; flour yield; genomic selection; GWAS

Published in

Genes
2019, Volume: 10, number: 9, article number: 669
Publisher: MDPI

SLU Authors

• Jahoor, Ahmed

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    
  • Nordic Seed A/S

UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.3390/genes10090669

Permanent link to this page (URI)

https://res.slu.se/id/publ/102158