Abstract
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.
Highlights
Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding
The aims of this study were: (1) to identify genetic loci associated with grain yield traits, quality traits, and disease-resistance traits by using single trait and multiple trait GWAS; (2) to include the genotype-by-location information in the GWAS model to evaluate the marker effects toward grain yield performance in winter wheat tested in three different experimental locations and (3) to develop models for genomic prediction of traits of interest using regression BLUP model (rrBLUP) and Bayesian Power Lasso model
The GWAS analysis successfully identified several markers significantly associated with grain yield, quality traits and disease-resistance traits in spring barley and winter wheat
Summary
Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. 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 Both wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) are major cereal crops worldwide, ranking as the second and fourth in total grain production. Several studies evidenced that most traits of commercial interest in wheat and barley are highly polygenic, with many QTLs that each only account for a small proportion of total genetic variances[10,11] This severely limits the application of traditional molecular-assisted selection in cereal crops. Genes or QTLs with large effects can be mapped using linkage mapping or genome-wide association studies (GWAS), Species Barley
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