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

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 ba...

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Veröffentlicht in:Scientific reports 2020-02, Vol.10 (1), p.3347-3347, Article 3347
Hauptverfasser: Tsai, Hsin-Yuan, Janss, Luc L., Andersen, Jeppe R., Orabi, Jihad, Jensen, Jens D., Jahoor, Ahmed, Jensen, Just
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container_title Scientific reports
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creator Tsai, Hsin-Yuan
Janss, Luc L.
Andersen, Jeppe R.
Orabi, Jihad
Jensen, Jens D.
Jahoor, Ahmed
Jensen, Just
description 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.
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subjects 631/449
631/449/711
Agricultural Science
Ascomycota - genetics
Ascomycota - pathogenicity
Bayes Theorem
Bayesian analysis
Breeding
Disease Resistance - genetics
Genome, Plant - genetics
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Genotype
Genotypes
Hordeum - genetics
Hordeum - growth & development
Hordeum - microbiology
Humanities and Social Sciences
Jordbruksvetenskap
multidisciplinary
Phenotype
Plant breeding
Plant Diseases - genetics
Plant Diseases - microbiology
Polymorphism, Single Nucleotide - genetics
Powdery mildew
Quantitative trait loci
Quantitative Trait Loci - genetics
Science
Science (multidisciplinary)
Seasons
Seeds
Single-nucleotide polymorphism
Starch
Triticum - genetics
Triticum - growth & development
Triticum - microbiology
Triticum aestivum
Wheat
title Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat
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