Genome-wide association study (GWAS) identifies genetic loci controlling Distinctness, Uniformity, and Stability (DUS) traits in wheat

全基因组关联分析(GWAS)鉴定出控制小麦独特性、均匀性和稳定性(DUS)性状的遗传位点。

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Abstract

Genetic analysis of wheat Distinctness, Uniformity, and Stability (DUS) characteristics identifies significant marker-trait associations for 15 DUS traits, including associations for 'seed: colouration with phenol' closely linked to Ppo-A1. Crop improvement via breeding underpins the yield gains required for future food security. Commercial development of new varieties is supported by the legal protection afforded by Plant Variety Rights, which for wheat is awarded via evaluation of 27 morphological 'characteristics' as part of Distinctness, Uniformity, and Stability (DUS) testing. While identification of molecular markers predictive for wheat DUS characteristics would be useful to aid processes such as variety identification and DUS test optimization, little is known about their genetic control. Here we assemble a panel of 412 European wheat varieties, along with corresponding DUS phenotypic data and genotype using the TaNG 43k genotyping array. The resulting 14,921 polymorphic genetic markers were distributed approximately evenly between the A (33%), B (38%), and D (29%) wheat sub-genomes. DUS characteristic heritability (h(2)) varied (mean = 0.44), ranging from 0.27 ('ear: glaucosity') to 1.00 ('ear: scurs or awns'). Subsequent genome-wide association study (GWAS) identified significant marker-trait associations for 15 of the 24 DUS characteristics analysed, resolved into 57 genetic loci. Of note, a highly significant association (- log(10)P = 44.79) for 'seed: colouration with phenol' on chromosome 2A was just 0.37 Mb from the Ppo-A1 gene known to control discolouration in wheat food products. GWAS was less successful for DUS characteristics with low heritability (traits with h(2) below or above 0.5 had a mean of 1.2 and 5.3 GWAS hits, respectively), for which the use of molecular markers would be more suited to alternative approaches such as genomic prediction. Collectively, this work will inform marker-aided approaches for DUS-relevant applications.

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