Genome-wide association study reveals novel marker-trait associations for agronomic traits and covered smut resistance in barley (Hordeum vulgare L.)
摘要
Barley (Hordeum vulgare) is highly susceptible to Ustilago hordei-induced covered smut (CS) disease, which has the potential to cause significant losses in organic production systems. The objective of this investigation was to identify sources of resistance to CS, emphasizing the importance of understanding the genetic basis for improving resistance. To address this challenge, an association panel consisting of 148 European barley cultivars, previously genotyped with AFLP/SSR markers, was phenotyped under both non-stress (NS) and CS disease stress (DS) conditions. This study indicated significant variation among the varieties for 12 morphological, agronomic, and disease infestation traits. A genome-wide association analysis using linkage disequilibrium was also performed. The analysis of population structure segregated the population into two distinct subgroups. The mixed linear model (MLM) procedure revealed 206 significant marker-trait associations (MTAs): 35 on chromosome 2H, 31 on 6H, 13 on 5H, nine on 3H, four on 4H, four on 7H, and two on 1H; totaling 98 mapped and 108 unmapped MTAs. Among these, 69 were identified under NS conditions, while 137 were identified under DS conditions. Most MTAs were detected for internode distance, flag leaf length, and flag leaf sheath length. A total of 35 stable MTAs were identified for all traits in both conditions, including the disease infestation-MTA linked to Bmac0316-142 (on 6H). Additionally, 10 new pleiotropic genomic regions containing co-located MTAs regulating two or more traits were discovered. These regions include Bmac0134-151, E35M54-243, E37M33-501, and E38M55-251 (on 2H), E38M55-320 (on 3H), E38M55-114 and E35M48-410 (on 5H), Bmac0316-135, and HVM22-172 (on 6H), and E42M32-231 (on 7H). The highlighted MTAs offer immense promise for enhancing barley varieties’ resistance to CS. However, rigorous validation using comprehensive phenotypic and genotypic datasets is imperative to ascertain the robustness and reliability of these MTAs.