However, a genome-wide association study (GWAS) for GLS resistanc

However, a genome-wide association study (GWAS) for GLS resistance has not yet been reported with Chinese maize germplasm. Accordingly, the objectives in this study were to (1) assess phenotypic variation among 161 Chinese maize inbred lines under artificial inoculation with a propagule Y27632 suspension, (2) identify genetic loci conferring

GLS resistance by performing a genome-wide association study of GLS resistance using 41,101 SNP markers in the population, and (3) identify candidate genes for GLS resistance. The results obtained here will help to drive the breeding process towards improvement of GLS resistance. An association mapping panel with 161 Chinese maize inbred lines was planted in a plant pathology nursery at Shenyang, Liaoning Province, China (41.48° N, 123.25° E), in 2010 and 2011, using complete randomized blocks with two replicates. Each plot was planted in single rows, 0.67 m apart and 4.5 m long, with a total of 20 plants per row. Among these lines, the inbred lines Shen 137 and Dan 340 were used as resistant and susceptible controls, respectively [15]. The association mapping panel was artificially inoculated during the bugle stage (V9–V11 developmental stage) with a

10-mL propagule suspension containing 2.5 × 104 conidia following the method of Dong et al. [10]. During the maize milky maturity stage, the disease reaction on each plant was scored on a R428 molecular weight scale with five levels (G1, G3, G5, G7, and G9) that represent the percentage of the infected foliar area (PIFA) as follows: G1 ≤ 5% PIFA and absence of symptoms; G3 = 6%–10% PIFA with few and sparse lesions; G5 = 11%–30% PIFA with lesions reaching the ear leaf

and a few lesions occurring on the leaves above the ear; G7 = 31%–70% PIFA with lesions reaching the leaves above the ear; G9 ≥ 71% PIFA with premature plant death before physiological maturity (black layer formation in kernels) [4] and [10]. GLS resistance was evaluated by PIFA for all plants in each row and the average score for the row comprised the phenotypic data. All the phenotypic data collected in 2010 and 2011 were summarized as percentages (e.g. PIFA). An arcsine transformation was performed and statistical tests Depsipeptide manufacturer were conducted using Statistical Analysis System (SAS) software [29]. A PROC UNIVARIATE normal plot was used to test whether the data was normally distributed. A standard analysis of variance (ANOVA) was performed using PROC GLM to determine variation in disease response. The general linear model procedure was used to analyze the effects of environments, block, inbred lines, and the interactions between these factors. Estimates of the variance components associated with all model terms were calculated using the PROC MIXED option.

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