[8] (1) Significant SNPs that were repeatedly detected in different experiments (herein, E1a, E1b, E2a, and E2b were regarded as different experiments) were selected to identify candidate

genes underlying GLS resistance. (2) To scan for potential genes within a sequence region containing consensus significant SNPs, the 60-bp source sequences of these “consensus” significant SNPs were used to perform nucleotide BLAST searches against the B73 RefGen_v2 (MGSC) (http://blast.maizegdb.org/home.php?a=BLAST_UI). Local LD blocks that contained consensus significant SNPs were selected as target sequence regions to scan for potential genes, using the GenScan web server at http://genes.mit.edu/GENSCAN.html. Local LD blocks were defined by the confidence interval PD332991 method of Gabriel et al. [38] using Haploview 4.0 [33]. (3) To identify candidate genes for GLS resistance, predicted peptides of potential genes were used to search for conserved domains at the NCBI website http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. Genes with disease resistance-related annotations were evaluated as candidate genes for GLS resistance. The resistant control Shen 137 proved highly resistant to GLS, with average scores of grade 3 (G3)

in 2010 and G1 in 2011, respectively, whereas the susceptible line Dan 340 was highly susceptible JNJ-26481585 in vivo to GLS and was rated as G9 in both years (Fig. 1-A), indicating an appropriate level of inoculation in this study. for The significant (P < 0.0001) correlation (R2 = 0.864) ( Table 1) between the phenotypic data among the 2 years indicated that GLS resistance among these 161 lines was highly consistent across years. A quantitative distribution of the phenotypes among 161 lines in each year ( Fig. 1-A) suggests that maize resistance to GLS is quantitatively inherited. The genotypic variances among 161 lines were highly significant (P < 0.0001) in each year, and the broad-sense heritability of GLS resistance was 0.88 ( Table 1), revealing the presence of predominantly genetically controlled resistance in this panel. Phenotypic differences in the GLS PIFA

among these five subgroups were extremely significant (P < 0.0001). The PB subgroup, with the lowest PIFA, exhibited the most resistance to GLS ( Fig. 1-B), and differed significantly from the other subgroups according to the Student–Newman–Keuls multiple range test (SNK) ( Fig. 1-B), suggesting either that the resistance genes originate from the PB subgroup, or that more genetic information about GLS resistance is available in the PB subgroup, and that fitting population structure and kinship matrix information into the model is necessary for association mapping of this trait. In these four experiments, a total of 51 SNPs across 10 chromosomes were significantly associated with PIFA (P < 0.001) ( Fig. 2; Table 2).