Screening Tall Fescue for Resistance to Rhizoctonia solani and Rhizoctonia zeae Using Digital Image Analysis.

Brown patch, caused by Rhizoctonia solani, is a destructive disease on tall fescue. Compared with R. solani, Rhizoctonia zeae causes indistinguishable symptoms in the field but varies in geographic distribution. This may contribute to geographic variability observed in the resistance response of improved brown patch-resistant cultivars. This study examined R. solani and R. zeae susceptibility of four cultivars, selected based on brown patch performance in the National Turfgrass Evaluation Program (NTEP), and nine plant introductions (PIs). Twenty genotypes per PI/cultivar were evaluated by using four clonal replicates in a randomized complete block design. Plants were inoculated under controlled conditions with two repetitions per pathogen. Disease severity was assessed digitally in APS Assess, and analysis of variance and correlations were performed in SAS 9.3. Mean disease severity was higher for R. solani (65%) than for R. zeae (49%) (P = 0.0137). Interaction effects with pathogen were not significant for PI (P = 0.0562) but were for genotype (P < 0.001). Moderately to highly resistant NTEP cultivars compared with remaining PIs exhibited lower susceptibility to R. zeae (P < 0.0001) but did not differ in susceptibility to R. solani (P = 0.7458). Correlations between R. solani and R. zeae disease severity were not significant for either PI (R = 0.06, P = 0.8436) or genotype (R = 0.11, P = 0.09). Breeding for resistance to both pathogens could contribute to a more geographically stable resistance response. Genotypes were identified with improved resistance to R. solani (40), R. zeae (122), and both pathogens (26).

Identification of Differentially Methylated Sites with Weak Methylation Effects.

Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.