|TRAN, HONG - Virginia Tech|
|ZHU, HONGXIAO - Virginia Tech|
|WU, XIAOWEI - Virginia Tech|
|KIM, GUNJUNE - Virginia Tech|
|LAROSE, HAILEY - Virginia Tech|
|HAAK, DAVID - Virginia Tech|
|ASKEW, SHAWN - Virginia Tech|
|BARNEY, JACOB - Virginia Tech|
|WESTWOOD, JAMES - Virginia Tech|
|ZHANG, LIQING - Virginia Tech|
Submitted to: Genes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/25/2018
Publication Date: 2/8/2018
Citation: Tran, H., Zhu, H., Wu, X., Kim, G., Clarke, C.R., Larose, H., Haak, D.C., Askew, S., Barney, J.N., Westwood, J.H., Zhang, L. 2018. Identification of differentially methylated sites with weak methylation effect. Genes. 9:75-92.
Interpretive Summary: There is increasing evidence that important traits in crops can be regulated through modifications to DNA that scientists term epigenetic changes. Stresses, such as treatment with herbicides, are known to alter plant DNA and thus, affect plant response to stress. Detecting these changes remains challenging, especially if the changes are minor. This manuscript introduces a novel algorithm that enables improved sensitivity and specificity of detection of small epigenetic changes. Compared to standard approaches, this approach improved detection of epigenetic changes in thale cress plants following treatment with different doses of Roundup herbicide. This improvement will enable more accurate detection of stress-induced changes in plants such as development of herbicide resistant weeds that are due to epigenetic-based DNA modifications.
Technical Abstract: 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 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 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.