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Article

Identification of Differentially Methylated Sites with Weak Methylation Effects

1
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
2
Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA
3
Department of Plant Pathology, Physiology and Weed Science, Virginia Tech, Blacksburg, VA 24061, USA
4
Genetic Improvement of Fruits and Vegetables Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Genes 2018, 9(2), 75; https://doi.org/10.3390/genes9020075
Received: 2 December 2017 / Revised: 17 January 2018 / Accepted: 25 January 2018 / Published: 8 February 2018
(This article belongs to the Section Molecular Genetics and Genomics)
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. View Full-Text
Keywords: differentially methylated regions; wavelet-based functional mixed model; weak methylation effect differentially methylated regions; wavelet-based functional mixed model; weak methylation effect
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MDPI and ACS Style

Tran, H.; Zhu, H.; Wu, X.; Kim, G.; Clarke, C.R.; Larose, H.; Haak, D.C.; Askew, S.D.; Barney, J.N.; Westwood, J.H.; Zhang, L. Identification of Differentially Methylated Sites with Weak Methylation Effects. Genes 2018, 9, 75. https://doi.org/10.3390/genes9020075

AMA Style

Tran H, Zhu H, Wu X, Kim G, Clarke CR, Larose H, Haak DC, Askew SD, Barney JN, Westwood JH, Zhang L. Identification of Differentially Methylated Sites with Weak Methylation Effects. Genes. 2018; 9(2):75. https://doi.org/10.3390/genes9020075

Chicago/Turabian Style

Tran, Hong; Zhu, Hongxiao; Wu, Xiaowei; Kim, Gunjune; Clarke, Christopher R.; Larose, Hailey; Haak, David C.; Askew, Shawn D.; Barney, Jacob N.; Westwood, James H.; Zhang, Liqing. 2018. "Identification of Differentially Methylated Sites with Weak Methylation Effects" Genes 9, no. 2: 75. https://doi.org/10.3390/genes9020075

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