Abstract
Epigenetics is the study of heritable alterations in gene function without changing the DNA sequence itself. It is known that epigenetic modifications such as DNA methylation and histone modifications are highly correlated with the regulation of gene expression. A twostage analysis is proposed that employs a hidden Markov model and a linear model to evaluate differential expression as related to DNA methylation for the purpose of examining the effects of DNA methylation on gene regulation using tiling array technology. In the first stage, a hidden Markov model (HMM) is employed to estimate the methylation status per tile by utilizing information of neighboring tiles. In the second stage, a linear model is applied to the expression data to identify significantly differentially expressed tiles given the changes in methylation status. The two-stage analysis is applied to Arabidopsis chromosome 4 tiling array data and the results are compared with a traditional ANOVA model which has been employed to identify significantly differentially expressed genes using microarray data.
Keywords
epigenetics, epigenomics, methylation, histone modification, tiling array, hidden Markov model, ANOVA model
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Yoo, Suk-Young and W., R. Doerge
(2008).
"A TWO-STAGE APPROACH FOR ESTIMATING THE EFFECT OF DNA METHYLATION ON DIFFERENTIAL EXPRESSION USING TILING ARRAY TECHNOLOGY,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1101
A TWO-STAGE APPROACH FOR ESTIMATING THE EFFECT OF DNA METHYLATION ON DIFFERENTIAL EXPRESSION USING TILING ARRAY TECHNOLOGY
Epigenetics is the study of heritable alterations in gene function without changing the DNA sequence itself. It is known that epigenetic modifications such as DNA methylation and histone modifications are highly correlated with the regulation of gene expression. A twostage analysis is proposed that employs a hidden Markov model and a linear model to evaluate differential expression as related to DNA methylation for the purpose of examining the effects of DNA methylation on gene regulation using tiling array technology. In the first stage, a hidden Markov model (HMM) is employed to estimate the methylation status per tile by utilizing information of neighboring tiles. In the second stage, a linear model is applied to the expression data to identify significantly differentially expressed tiles given the changes in methylation status. The two-stage analysis is applied to Arabidopsis chromosome 4 tiling array data and the results are compared with a traditional ANOVA model which has been employed to identify significantly differentially expressed genes using microarray data.
