Predicting gene expression using DNA methylation in three human populations

• Authors: Huan Zhong, Hong Kong Baptist University; Soyeon Kim, University of Pittsburgh; Degui Zhi, University of Texas Houston; Xiangqin Cui, Emory University
• Journal Title: PeerJ: Bioinformatics and Genomics
• Volume: Volume 7, Number 5
• Publisher: PeerJ Inc. | 2019-05-01, Pages e6757-e6757
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.7717/peerj.6757
• Subjects: Biology, Genetics; Biology, Microbiology; Biology, Biostatistics; Biology, Bioinformatics
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; DNA methylation; Methylation microarray; Transcriptome; Lasso; Integrative analysis; Body methylation; Adipose tissue; Start sites; Selection; Transcription; Epigenetics; Regression; Variants; Islands

Abstract

Background: DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene expression, especially the negative correlation in the promoter region. However, its correlation with gene expression across genome at human population level has not been well studied. In particular, it is unclear if genome-wide DNA methylation profile of an individual can predict her/his gene expression profile. Previous studies were mostly limited to association analyses between single CpG site methylation and gene expression. It is not known whether DNA methylation of a gene has enough prediction power to serve as a surrogate for gene expression in existing human study cohorts with DNA samples other than RNA samples. Results: We examined DNA methylation in the gene region for predicting gene expression across individuals in non-cancer tissues of three human population datasets, adipose tissue of the Multiple Tissue Human Expression Resource Projects (MuTHER), peripheral blood mononuclear cell (PBMC) from Asthma and normal control study participates, and lymphoblastoid cell lines (LCL) from healthy individuals. Three prediction models were investigated, single linear regression, multiple linear regression, and least absolute shrinkage and selection operator (LASSO) penalized regression. Our results showed that LASSO regression has superior performance among these methods. However, the prediction power is generally low and varies across datasets. Only 30 and 42 genes were found to have cross-validation R2 greater than 0.3 in the PBMC and Adipose datasets, respectively. A substantially larger number of genes (258) were identified in the LCL dataset, which was generated from a more homogeneous cell line sample source. We also demonstrated that it gives better prediction power not to exclude any CpG probe due to cross hybridization or SNP effect. Conclusion: In our three population analyses DNA methylation of CpG sites at gene region have limited prediction power for gene expression across individuals with linear regression models. The prediction power potentially varies depending on tissue, cell type, and data sources. In our analyses, the combination of LASSO regression and all probes not excluding any probe on the methylation array provides the best prediction for gene expression.