Predicting gene expression using DNA methylation in three human populations
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 particul...
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PeerJ Inc.
2019-05-01
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| author | Huan Zhong Soyeon Kim Degui Zhi Xiangqin Cui |
| author_facet | Huan Zhong Soyeon Kim Degui Zhi Xiangqin Cui |
| author_sort | Huan Zhong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:46:35Z |
| format | Article |
| id | doaj.art-ac948df9eca94ec7b59f2bce6cbf8d4a |
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| language | English |
| last_indexed | 2024-03-09T06:46:35Z |
| publishDate | 2019-05-01 |
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| spelling | doaj.art-ac948df9eca94ec7b59f2bce6cbf8d4a2023-12-03T10:35:04ZengPeerJ Inc.PeerJ2167-83592019-05-017e675710.7717/peerj.6757Predicting gene expression using DNA methylation in three human populationsHuan Zhong0Soyeon Kim1Degui Zhi2Xiangqin Cui3Department of Biology, Hong Kong Baptist University, Hong Kong, ChinaSchool of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of AmericaSchool of Biomendical Informatics, University of Texas Health Center at Houston, Houston, TX, United States of AmericaDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States of AmericaBackground 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.https://peerj.com/articles/6757.pdfDNA methylationMethylation microarrayTranscriptomeLASSO |
| spellingShingle | Huan Zhong Soyeon Kim Degui Zhi Xiangqin Cui Predicting gene expression using DNA methylation in three human populations PeerJ DNA methylation Methylation microarray Transcriptome LASSO |
| title | Predicting gene expression using DNA methylation in three human populations |
| title_full | Predicting gene expression using DNA methylation in three human populations |
| title_fullStr | Predicting gene expression using DNA methylation in three human populations |
| title_full_unstemmed | Predicting gene expression using DNA methylation in three human populations |
| title_short | Predicting gene expression using DNA methylation in three human populations |
| title_sort | predicting gene expression using dna methylation in three human populations |
| topic | DNA methylation Methylation microarray Transcriptome LASSO |
| url | https://peerj.com/articles/6757.pdf |
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