Peer Review #2 of "Predicting gene expression using DNA methylation in three human populations (v0.1)"

ABSTRACTVariable Number Tandem Repeats (VNTRs) are composed of large tandemly repeated motifs, many of which are highly polymorphic in copy number. However, due to their large size and repetitive nature, they remain poorly studied. To investigate the regulatory potential of VNTRs, we used read-depth data from Illumina whole genome sequencing to perform association analysis between copy number of ~70,000 VNTRs (motif size ≥10bp) with both gene expression (404 samples in 48 tissues) and DNA methylation (235 samples in peripheral blood), identifying thousands of VNTRs that are associated with local gene expression (eVNTRs) and DNA methylation levels (mVNTRs). Using large-scale replication analysis in an independent cohort we validated 73-80% of signals observed in the two discovery cohorts, providing robust evidence to support that these represent genuine associations. Further, conditional analysis indicated that many eVNTRs and mVNTRs act as QTLs independently of other local variation. We also observed strong enrichments of eVNTRs and mVNTRs for regulatory features such as enhancers and promoters. Using the Human Genome Diversity Panel, we defined sets of VNTRs that show highly divergent copy numbers among human populations, show that these are enriched for regulatory effects on gene expression and epigenetics, and preferentially associate with genes that have been linked with human phenotypes through GWAS. Our study provides strong evidence supporting functional variation at thousands of VNTRs, and defines candidate sets of VNTRs, copy number variation of which potentially plays a role in numerous human phenotypes.

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Predicting gene expression using DNA methylation in three human populations

PeerJ ◽

10.7717/peerj.6757 ◽

2019 ◽

Vol 7 ◽

pp. e6757 ◽

Cited By ~ 8

Author(s):

Huan Zhong ◽

Soyeon Kim ◽

Degui Zhi ◽

Xiangqin Cui

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Linear Regression ◽

Human Population ◽

Penalized Regression ◽

Superior Performance ◽

Gene Region ◽

Human Populations ◽

Epigenetic Mark ◽

Lasso Regression

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.

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Predicting gene expression using DNA methylation in two human populations

10.7287/peerj.preprints.27055v1 ◽

2018 ◽

Author(s):

Huan Zhong ◽

Soyeon Kim ◽

Degui Zhi ◽

Xiangqin Cui

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Linear Regression ◽

Penalized Regression ◽

Superior Performance ◽

Gene Region ◽

Human Populations ◽

Epigenetic Mark ◽

Lasso Regression ◽

Cpg Sites

Background. DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene expression, especially the negative regulation in the promoter region. However, its correlation with gene expression at 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 but not RNA samples. Results. We studied two human population datasets, Multiple Tissue Human Expression Resource Projects (MuTHER)’s Adipose tissue as well as asthma and normal peoples’ peripheral blood mononuclear cell (PBMC), for predicting gene expression using methylation of all CpG sites from the gene region. 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, even with LASSO regression, very small prediction R2 was obtained for the majority of genes and only about one thousand genes had prediction R2 greater than 0.1. GO term and pathway analyses of these more predictable genes showed that they are enriched for immune and defense genes. Conclusion. In human populations, DNA methylation of CpG sites at gene region have weak prediction power for gene expression. The relatively more predictable genes tend to be defense and immune genes.

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Predicting gene expression using DNA methylation in two human populations

10.7287/peerj.preprints.27055 ◽

2018 ◽

Author(s):

Huan Zhong ◽

Soyeon Kim ◽

Degui Zhi ◽

Xiangqin Cui

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Linear Regression ◽

Penalized Regression ◽

Superior Performance ◽

Gene Region ◽

Human Populations ◽

Epigenetic Mark ◽

Lasso Regression ◽

Cpg Sites

Background. DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene expression, especially the negative regulation in the promoter region. However, its correlation with gene expression at 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 but not RNA samples. Results. We studied two human population datasets, Multiple Tissue Human Expression Resource Projects (MuTHER)’s Adipose tissue as well as asthma and normal peoples’ peripheral blood mononuclear cell (PBMC), for predicting gene expression using methylation of all CpG sites from the gene region. 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, even with LASSO regression, very small prediction R2 was obtained for the majority of genes and only about one thousand genes had prediction R2 greater than 0.1. GO term and pathway analyses of these more predictable genes showed that they are enriched for immune and defense genes. Conclusion. In human populations, DNA methylation of CpG sites at gene region have weak prediction power for gene expression. The relatively more predictable genes tend to be defense and immune genes.

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Worldwide patterns of human epigenetic variation

10.1101/021931 ◽

2015 ◽

Cited By ~ 2

Author(s):

Oana Carja ◽

Julia L MacIsaac ◽

Sarah M Mah ◽

Brenna M Henn ◽

Michael S Kobor ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Epigenetic Modification ◽

Dna Polymorphisms ◽

Human Populations ◽

Mrna Levels ◽

Nucleotide Polymorphisms ◽

Epigenetic Diversity ◽

Or Gene ◽

Gene Expression Levels

DNA methylation is an epigenetic modification, influenced by both genetic and environmental variation, that can affect transcription and many organismal phenotypes. Although patterns of DNA methylation have been shown to differ between human populations, it remains to be determined whether epigenetic diversity mirrors the patterns observed for DNA polymorphisms or gene expression levels. We measured DNA methylation at 480,000 sites in 34 individuals from five diverse human populations in the Human Genome Diversity Panel, and analyzed these together with single nucleotide polymorphisms (SNPs) and gene expression data. We found greater population-specificity of DNA methylation than of mRNA levels, which may be driven by the greater genetic control of methylation. This study provides insights into gene expression and its epigenetic regulation across populations and offers a deeper understanding of worldwide patterns of epigenetic diversity in humans.

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