Predicting gene expression using DNA methylation in two ... - PeerJ

Predicting gene expression using DNA methylation in two human populations Huan Zhong 1 2 3

1

, Soyeon Kim

2

, Degui Zhi

2

, Xiangqin Cui Corresp.

3

Department of Biology, Hong Kong Baptist University, Hong Kong, China School of Biomendical Informatics, University of Texas Health Center at Houston, Houston, Texas, United States Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States

Corresponding Author: Xiangqin Cui Email address: [email protected]

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.

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27055v1 | CC BY 4.0 Open Access | rec: 26 Jul 2018, publ: 26 Jul 2018

1

Predicting Gene Expression Using DNA methylation in Two

2

Human Populations

3 4

Huan Zhong1, Soyeon Kim2, Degui Zhi2, Xiangqin Cui3

5 6

1

Department of Biology, Hong Kong Baptist University, HONG KONG

7

2

School of Biomedical Informatics, University of Texas Health Science Center at Houston,

8

Houston, TX, USA

9

3

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA

10 11

Corresponding author:

Xiangqin Cui

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Email: [email protected]

13

14

Keywords

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DNA methylation, Methylation Microarray, transcriptome, LASSO

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18

Abstract

19

Background

20

DNA methylation, an important epigenetic mark, is well known for its regulatory role in gene

21

expression, especially the negative regulation in the promoter region. However, its correlation

22

with gene expression at population level has not been well studied. In particular, it is unclear if

23

genome-wide DNA methylation profile of an individual can predict her/his gene expression

24

profile. Previous studies were mostly limited to association analyses between single CpG site

25

methylation and gene expression. It is not known whether DNA methylation of a gene has

26

enough prediction power to serve as a surrogate for gene expression in existing human study

27

cohorts with DNA samples but not RNA samples.

28

Results

29

We studied two human population datasets, Multiple Tissue Human Expression Resource

30

Projects (MuTHER)’s Adipose tissue and Asthma and normal peoples’ peripheral blood

31

mononuclear cell (PBMC), for predicting gene expression using methylation of all CpG sites

32

from the gene region. Three prediction models were investigated; single linear regression,

33

multiple linear regression, and least absolute shrinkage and selection operator (LASSO)

34

penalized regression. Our results showed that LASSO regression has superior performance

35

among these methods. However, even with LASSO regression, very small prediction R2 was

36

obtained for the majority of genes and only about one thousand genes had prediction R2 greater

37

than 0.1. GO term and pathway analyses of these more predictable genes showed that they are

38

enriched for immune and defense genes.


39

Conclusion

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In human populations, DNA methylation of CpG sites at gene region have weak prediction

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power for gene expression. The relatively more predictable genes tend to be defense and immune

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genes.

43 44 45

46

Background

47 48

DNA methylation has long been recognized as an important epigenetic modification in

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regulating gene expression (Razin & Riggs, 1980). This process often occurs at CG dinucleotides

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sites (CpG site), adding a methyl group to the cytosine residue(You & Jones, 2012; Wagner et

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al., 2015). In mammals, more than 70% of CpG sites are methylated.

52 53

The regulatory role of DNA methylation has been mostly studied with a small number of CpG

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sites in a limited number of genes. The more recent application of microarrays and next

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generation sequencing enables Large-scale analysis of DNA methylation and gene expression

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across the whole genome(Krueger et al., 2012). However, most genome-wide methylation and

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expression studies have small sample sizes for comparing controlled groups. Only a few studies

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profiled both genome-wide DNA methylation and gene expression in larger human populations

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and examined their relationship. Del Rey et al., (Del Rey et al., 2013)studied the genome-wide

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DNA methylation and gene expression in 83 low-risk subtypes of Myelodysplastic syndrome

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(MDS) patients and 36 controls using microarrays. While they found negative correlations


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between methylation and gene expression in a large proportion of differentially expressed and

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differentially methylated genes, they also found substantial positive correlations in them. In

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another study of 648 twins, overall negative correlations were found in the adipose tissue,

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promoter region (-0.018), gene body (-0.013) and 3-prime UTR (-0.007)(Grundberg et al., 2013).

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More recently, Wagner et al.(Wagner et al., 2014) profiled the genome wide DNA methylation

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and gene expression in forearm skin fibroblast among 62 unrelated individuals. They found that

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the association between gene expression and methylation is not always negative in promoter

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region or positive in gene body.

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In this study, we examine the DNA methylation and gene expression relationship in two large

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human datasets. One is from the MuTHER project that includes 856 female European individuals

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from TwinsUK Adult Twin Registry(Spector & Williams, 2006). Another dataset is the

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Childhood Asthema study that has 194 inner city children(Yang et al., 2015), which we call

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PBMC dataset for convenience throughout the paper. We determine the overall relationship

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between DNA methylation and gene expression for each gene and evaluate the predictive

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potential of DNA methylation for gene expression. We demonstrate that a penalized regression

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improves the overall prediction. However, the prediction power is still low for most genes. For

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only a small set of genes, DNA methylation has substantial prediction power, especially the

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immune genes. Similar to PrediXcan’s prediction of gene expression using genotype

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variations(Gamazon et al., 2015), we wrapped our predictions based on DNA methylation into a

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package named “MethylXcan”.

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Methods

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1) Datasets：


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Adipose Dataset

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This dataset is from the MuTHER study, consisting of 856 female European-descent individuals

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enrolled in the TwinsUK Adult Twin Registry. The quartile normalized gene expression and

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DNA methylation data from subcutaneous fat were downloaded from ArrayExpress

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(http://www.ebi.ac.uk/arrayexpress/).

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1140) were generated for 25160 genes using Illumina HumanHT-12 v3.0 on 825 individuals.

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The log2-transformed signals were quartile normalized for each tissue followed by quartile

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normalized across the whole population(Grundberg et al., 2012). The DNA methylation data

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(accession number E-MTAB-1866) were generated using Illumina Infinium

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HumanMethylation450 from 649 female twins. The methylation beta values were already

94

quantile normalized for each type of probe, ranging from 0 (unmethylated) to 1 (total-

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methylated) [6].

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PBMC Dataset

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This dataset was downloaded from Gene Expression Ominbus (GSE40736). It includes 194

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inner-city children with 97 cases of atopy and persistent asthma and 97 healthy controls. All the

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study subjects were 6 to 12 years old from African American, Dominican-Hispanic and Haitian-

The gene expression data (accession number E-TABM-

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Hispanic background [9]. DNA methylation data were generated using Illumina’s Infinium

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Human Methylation450k BeadChip. The normalized data matrix was downloaded from

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ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40576/matrix/ and the methylation M-

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values were converted to beta values ranging from 0 to 1. Gene expression data were generated

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for 23612 genes using Nimblegen Human Gene Expression arrays (12x135k). The normalized

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data matrix was downloaded from


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ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40732/matrix/. According to the

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publication, one outlier sample has been removed after principle component analysis, SWAN

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normalization was used for methylation data. Log2 transformation and RMA normalization were

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used for gene expression data. For each gene, expression level was standardized across samples.

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2) Dataset cleaning and filtering

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To assess the DNA methylation effect in prediction gene expression, we defined the

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“methylation probes” as the 344,303 probes in Table S1 of the Grundberg’s paper. The probes on

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the methylation array but excluded from the Table S1, which have potential SNP effects or cross

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hybridization effects, are termed “S&C probes”. The combinations of these two types of probes

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are termed “all probes”.

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The Adipose dataset has 32,478 missing values in the DNA methylation data. Samples with

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missing values were excluded from regression analysis. Among the 485679 probes in the dataset,

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344201 probes remained in Adipose dataset after filtering. For the PBMC dataset, 344180 out of

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the 485461 probes in the dataset remained after filtering. For method comparisons and the

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consistency of the analysis, only genes that have the LASSO models were used, 8121 genes and

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150349 CpG sites in Adipose dataset and 4251 genes and 73553 CpG sites in PBMC datasets.

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(Supplementary Table 1).

123 124

3) Modeling the relationship between gene expression and DNA methylation.

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CpG probes were mapped to genes using UCSC RefGene annotation. Gene expression and DNA

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methylation data for each gene were extracted using in-house perl script. Since there was no


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missing value for methylation of the PBMC dataset, all samples were used in the regression

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analysis.

129 130

We used three types of regressions, single linear regression, multiple regression, and least

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absolute shrinkage and selection operator (LASSO) regression(Tibshirani, 1996), to model the

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linear relationship between gene expression and DNA methylation. Squared correlation (R2)

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between predicted and observed data was used to compare the three types of regressions. In the

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single linear regression, each CpG site was modeled separately to predict gene expression level.

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The CpG site that provides maximum R2 was used to represent each gene. In multiple

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regressions, all the CpG sites in each gene were used as predictors and the R2 was calculated. In

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the LASSO regression, all CpG sites were used to predict the gene expression. We used the

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GLMNET package in R to fit the LASSO model in which penalized parameters were obtained

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using 10-fold cross-validation to minimize the mean squared error, while the predictors and

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response variables were all standardized.

141 142

4) Cross validation

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In addition to calculating the R2 from fitting the models (fitting R2), we also conducted five-fold

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cross validation to compare the prediction power of the three regression models using the

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validation R2 (R2.cv). Specifically, the samples were randomly separated into training set (4/5 of

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data) and testing set (1/5 of data). The procedure was iterated 10 times and the mean R2 of the 10

147

five-fold cross validations was used as our final cross validation R2 for each model. For single

148

regression, cross validation was conducted for each CpG site and the maximum R2 was used for


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each gene. For LASSO regression analysis, we first obtained the optimal penalty parameter using

150

ten-fold cross validation and then used another five-fold cross validation to evaluate the

151

predictive performance of the model.

152

Note that we calculated fitting R2 in the LASSO cross validation models. We used the entire

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datasets as testing in the LASSO cross validation models in order to obtain the fitting R2 in a

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fashion consistent with the multiple and single cross-validation models. In this case, all the R2

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values in the paper are squared correlation of the predicted and the true values in the training set.

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5) Model comparisons on significant genes

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We first identified genes that showed overall model prediction p values less than 0.05 in Lasso

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regressions and then compared the three regression models on these genes.

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6) Gene Ontology (GO) and pathway enrichment analysis.

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For genes with R2 greater than 0.1, we use The Database for Annotation, Visualization and

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Integrated Discovery (DAVID ) at https://david.ncifcrf.gov/ (Huang, Sherman & Lempicki,

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2008) to conduct GO term enrichment analysis based on modified Fisher Exact Test. The

163

background genes were set to be the genes on the expression array, HumanHT-12_V3_0_

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R2_11283641_A. The significantly overrepresented GO terms were selected based on the EASE

165

Score, which is the geometric mean of p-values on logarithm scale for the member terms. We

166

applied medium classification stringency in the DAVID website to our data.

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“GOTERM_BP_FAT” was used to obtain more information in biological processes of the Gene

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Ontology enrichment analysis. “KEGG_PATHWAY” was selected for pathway enrichment

169

analysis in the same fashion. The most enriched GO terms and pathways with low p-value or

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FDR were shown in the results.


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7) Gene expression prediction using different type of probes on the methylation microarray

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The probes excluded by Table S1 of the Grundberg’s paper were treated as probes with SNP

173

and/or hybridization effects (S&C probes). We compared these probes, the methylation probes,

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and the combination of these two types of probes in predicting gene expression.

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8) Software Package: MethylXcan

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We created a software package, MethylXcan, for predicting gene expression using DNA

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methylation array data in the analyses of this paper. This package includes all three regressions

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and calculates the squared correlation for each model. The program was written in R and Perl,

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and has been tested under linux or MACSOX system. Users can use this package on their own

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data after formatting their methylation data, expression profiling data, and annotation files as

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specified by the package. URL: https://github.com/dorothyzh/MethylXcan

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183

Results

184 185

Most DNA methylation studies analyze one CpG at a time in explaining gene expression. In this

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study we set out to find whether combining all CpG sites in a gene can better predict the gene

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expression in a human population. We obtained two human datasets, the Adipose dataset

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generated from subcutaneous fat tissue and the Childhood Asthma dataset generated from

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PBMC. To evaluate the predicting power of DNA methylation on gene expression, we conducted

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three types of linear regression analyses, single regression, multiple regression, and LASSO

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regression for each gene. The squared correlations (R2) were used for model comparisons. To

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focus on DNA methylation effect, we first left out CpG probes that overlap SNPs or cross-


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hybridize to multiple locations. In addition, since some genes fail to establish a LASSO model,

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we only focus on genes with LASSO models for comparing different regression methods.

195 196

Multiple regressions using all CpGs from a gene predict gene expression the best in model

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fitting.

198 199

For the Adipose dataset, the single regression identified a large number of genes with significant

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CpG sites, about half (3509) with at least one CpG site significant at significance level of 0.0001

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and most of them (7423) have at least one CpG site significant at significance level of 0.05.

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However, the prediction power represented by the maximum R2 for these CpG sites is generally

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low. Only 20 genes have maximum R2 greater than 0.3 and 491 (3.0%) genes have maximum R2

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larger than 0.1 (Table 1). Since multiple CpG sites from each gene were assayed on the

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methylation microarray, we applied multiple linear regression to utilize all CpG sites as

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predictors simultaneously. The R2 explained by the regression model did improve substantially

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for the majority of genes compared with that of the single linear regression model (Figure 1A).

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The significant genes (red color) have relatively higher R2 compared with the non-significant

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genes (Figure 1). For the PBMC data, a much smaller number of significant CpG sites were

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identifies at the same significance level for the 4251 genes analyzed (582 with p value less than

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0.0001 and 3112 with p value less than at 0.05 for single regression) potentially due to the

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smaller sample size. However, the numbers of genes with larger R2 values are comparable with

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those from the Adipose dataset (Table 1). The improvement of R2 from the multiple regressions

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over single regress in the PBMC dataset is also similar to that in the Adipose dataset (Figure

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1A).


216 217

Compared with multiple regressions, LASSO regression did not generate R2 as high as that from

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multiple regressions in both datasets (Figure 1B and D). For example, the number of genes with

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R2 greater than 0.2 decreased from 472 to 343 for the Adipose dataset and from 1163 to 561 for

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the childhood asthma PBMC datasets (Table 2). With LASSO regression, only about 19.7% of

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the genes (1608 out of 8123) from the Adipose dataset and about 56% of the genes (2382 out of

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4251) from the PBMC dataset have prediction R2 greater than 0.1.

223 224

LASSO regression shows best prediction in cross-validation.

225 226

To better assess the accuracy of the predictive models, we performed 5-fold cross validation on

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single, multiple regressions, and LASSO regressions to estimate the R2. For comparison purpose,

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we present R2 with genes for which the LASSO models are available. The results showed that the

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LASSO regression produced much larger R2 values than the single regression and less dramatic

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but discernable increases over multiple regressions (Figure 2). These differences are also

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reflected in the number of genes with R2 exceeding certain thresholds. For example, 893 genes

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(11%) from the Adipose dataset have R2 greater than 0.1 from LASSO regression, while 791

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genes (9.7%) and 125 genes (1.5%) have R2 greater than 0.1 from multiple regression and single

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regression, respectively (Table1). For genes with R2 greater than 0.3, LASSO regression has 44

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genes (0.54%) while multiple regression and single regression have 40 (0.49%) and 2 genes

236

(0.03%), respectively. These results indicate that penalized regression has better prediction than

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multiple or single regressions for cross-validation test datasets. Cross validation tends to

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overcome bias and over-fitting issues. As expected, cross-validation R2 values are generally


239

lower than those from the model fittings reflected by the smaller number of genes with R2 values

240

greater than certain cut-offs (Table 1).

241

regression fitting have relatively higher cross-validation R2 than non-significant genes (red dots

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in Figure 2) as expected. Similar results were obtained from the PBMC dataset (Table 1 and

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Figure 2 C and D).

In addition, the significant genes from multiple

244 245

To make sure that the prediction R2 is larger than thoses from random chance, we compared the

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cumulative R2 from the two real datasets with those from the the null distribution of correlations

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based on Fisher z-transfromation in quantile-quantile plots (Figure 3). Both datasets show that

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the observed R2 values are much larger than the expected R2 values from random chance (Figure

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3A and 3C). In addition, the Adipose dataset has a larger departure than the PBMC dataset,

250

indicating that the LASSO model could capture more proportion of the transcriptome variability

251

in the Adipose dataset. This is potentially due to the larger sample size or the regulative nature of

252

the different tissues.

253 254

Using all probes improves prediction power for gene expression

255 256

In order to evaluate DNA methylation power in predicting gene expression, we first left out a

257

large proportion of probes potentially affected by genetic or cross-hybridization effects

258

(Supplementary Table 1). However, using all probes on the array is preferred if our goal is to

259

achieve better prediction accuracy of gene expression. To evaluate the prediction power from all

260

probes, we included all available probes in LASSO regression and found that the overall

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prediction power did increase compared to the models using only the methylation probes (Figure


262

3B and 3D). We observed more genes with R2 values exceeding the thresholds (Table 1). In

263

addition, the largest R2 value is much larger when all probes are used compared that from only

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the methylation probes. For example, the largest R2 is 0.92 from all probes compared to 0.54

265

from only methylation probes in the Adipose dataset (Figure 3B and Figure 3A). Likewise, the

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largest R2 is 0.88 from all probes compared to 0.46 from methylation probes alone in the PBMC

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dataset (Figure 3C and 3D). Furthermore, LASSO models are available for more genes when all

268

probes are used in both datasets (Supplementary Figure 2).

269 270

The increase of prediction power on gene expression from using all probes on the methylation

271

microarray suggests that there is contribution from the probes with potential SNP effects or cross

272

hybridization effects (S&C probes) we initially excluded in estimating methylation prediction.

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To further examine the size and nature of their contribution, we separately estimated the

274

prediction power of the methylation probes, S&C probes, and the combination of them (all

275

probes). The results showed that the S&C probes have independent prediction power from the

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methylation probes and the combination of both has increased prediction power over the

277

methylation probes alone (red points vs black line in the left two panels in Figure 4). The

278

prediction power from the S&C probes was estimated for genes with enough SNP probes to form

279

a LASSO model and their prediction power are mostly above zero (blue points in Figure 4). The

280

fact that the blue points are randomly distributed instead of following the black line suggests that

281

the two sources of R2 are not correlated; therefore, the larger genetic effect and larger epigenetic

282

effect do not seem to coexist in the same genes.

283

large prediction powers from either methylation probes or S&C probes.

284

methylation probes show continuous methylation values while the S&C probes show categorical

285

values due to limited genotypes of the samples.

Figure 5 shows some examples of genes with As expected, the


286 287

Methylation probes tend to better predict the expression of defense and immune gene

288

expressions

289 290

To examine the potential biological function of the genes showing good predictability of gene

291

expression by DNA methylation, we conducted gene ontology (GO) and pathway enrichment

292

analysis using DAVID. For the Adipose dataset, we examined the 1608 genes with R2 greater

293

than 0.1 from LASSO regression. With false discovery rate (FDR) of 0.1, the significantly

294

enriched biological process (GOTERM_BP_FAT) GO terms are response to inflammatory,

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defense activities and wounding, as well as antigen (Table 2), which seem to be consistent with

296

the previous findings for subcutaneous fat cells (Berg and Scherer 2005). For the PBMC dataset,

297

we conducted the same gene set enrichment analysis on the 2382 genes with R2 greater than 0.1

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and found that the significantly enriched terms are mostly related to defense and immune

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functions, lymphocyte activation, leukocyte activation, and immune response (Supplementary

300

Table 2). These results seem to be reasonable for atopy and persistent asthma blood cells.

301

Similar GO terms and pathways were enriched in results obtained from all probes

302

(Supplementary Tables 3 and 4).

303

304

Discussion

305 306

We examined the relationship between gene expression and DNA methylation across the genome

307

using data from two large human studies. We explored three linear regression models for

308

predicting gene expression and found that shrinkage based LASSO multiple regression provides


309

the best prediction. However, even with LASSO regression, the methylation probes can predict

310

expression in only a small proportion of genes with moderate prediction power. These genes are

311

mostly immune and defense genes. Using all probes on the methylation array does improve

312

prediction power to some degree.

313 314

We used squared correlation (R2) to assess predictive power in regression models. The single

315

linear regression is based only on the best predictive CpG in each gene while the multiple

316

regression models uses all CpG sites in each gene. However, the multiple regression model has

317

substantial over-fitting problem for genes with large number of CpGs. The shrinkage based

318

LASSO regression model overcomes the over-fitting problem without losing predictability.

319

LASSO imposes sparsity among the coefficients and puts constraint on the overall absolute

320

values of the regression coefficients, which forces certain coefficients to be zero. This property is

321

beneficial in variable selection and improves model interpretability. LASSO is not the only

322

shrinkage-based regression method. There are other penalty regression models, such as the Ridge

323

(Hoerl & Kennard, 1970) and elastic net(Zou & Hastie, 2005). However, ridge regression scales

324

all coefficients by a constant without setting some of them to zero, which is less ideal for

325

variable selection. The elastic net method is a hybrid of the ridge and LASSO. There are also

326

other advanced shrinkage methods, such as elastic net with rescaled-coefficients and grouped

327

lasso(Yuan & Lin, 2006; Meier, Van De Geer & Bühlmann, 2008), as well as other variants of

328

LASSO(Zou et al., 2013). Further evaluation is needed for their merits in improving prediction

329

of gene expression in this setting.

330


331

One potential reason for low prediction power from DNA methylation on gene expression is the

332

complex mechanisms of gene expression regulation. In addition to DNA methylation,

333

transcription factors, histone modification(Verdin & Ott, 2015), and non-coding RNAs

334

(Janowski et al., 2005; Ting et al., 2005; Ting, McGarvey & Baylin, 2006; Kaikkonen, Lam &

335

Glass, 2011) all play critical roles in gene transcription regulation(Jones, 2015). Another

336

potential reason for low prediction power of methylation is that the landscape of DNA

337

methylation differs dramatically across cell types, tissues(Lokk et al., 2014), ages(Teschendorff

338

et al., 2010), and races(Song et al., 2015). The relationship between gene expression and DNA

339

methylation could also vary substantially across these factors. The correlation between gene

340

expression and DNA methylation from bulk studies at population level encompasses all these

341

variability; therefore, it is not surprising to see low prediction power based on DNA methylation

342

alone. It is likely to be limited in general that the potential of DNA methylation alone as

343

surrogate for gene expression. The combination of DNA methylation and other predictors, such

344

as genotype, may be more powerful for this purpose.

345 346

Although the overall prediction power of DNA methylation on gene expression was low in our

347

analyses of both datasets, much better prediction power was found with the immune and defense

348

genes. This result is consistent with the extensive evidence linking aberrant DNA methylation

349

patterns with genes involved in immune deficiencies, autoimmune disorders, and inflammatory

350

disease(Bayarsaihan, 2011). Some key immune genes, such as mb1 (CD79a)(Gao et al., 2009)

351

and MHCII, (HLA-DR, DQ, DP) (Seguín-Estévez et al., 2009; Suárez-Álvarez et al., 2010;

352

Majumder & Boss, 2011)have been shown to have strong association between DNA methylation

353

and their key immune functions. On cellular level, hypomethylation in T cell promoter regions

354

plays an important role in lupus(Deng et al., 1998; Gorelik & Richardson, 2010). In addition,


355

DNA methylation remodeling is critical in the differentiation of naïve immune cells into antigen-

356

specific effector cells in the response to the pathogen or virus(Scharer et al., 2013). By altering

357

DNA methylation pattern, cells can adapt to external stimuli via changes in gene expression.

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Given these evidences and the results in our study, DNA methylation could be more useful in

359

predicting the gene activities in the immune and defense system.

360

361

Conclusions

362

We explored three regression methods to predict gene expression using DNA methylation, single

363

regressions, multiple regressions, and LASSO penalized regression. LASSO regression reduces

364

over-fitting and improved the prediction power. Both datasets we analyzed shows overall low

365

prediction power. The better predictive genes (with squared correlations larger than 0.1 in

366

LASSO model) are mostly involved in immune, inflammatory, and wound responses. Overall,

367

we will recommend caution for using one’s methylation profile to predict one’s transcriptome.

368 369

List of Abbreviations

370

SNP: single nucleotide polymorphisms

371

LASSO: least absolute shrinkage and selection operator

372

R2: squared correlation

373

S&C: the probes have potential SNP effects or cross hybridization effects

374

MuTHER: Multiple Tissue Human Expression Resource Project

375

PBMC: peripheral blood mononuclear cell

376

MDS: myelodysplastic syndrome


377

GO: gene ontology

378 379

Availability of data and material

380

For Adipose Dataset, both gene expression and DNA methylation data can be found at

381

http://www.ebi.ac.uk/arrayexpress/

382

For PBMC Dataset, gene expression data are at

383

ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40732/matrix/., while DNA methylation

384

data are at ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40576/matrix/.

385

Software package, ‘MethylXcan’ are at https://github.com/dorothyzh/MethylXcan.

386 387

388

References

389 390

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Figure 1 R 2 comparison among three regression models Multiple regression is compared to single (left two panels) and lasso (right two panels) regressions in two datasets, Adipose and PBMC. R 2 shown here are on cubic root scale for visualization clarity. “single”, single linear regression with the most significant CpG site as predictor; “multiple”, multiple regression with all methylation CpG sites in a gene as predictors. Red points represent significant genes from multiple regressions at significance level of 0.05. Blue solid line is the identity line and the dashed lines represent R 2 of 0.1.



Figure 2 R 2 comparisonamong regression models in cross validation. LASSO regression is compared to single (left two panels) and multiple (right two panels) regressions for two datasets, Adipose and PBMC. Five-fold validation was used for all regression models. The red points represent the significant genes from multiple regressions (p =0.1

>=0.1

>=0.2

>=0.2

>=0.3

>=0.3

NO

491

125

85

18

20

2

YES

591

137

114

25

34

7

NO

2211

791

472

177

114

40

YES

3105

966

765

232

217

67

NO

1608

893

343

197

110

44

YES

2189

1094

538

272

195

75

NO

851

381

108

33

14

4

YES

1330

666

212

90

58

34

NO

3358

746

1163

109

382

30

YES

4455

994

1902

197

694

64

NO

2382

1022

561

165

142

30

YES

3207

1465

870

289

235

66

SNP-probe

3

Dataset

Method Inclusion

4 Single

5 6 7

Multi Adipose

8 LASSO

9 10

Single

11 12

PBMC

Multi

13 14

LASSO

15 16


Table 2(on next page) Gene Ontology andKEGG pathway enrichment analysis for more predictive genes in the Adipose dataset.

Genes with R2 greater than 0.1 (1680) were selected for conducting the GO and Pathway enrichment analyses. Enriched and representative terms with FDR less than 0.01 are included in the table.


1 Fold Term

P-value

FDR

Enrich GO:0006952~defense response

2.28

1.50E-08

2.70E-05

GO:0006954~inflammatory response

2.69

2.72E-07

4.80E-04

GO:0009611~response to wounding

2.22

5.79E-07

0.001

8.93

8.43E-07

0.0015

peptide or polysaccharide antigen via MHC class II

7.58

3.94E-06

0.007

KEGG_PATHWAY:hsa05330~Allograft rejection

5.91

8.58E-06

0.01

3.52

9.17E-06

0.011

presentation

3.73

1.65E-05

0.02

KEGG_PATHWAY:hsa05332~Graft-versus-host disease

5.45

1.87E-05

0.02

KEGG_PATHWAY:hsa04940~Type I diabetes mellitus

5.06

3.77E-05

0.046

GO:0048002~antigen processing and presentation of peptide antigen GO:0002504~antigen processing and presentation of

KEGG_PATHWAY:hsa05322~Systemic lupus erythematosus KEGG_PATHWAY:hsa04612~Antigen processing and

2
