The Influences of Genetic and Environmental Factors on Methylome-wide Association Studies for Human Diseases

Abstract

DNA methylation (DNAm) is an essential epigenetic mechanism for normal development, and its variation may be associated with diseases. High-throughput technology allows robust measurement of DNA methylome in population studies. Methylome-wide association studies (MWAS) scan DNA methylome to detect new epigenetic loci affecting disease susceptibility. MWAS is an emerging approach to unraveling the mechanism linking genetics, environment, and human diseases. Here I review the recent studies of genetic determinants and environmental modifiers of DNAm, and the concept for partitioning genetic and environmental contribution to DNAm. These studies establish the correlation maps between genome and methylome, and enable the interpretation of epigenetic association with disease traits. Recent findings suggested that MWAS was a promising genomic method to identify epigenetic predictors accounting for unexplained disease risk. However, new study designs, analytical methods and shared resources need to be implemented to address the limitations and challenges in future epigenomic epidemiologic studies.

Introduction

Epigenetics usually refers to the heritable molecular modifications that have independent effect from primary DNA sequence, and can be modified by environmental exposures at various developmental stages throughout the lifespan [1, 2]. The epigenetic modification can be inherited across cell generations to exert a long-term impact on the development of chronic diseases. DNA methylation (DNAm) is an essential epigenetic mechanism for normal development and is associated with several key processes linked to chronic diseases. The methyl group is passed from the donor molecule to cytosine catalyzed by DNA methyltransferases (DNMT). Most human diseases are thought to be due to both genetic and environmental factors, and the interplay between genes and environment. Studying the epigenome in the well-characterized samples may enable us to discover novel genes and pathways through which genetic factor and environmental exposures influence disease development, and thereby provide new targets for prevention and treatment [2-4].

Continuously growing research in epigenetics and epigenomics has resulted in increasing knowledge of the important roles of epigenetics in normal development and disease process. After the completion of the Human Genome Project in 2003 [5], multiple consortial studies have emphasized on identifying and annotating epigenomic features in human. National Institutes of Health (NIH) roadmap epigenomics project (http://www.roadmapepigenomics.org/) aims to produce a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The mapping consortium has generated a comprehensive map of the dynamic human DNA methylome [6]. The International Human Epigenome Consortium (IHEC) is a global consortium with the primary goal of providing access to high-resolution reference human epigenome maps for normal and disease cell types to the research community (http://www.ihec-epigenomes.org/). Another major community project, ENCyclopedia Of DNA Elements (ENCODE), has targeted to identify all functional DNA elements in the human genome including some epigenetic modifications [7, 8]. The mapping of epigenomic features has provided important data sets and information for molecular mechanism linking epigenetic variants and functional outcomes. Additionally, the European Union has committed more than €200 million funding to support over 300 epigenetics projects [9]. The availability of high-throughput genotyping methods, high-density reference panel of human epigenome and well-characterized samples enables the epigenetic association study at the genomic and population level. Epigenome-wide association study (EWAS) is an examination of epigenome-wide markers in many individuals to scan for any epigenetic marker associated with a trait. Current EWAS in human populations are all based on genome-wide measurement of DNA methylation on cytosine. Thus, I refer these studies as methylome-wide association studies (MWAS) to distinguish them from studies of other epigenetic markers such as histone modifications.

MWAS emerges as a promising approach to searching molecular mediators for genetic and environmental factors, and unexplained risks for diseases. Current population level MWAS rely on high-density microarrays [10] or sequencing-based methods [11] following biochemical modifications or enrichments of genomic DNA [12]. Although MWAS only represent a small portion of epigenetic studies, several studies have successfully identified DNAm sites associated with disease traits [13-16]. Using hundreds of subjects, these MWAS have much smaller sample sizes comparing to a typical genome-wide association study (GWAS) of the same trait, which indicates rather large effects of identified DNAm sites [15]. Meanwhile, the first wave of MWAS aiming to discover associations between epigenomic variation and disease traits are limited by a number of issues such as imperfect technologies, tissue and cell-type specificity, access to biospecimens, sample size and analytical framework for data pre-processing and statistical modeling [17]. The field has rapidly evolved and equipped us with improved designs, methods and tools for MWAS. Several recent articles have reviewed the design, analysis and interpretation of EWAS [12, 18-20], especially in the context of epigenetic association study of human disease. In light of these well-covered reviews about EWAS of complex diseases and traits, here I focus on recent reports on environmental and genetic determinants of DNA methylome, and discuss their influences on MWAS. In the following sections, I only include recent reports using a methylome-wide approach among a minimum of 50 subjects due to power consideration.

Genetic Determinants of DNA Methylome

Genetics, along with environmental factors and stochastic process, are the primary sources of epigenetic variation [21]. Such genetic influences can be from the key genes maintaining the epigenetic profile (e.g. DNMT gene family), or from the neighboring genetic variants affecting the epigenetic state (e.g. modification of binding affinity of enzymes). DNA methylation quantitative trait loci (meQTLs) refer to genetic variations within genomic regions associated with a DNAm site. A genome-wide meQTL map can be obtained by correlating the GWAS data with DNA methylomic data from the same samples. Several studies have reported genome-wide meQTLs in peripheral blood, brain, lung, adipose tissue, lymphoblastoid cell line (LCL), as well as tumor tissues. I summarize thirteen published meQTL studies since 2010 in Table 1 [22-34]. All studies analyzed the cis-meQTLs (i.e. SNP and DNAm site collocate in the same genomic region), while a few also reported the trans-meQTLs. Because of the large number of SNP-DNAm pairs to test, trans-meQTL analyses are more computational intensive and require a more stringent multiple-testing correction. The definition of the cis-effect is arbitrary from study to study, ranging from 5 kb [27] to the whole chromosome [33] with majority (8 out of 13) between 100 kb – 1 Mb. The heterogeneous definition of cis-effect poses a challenge to directly compare the cis-meQTL maps between studies, since the non-overlapping sites can simply be a result of the inclusion or exclusion of certain SNP-DNAm pairs. I would recommend 1 Mb flanking region as a standard definition of cis-effect in meQTL analysis, being inclusive and not very computationally demanding.

Table 1.

Summary of published studies of meQTL in multiple human tissues and cell types

Study	Tissue/Cell type	N	Race	Sex	Mean Age	DNAm Platform	Region in cis	Transcrip tome
2010 Gibbs JR et al. [22]	cerebellum, frontal cortex, pons & temporal cortex	108, 133, 125, 127	EA	M+ F	46.2	27K	1 Mb	Y
2010 Zhang D et al. [23]	cerebellum	153	EA	M+ F	44.3	27K	1 Mb	N
2011 Bell et al. [24]	LCL	77	Afr.	M+ F	NA	27K	50 kb	Y
2013 Drong AW et al. [25]	Adipose tissue	38+18 1(F)	EA	M+ F	61.1	DMH+27K	500 kb
2013 Zhi D et al. [29]	CD4+ T-cell	593	EA	M+ F	48	450K	20kb	N
2013 Heyn H et al. [28]	LCL	288	EA AA Han.	M+ F	34.3	450K	1 Mb	Y
2013 Gutierrez- Arcelus M et al. [27]	fibroblasts, T- cells & LCL from cord blood	204	EA	M= F	neona te	450K	5 kb	Y
2013 Grundberg E et al. [26]	Adipose tissue, PBL	648, 200	EA	F	59	450K+BSS	100 kb	Y
2014 Zhang X et al. [34]	LCL	133	EA Afr.	M+ F	NA	450K	100 kb	Y
2014 Smith AK et al. [32]	Peripheral and cord blood, 4 postmortem brain regions [22]	90, 174, 111, 125, 105, 106	EA AA	M+ F	neona te, 43- 48	27K	50 kb	N
2014 Shi J et al. [31]	Normal lung tissue	210	EA	M+ F	65	450K	500 kb	Y*
2014 Heyn H et al. [30]	13 solid cancer types	3,649	EA	M+ F	NA	450K	1 Mb	N
2014 Teh AL et al. [33]	umbilical cord blood	237	Asia n	M+ F	neona te	450K	Same chr.	N

LCL: lymphoblastoid cell line; DMH: differential methylation hybridization; BSS: bisulfite sequencing; EA: European ancestry; Afr: African; AA: African American

^*: RNA-seq data are from The Cancer Genome Atlas (TCGA) study.

All studies used at least on type of array-based platforms to measure the DNA methylome, either the Illumina Infinium HumanMethylation27 (27K), or the more recent HumanMethylation 450 (450K) BeadChips (Illumina Inc., San Diego, CA) with better coverage of genomic regions. Interestingly, all studies excluded the DNAm sites located on the sex chromosomes, although thousands of DNAm sites were measured for their methylation status. Comparing to DNAm sites located on the autosomes, most X chromosome sites are hemimethylated in females, showing a bimodal distribution strongly associated with sex [24, 35]. The sex-specific DNAm sites on X chromosome are caused by X-chromosome inactivation (XCI), characterized by an X chromosome-wide methylation on the female genome [36]. A new analytical strategy specific to such bimodal distributed DNAm sites would fill the gap of the ignored X chromosome in meQTL study, and benefit the MWAS of human diseases.

Another common theme is that the majority of studies (11 out of 13) scanned for meQTL in samples of European ancestry (EA). In addition to two studies of HapMap LCLs [24, 34] and one study of Human Variation panel (HVP) LCLs [28], only two studies reported non-EA meQTL map in African Americans (AA) [32] and Asian populations [33] using peripheral and cord blood directly from individuals. Many genetic associations identified in GWAS of EA are not transferable to other racial groups due to their diverse genetic backgrounds. Similarly, the association between common SNPs and DNAm sites can be different across racial groups. Smith et al. highlighted the overlapping meQTLs between racial groups [32]. However, a large proportion of cis-meQTLs are race-specific, and the inter-race consistency of trans-meQTLs is unknown. Therefore, more studies in diverse racial groups are needed to address potentially distinct genetic influence on DNA methylome.

Using over 700,000 genome-wide SNPs and 22,928 autosomal DNAm sites from 460 unrelated AA individuals, I analyzed the genome-wide meQTL of peripheral blood leukocytes (PBLs). Adjusting for age, sex, cell type proportions and batch effect, I identified 16,320 pairs of significant meQTLs adjusting for multiple testing (Bonferroni corrected alpha level of 0.05). These meQTLs are summarized in Figure 1, ordered by the chromosomal position of SNPs and CpG sties. Among 16,320 meQTLs representing 6,081 unique SNPs and 2,991 unique CpG sites, 32% are cis-meQTLs (within 1 Mb flanking region) spreading across the entire genome (points along the diagonal line in Figure 1). Consistent with meQTL studies of other tissues and cell types [22, 24], the genetic contributions to the methylome observed in this AA sample are ubiquitous, both locally (in cis) and from long distance (in trans). A number of SNPs are associated with multiple DNAm sites, which imply the existence of master genetic regulators for epigenetic markers.

Figure 1.

A genomic map of meQTLs in human peripheral blood lymphocytes.

These reported meQTL studies cover both young and old adults, as well as neonates. However, none of the studies involve pediatric samples between newborn and early teens. DNA methylation profile changes dramatically during this critical development stage [37], which is important to understand the genetic effect on methylome and chronic diseases. More importantly, the early-life exposures may interact with genetic factors to have a synergistic impact on the methylome. Thus, future meQTL studies need to focus on the pediatric age group to complete our understanding of genome-methylome correlation across the entire life-span.

LCLs have been developed to provide the epidemiological study with an ‘unlimited’ supply of DNA for genetic studies. These immortalized cell lines can be easily obtained to investigate the molecular system involving transcriptome, proteome and epigenome. LCLs from HapMap [24, 34] and HVP [28] were used to map meQTLs. Although the overall correlation of DNAm profile between LCL and PBLs was high, the transformation process may alter the methylation status of a large number of DNAm sites and increase the inter-individual variation [35]. Therefore, PBLs better reflect the natural DNAm profile of an individual, and are preferred in MWAS over LCLs. Due to tissue and cell type specificity of DNAm, the meQTLs can be substantially different from tissue to tissue [26, 32], cell type to cell type [27]. The functional impact of these tissue-specific meQTLs may be important to understand the pathophysiology of diseases in target tissues. To date, we do not have the meQTL maps of many targeted tissues and cell types from sizeable population samples. We need to establish a more complete panel of tissue-specific meQTL maps as a public available resource, to better understand the genetic determinants of DNA methylome, and its role in chronic diseases.

Utilities and Applications of meQTL

Overall, these studies catalogued a large number of meQTLs in multiple tissues and cell types across the human genome, and provide a rich resource not only to understand the genetic regulation of DNAm, but also to illustrate an important molecular mechanism mediating the interaction between genetic variants and environment for chronic diseases. Several recent studies have demonstrated the utilities of meQTL data.

First, as a potentially functional feature on the genome, DNAm site may mediate the genetic association between SNPs and disease traits. meQTLs have been used to link the genetic associations to disease traits from recent GWAS. Significant GWAS loci are enriched for meQTLs. Over 300 unique GWAS SNPs covering 34% of reported diseases/traits are meQTLs in LCLs [34]. Among the top susceptibility variants of bipolar disorder, meQTLs are enriched in the cerebellum but not in lymphocytes [38]. Liu et al. reported that the rheumatoid arthritis-associated genetic variants may function through the epigenetic mechanism as a potential molecular mediator for gene-environment interaction [13]. Shi et al. reported that four out of the five established lung cancer loci in European ancestry are meQTLs in lung tissue. In aggregate, cis-meQTLs in lung tissue are enriched for lung cancer risk [31].

Secondly, meQTL can be used as the instrumental variable in Mendelian Randomization (MR) study of DNAm markers [3, 39]. MR was initially proposed as an epidemiologic method to obtain unbiased estimates of the putative casual effects without conducting a randomized trial [40, 41]. The MR approach uses the genetic variant mimicking the biological effects of a modifiable exposure. If the exposure truly alters the disease risk, the genetic variant should also be associated with the disease through the causal pathway. Because the genetic variant is randomly assigned to the offspring during meiosis in a population, the genotype distribution should not be biased by confounding. Only the genetic variant in the causal pathway should be associated disease outcome by carrying the association through the causal exposure. MR assumes that the genetic variants are independent of the confounders; the genetic variants are reliably associated with the exposure; and there is no direct effect of the genetic variants on disease. In epigenetic epidemiologic study, MR approach can be applied to study 1) the causal environmental factor for epigenetic profile, and 2) the causal epigenetic risk factor for human disease, as detailed in recent publications [3, 39].

Environmental Modifiers of DNA Methylome

Environmental factors are also important epigenetic modifiers [2, 20]. The epigenetic variation may mediate the environmental risks for human diseases during the entire life-span, from early embryogenesis, in utero development, childhood to adulthood. Although the environment-induced epigenetic change may vary at different development stages, the cumulative effects can eventually lead to chronic disease in later life. While facing challenges in exposure measurement, epityping technology, availability of biospecimen, study design and analytical methods, genome-wide environmental epigenetics studies also hold the promises to discover epigenetic dysregulations mediating the life-course exposures [20]. Heijmans et al. first showed that early-life environment can cause DNAm changes in humans persisting over 60 years after the Dutch Hunger Winter [42]. Individuals who were prenatally exposed to famine during had less DNA methylation of IGF2 gene compared with their unexposed, same-sex siblings adjusted for age and relatedness [42]. A number of environmental factors, such as socioeconomic status [43], early life environment [44], traumatic experience [45], pollutants[46], nutrition[47] and physical activity[48, 49], have been associated with the DNAm profile. These preliminary findings require further replication studies with larger sample sizes and consistent phenotyping. To date, cigarette smoking is the most convincing environmental modifier of DNA methylome. The strong epigenetic associations with smoking behavior have been consistently demonstrated in several comprehensive MWAS in different populations. After the first MWAS of cigarette smoking published in 2011 [50], 11 more MWAS have been reported in sizable population samples. In Table 2, I summarize a total of ten MWAS of smoking in adults [50-59], and two MWAS of maternal smoking effect on offspring [60, 61].

Table 2.

Summary of published methylome-wide association studies of cigarette smoking.

MWAS Study	Source of DNA	Platform	Sample Size	Race	Significant smoking-related Loci
Breitling et al. 2011 [50]	PB	27K	177+316	EA	F2RL3
Wan 2012 et al. [51]	PB	27K	1,085+369	EA	F2RL3, GPR15, LRRN3, LIM2
Monick et al. 2012 [52]	LCL+ LAM	450K	119+19	EA	AHRR
Sun 2013 et al. [54]	PB	27K+450K	972+239	AA	F2RL3, GPR15, AHRR, 2q37.1 et al.
Shenker 2013 et al. [53]	PB	450K	374+180	EA	AHRR, 2q37.1, 6p21.33, F2RL3 et al.
Zeilinger 2013 et al. [55]	PB	450K	1,793+479	EA	AHRR, ALPPL2, F2RL3 & many more
Dogan 2014 et al. [56]	PB	450K	111	AA	AHRR, GPR15
Elliott 2014 et al. [57]	PB	450K	192	SA+EA	AHRR, 2q37.1, F2RL3 et al.
Harlid 2014 et al. [58]	Whole blood	450K	908+200	EA	7 loci replicated + 2 new loci
Besingi 2014 et al. [59]*	PB	450K	432	EA	AHRR, 2q37.1 et al.; none for snuffing
Joubert 2012 et al. [60] **	Cord blood	450K	1,062+36	EA	AHRR, CYP1A1, GFI1 et al.
Markunas 2014 et al. [61] **	Whole blood	450K	889	EA	7 loci replicated + 10 new loci

EA: European ancestry; AA: African American; SA: South Asian; PB: peripheral blood; LAM: lung alveolar macrophage;

^*: tobacco smoking vs. tobacco snuffing (smoking-less).

^**:maternal smoking exposure.

Cigarette smoking-related DNA methylation

Cigarette smoking is an environmental risk factor for many chronic diseases including CVD and cancer, the deadliest diseases in the US. Smoking can induce cellular and molecular changes, including epigenetic modification, but the short-term and long-term epigenetic modifications caused by cigarette smoking at the gene level have not been well understood. Recent MWAS studies have identified and replicated smoking-related DNAm sites in samples of European ancestry [50, 51, 53]. The most significant smoking-related DNAm sites are hypomethylated among smokers [53, 54, 58]. Several studies also reported an inverse association between pack-years and DNA methylation, and a positive association between time since quitting smoking and DNA methylation [51, 55, 58, 62]. With hundreds of smokers and non-smoking controls, over a dozen of differentially methylated loci have been discovered and replicated in at least two studies. The most replicable smoking-related DNAm loci include F2RL3 (factor II receptor-like 3), AHRR (aryl hydrocarbon receptor repressor), GPR15 (G-protein-coupled receptor 15), 2q37.1, LRRN3 (leucine rich repeat neuronal 3), AKT3 (v-akt murine thymoma viral oncogene homolog 3), LIM2 (lens intrinsic membrane protein 2, 19kDa), NCAPD3 (non-SMC condensin II complex, subunit D3) and CNTNAP2 (contactin associated protein-like 2). These smoking-related DNAm loci are often replicable across racial groups [54]. So far, we have limited knowledge of how these genes and their products relate to smoking and physiological function. F2RL3 plays a role in platelet activation and cell signaling, which may mediate the risk for cardiovascular disease. Aryl hydrocarbon receptor triggers expression of a diverse set of genes, some of which are involved in metabolism of endogenous toxins from cigarette smoke [63, 64].

Joubert et al. conducted an MWAS of maternal smoking and identified 10 loci differently methylated in umbilical cord blood. Interestingly, these loci include reported sites associated with adult smoking (e.g. AHRR, CYP1A1 and CNTNAP2) and sites uniquely associated with maternal smoking exposure (e.g. GFI1) [60]. The notable difference in smoking-related DNAm between newborns and adults may indicate distinct effects of direct versus indirect smoking exposure, or of different development stages. Markunas et al. replicated 7 previously reported DNAm loci, and identified 10 new regions using 287 mothers smoking during pregnancy and their newborn infants [61]. The DNAm variants in newborns may mediate the effect of in utero smoking exposure on health outcomes in later life. A recent MWAS compares the DNAm changes associated with tobacco smoking to snuffing. Contrary to tobacco smoking, the smoke-less tobacco use is not significantly associated with any DNAm site, neither with any enrichment of biological functions nor molecular processes [59]. This interesting observation suggests that the smoking-related DNAm variations are most likely caused by the burnt products of tobacco, but not by its natural components.

Utilities and applications of environment-associated DNAm marker

The environment-associated DNAm sites may serve as novel biomarkers for both short-term and long-term exposures. In the case of cigarette smoking, DNAm sites located in F2RL3 and AHRR not only predict current smoker as cotinine does, but also distinguish former-smokers vs. never-smokers [65, 66]. The DNAm levels of F2RL3 sites are associated with the cumulative dose of smoking, as well the time since quitting [65]. Therefore, these DNAm sites are potentially better predictors representing the long-term risk of smoking than the often-flawed self-reported smoking data. Such epigenetic biomarkers for long-term exposures may exist for other environmental risk factors, where the longitudinal profile of the exposure is challenging to measure. For these methylation biomarkers, we can assess if they are more strongly associated with disease traits than the traditional risk factors in epidemiologic studies. More importantly, we may identify new epigenetic predictors for disease outcomes. Recently, two studies showed that the DNAm sites in F2RL3 predict secondary cardiovascular events among patients with stable coronary heart disease [67], and predict total mortality after 10-year follow up after adjusting for smoking status [62]. Based on single-marker predictors, we can develop a joint methylation risk score (MRS) for each risk factor with potentially stronger predictive ability. In light of recent MWAS findings of physical activity [48, 49], BMI [15] and blood lipids [16], we will be able to examine the predictive ability of the MRS and may improve the prognosis of chronic diseases. The epigenetic plasticity, in combination with the life-course exposure of disease risks, will offer a new window into a more comprehensive explanation for the development of chronic diseases and inter-individual variations.

Partition of Environmental and Genetic Influences on DNA Methylome

The genetic and environmental contribution to a disease is not fixed across the life-span. The proportion of genetic and environmental components of each DNAm site may vary in different studies due to the development stage, tissue type of the interest and life-course experience. For chronic diseases, the cumulative environmental effects may dominant the genetic effect in older adults. For age-related chronic diseases such as cardiovascular disease and chronic kidney disease, the epigenetic mechanism may be particularly important to explain different disease risks among people carrying the same genetic risk alleles. The classical twin design has been used to estimate the contributions of genetic and environmental effects to complex traits. The phenotypic variation in a trait can be decomposed to the genetic variance and environmental variance. These unobserved variance components can be quantitatively estimated from the observed phenotypic covariance in monozygotic (MZ) and dizygotic (DZ) twin pairs.

MZ twins carry identical genetic information from the primary sequence of DNA, and their epigenetic profiles diverge during aging [68]. Fraga et al. showed that MZ twins were epigenetically indistinguishable during the early years of life, and older MZ twins exhibited remarkable differences in DNAm and histone acetylation [68]. The increase of epigenetic differences between MZ twins in life-course strongly suggested the influence of unshared environment on the epigenome. Rates of discordant disease phenotypes in MZ twins are usually above 50% even for highly heritable diseases [69-71]. It has been suggested that epigenetics can make a significant contribution to phenotypic discordance in MZ twins [72, 73]. The epigenomes of discordant MZ twin pairs are, by definition, matched by genetic profile, age and sex, three important predictors of epigenetic profile. In addition, MZ twin design allows controlling for unmeasured confounders such as shared environmental influences. Therefore, using discordant MZ twin pairs is the most powerful design to study epigenomic variation associated with targeted diseases traits [74]. The discordant twin model is particularly useful in detecting moderate effects even with small samples of twin pairs. Several epigenetic studies of MZ discordant twins have investigated DNA methylation profiles in relation to several human diseases such as diabetes [75], psychiatric disorders [76-79], cancer [80] and autoimmune diseases [81-83].

In addition to the discordant twins design, we can also use MZ twin pairs to study the within-twin DNAm difference associated with quantitative traits, which is driven by unshared environment. We are able to estimate both within-twin-pair and between-pair effects in a single regression model [84]. Using 69 pairs of middle-aged MZ twins, we found that the within-twin effect of cg22891070 (a BMI-associated CpG site [15]) is 7.7×10⁻⁴ (p-value of 0.93) whereas the between-twin effect is 0.031 (p-value of 0.07). This observation hints that the epigenetic association with BMI on the HIF3A loci is not likely driven by the unshared environment.

Future Opportunities in DNA Methylome Research

The modifiability of epigenetics provides an important molecular mechanism responding to the external and internal environment, but also poses a great challenge in understanding the causality underlying the identified epigenetic association. Many environmental factors can potentially modify the DNAm across the life-span, and many are variable over time. Thus, the long-term profiles of DNAm have to be measured and studied to understand their relationship to environment and disease development. In the case of pharmacoepigenetic research, the DNAm profiles have to be captured prior and after the exposure to establish the causal effect of a given treatment. To improve the accuracy and efficiency of recruiting participants and collecting biospecimens, Sun and Davis developed a novel strategy for pharmacoepigenetic research taking advantage of the real-time data of an electronic medical record system [85]. The longitudinal studies of DNA methylome and other epigenomic features will be critical to establish the contribution to human diseases in the life-span.

The integrative genomic approach involving transcriptomic and GWAS data has greatly improved our understanding of DNA methylome. However, the technology for a comprehensive profiling of environmental factors is not available. Exposome is a new concept referring to the totality of human environmental exposures. Although currently we are not able to measure or model the exposome, the improved metabolomic technology is a promising method to measure thousands of metabolites representing both external and internal environment [86, 87]. A joint metabolome-DNA methylome study surveyed the epigenetic association with 649 blood metabolic traits [88]. The identified associations are driven by an underlying genetic effect, or by environmental and life-style-dependent factors without any underlying genetic signals. These findings extend the role of DNAm in regulating metabolism. Regarding to the epityping, both 27K and 450K array, as well as BSS require bisulfite conversion of genomic DNA in order to distinguish methylated from unmethylated cytosines. This chemical conversion does not distinguish 5-methylcytosine (5mC) from 5-hydroxymethylcytosine (5hmC) [89], another type of cytosine modification with potentially different function in cellular processes [90]. Future sequencing-based MWAS can distinguish 5hmC from 5mC, to discovery 5hmC specific loci associated with disease traits.

Conclusions

Epidemiologic and human genetic studies have demonstrated that complex diseases are caused by both genetic and environmental factors. Epigenome including DNA methylome links the environmental exposures and genetic effects underlying the development of human diseases. Thus, study of DNA methylome may lead to a new approach to examining gene-environment interaction, and to identify unexplained risks for complex diseases beyond known environmental and genetic risks. Our knowledge of human DNA methylome has advanced dramatically in recent years. Using high-throughput technologies such as microarray and next-generation sequencing, large sample size of human population can be achieved to unveil potential impact of DNA methylome on human diseases. Recent MWAS involving hundreds to thousands of subjects have successfully identified epigenetic associations with human diseases, environmental and genetic factors. However, sheer increase of sample size is not sufficient to address the challenges and limitations facing MWAS. To fully recognize the role of DNA methylome in complex diseases and to eventually establish a new epigenetic approach to preventing and treating diseases, we have to carefully consider the characteristics of samples, study design and key research questions for each MWAS. Therefore, epidemiology methods and designs will play a very important role in the era of epigenomic epidemiology and MWAS. We will need more well designed MWAS and follow-up studies to continuously unravel the complexity between human epigenome and diseases, and to further understand the epigenetic mechanisms of complex diseases, and to examine the utilities of the DNAm markers.