AMERICAN JOURNAL OF HUMAN BIOLOGY 27:295–303 (2015)

Human Biology Toolkit

Epigenetics for Anthropologists: An Introduction to Methods AMY L. NON1* AND ZANETA M. THAYER2 Department of Anthropology, Vanderbilt University, Nashville, Tennessee 37235-7703 2 Department of Anthropology, University of Colorado Denver, Denver, Colorado 80217-3364

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ABSTRACT: The study of epigenetics, or chemical modifications to the genome that may alter gene expression, is a growing area of interest for social scientists. Anthropologists and human biologists are interested in epigenetics specifically, as it provides a potential link between the environment and the genome, as well as a new layer of complexity for the study of human biological variation. In pace with the rapid increase in interest in epigenetic research, the range of methods has greatly expanded over the past decade. The primary objective of this article is to provide an overview of the current methods for assaying DNA methylation, the most commonly studied epigenetic modification. We will address considerations for all steps required to plan and conduct an analysis of DNA methylation, from appropriate sample collection, to the most commonly used methods for laboratory analyses of locus-specific and genome-wide approaches, and recommendations for statistical analyses. Key challenges in the study of DNA methylation are also discussed, including tissue specificity, the stability of measures, timing of sample collection, statistical considerations, batch effects, and challenges related to analysis and interpretation of data. Our hope is that this review serves as a primer for anthropologists and human biologists interested in incorporating epigenetic data into their research C 2015 Wiley Periodicals, Inc. V programs. Am. J. Hum. Biol. 27:295–303, 2015. INTRODUCTION Epigenetics, as first described by Conrad Waddington, was a concept used to explain the dynamic way by which genetic variation interacts with environmental exposures across development to produce a phenotype (Jablonka and Lamb, 2002). In molecular biology research today, epigenetics is understood as mitotically and/or meiotically heritable changes in gene expression (or gene expression potential) that are not caused by changes in underlying DNA sequence (Jaenisch and Bird, 2003; Russo et al., 1996). While an individual’s genome is generally stable across the life course, an individual’s epigenome can change dynamically in response to environmental experience. Notably, epigenetic patterns are sensitive to environmental exposures such as nutrition, toxicants, and psychosocial stress. Not surprisingly, many biological anthropologists and human biologists have begun to develop an interest in investigating epigenetics as a mechanism linking environmental experiences with health outcomes (Kuzawa and Sweet, 2009; Non et al., 2014; Rodney and Mulligan, 2014; Thayer and Kuzawa, 2011). Because epigenetic markers modify gene expression and are responsive to environmental signals, they represent a much-needed bridge between nature and nurture, which have been falsely divided within the social and biological sciences (Meaney, 2010). Epigenetic changes may serve as a mechanism of developmental plasticity, in which the developing offspring’s biology may adjust in response to environmental stimuli, such as malnutrition or psychosocial stress (Hochberg et al., 2011). The potential heritability of these marks across generations also suggests that they may be an important mechanism of evolutionary change. In particular, epigenetic modifications may provide a biological “memory” of experiences within or across generations that allows organisms to adapt their biology in response to rapid environmental changes. In this way, epigenetics may represent a mechanism of short-term adaptation that can respond to selective pressures that are too short-lived for the slower process of natural selection (Kuzawa and Thayer, 2011). C 2015 Wiley Periodicals, Inc. V

For these reasons, epigenetic modifications may also be a useful marker of prior environmental exposures that cannot otherwise be assessed (Bakulski and Fallin, 2014). For example, prenatal exposures, such as smoking (Markunas et al., 2014), and ancestral exposure to environmental toxins found in pesticides (Manikkam et al., 2014), have been found to modify epigenetic marks in offspring of humans and rats, respectively. If more difficult to assess environmental exposures are also associated with persistent epigenetic changes, it may be possible to infer prior exposure based on patterns of epigenetic marks. Given the potential importance of epigenetic methods for anthropological and other human biology research, the focus of this article will be to provide an overview of approaches for epigenetic studies. Although there are many types of epigenetic mechanisms, by far the most commonly researched type is methylation. Methylation refers to the attachment of a methyl group to a CpG site [cytosine and guanine base pair (bp) connected via phosphate] or the attachment of a methyl group to a histone tail; however, the former, known as DNA methylation, is more frequently researched, as it is a more stable modification (Cedar and Bergman, 2009). We will, therefore, focus on how to assess changes in DNA methylation at CpG sites. We will also address considerations for appropriate sample collection, laboratory analyses, and statistical analyses. METHODOLOGY FOR ANALYZING METHYLATION Sample collection Although genomes are consistent across cells, epigenetic marks vary dramatically across cell and tissue types. In fact, epigenetic differences across cells are at *Correspondence to: Amy Non, Department of Anthropology, VU Station B #356050, 2301 Vanderbilt Place, Nashville, TN 37235-7703, USA. E-mail: [email protected] Received 8 September 2014; Revision received 22 November 2014; Accepted 20 December 2014 DOI: 10.1002/ajhb.22679 Published online 24 February 2015 in Wiley Online Library (wileyonlinelibrary.com).

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enough time to get sufficient DNA, but be careful not to brush too forcefully, which can cause bleeding and contaminate the sample. Using more than one swab is recommended in order to make up for relatively low yield with each individual swab. DNA collected in buccal swabs is stable for days at room temperature or even months or years with the application of tablets or a buffer (e.g., Isohelix Dri-Capsules or Isohelix DNA stabilization and lysis kit). Following collection, DNA can be extracted from samples using standard protocols. Assay methodologies

Fig. 1. Comparison of accessible sample options. Abbreviations: CVD, cardiovascular disease; DMR, differentially methylated regions; WBC, white blood cells.

least partly responsible for differentiating cells with the same genome into different tissue types (Ohgane et al., 2008). As a first step, it is therefore necessary to determine which type(s) of tissue sample(s) to collect. For anthropologists and other human biologists interested in minimally invasive protocols, three commonly used tissue samples are buccal, saliva, and blood. Figure 1 provides a brief summary of the pros and cons of working with each of these sample types. Multiple methods for collecting buccal cells (Mulot et al., 2005) and saliva samples (Matthews et al., 2013) are available. Blood samples are typically collected with EDTA tubes (EDTA is an anticoagulant that keeps blood samples from clotting). DNA can also be extracted from dried blood spots, which are routinely collected from neonates at birth, and often stored for decades in biobanks. Although less DNA can be obtained from dried blood spots relative to fresh blood, a recent study demonstrated that reliable genome-wide methylation data can be obtained from 3-year-old dried blood spots, opening new possibilities for population-level epigenetic studies of neonates (Hollegaard et al., 2013). In addition to the standard tissue types, placenta is also an interesting target tissue, as it is easily accessed after birth, and is located at the interface of maternal and fetal environments (Schroeder et al., 2013). Other minimally invasive tissues of potential interest include epithelial cells via tapestripping (Pailler-Mattei et al., 2011), and corneal cells via irrigation of the corneal surface (Peterson et al., 2011). Notably, most minimally invasive protocols have utilized buccal cells collected using swabs. This collection protocol is preferred due to the relative homogeneity of cell types in buccal samples, which tend to include mostly epithelial cells, and smaller amounts of white blood cells. Homogeneity of cell types is an important concern in epigenetic study design, as DNA methylation levels vary across cell types (Zhang et al., 2013), and thus an association between DNA methylation and a phenotype can be lost if levels are averaged across cells with varying methylation. Additionally, recent evidence has shown that patterns of methylation in buccal cells are more similar to other tissues, such as liver, kidney, brain, and sperm, compared with blood cells (Lowe et al., 2013). Researchers utilizing buccal cells should take care to swab cheeks for American Journal of Human Biology

A wide selection of technologies for analysis of DNA methylation has been developed over the last decade. Below, we present a brief overview of the most commonly used approaches along with key advantages and disadvantages of each method, which are summarized in Figure 2. After deciding what tissue(s) to analyze, the next key decision in designing a DNA methylation study is to choose between a targeted locus-specific approach, an exploratory genome-wide approach, and a global methylation approach. A locus-specific approach is an analysis that targets a small genic or intergenic region. A genomewide approach is defined as analyses of thousands to millions of individual CpG sites distributed throughout the genome. A global methylation approach quantifies total methylated cytosines in a DNA sample, but provides no information on methylation levels at any individual locus, and thus is less informative than the other approaches. Given its shortfalls relative to the locus-specific and genome-wide approaches, global methylation is not considered further in this manuscript. For greater detail on those methods, see Karimi et al. (2011), Ramsahoye (2002), Weisenberger et al. (2005), and Yang et al. (2004). Similar to considerations in studies of genetic variants, factors that should be considered in choosing between approaches include amount of DNA available, presence or lack of a priori hypotheses regarding biological pathways of interest, and cost. In brief, advantages of the locusspecific approach are that it requires less input DNA, lower cost, and less concern over Type I error (false positives) as fewer loci are tested. This is the best approach to use when the researcher is interested in investigating a specific biological pathway. The primary disadvantage of a locus-specific approach is that it is limited to previously studied gene regions. Conversely, the genome-wide approach offers advantages of novel discovery of differentially methylated regions (DMRs), and allows for detection of genome-wide effects, without assuming that changes may be seen only at a specific locus, but carries its own challenges of larger volume of input DNA (500 ng), higher cost, large datasets, and complex statistical methods to account for multiple testing. Additionally, with genome-wide data, one can assess methylation of a single gene region, such as a promoter or enhancer, and the same data could be used to test for genome-wide mean methylation levels for association with a phenotype (Reinius et al., 2012). If more than 30–50 different loci/individual are to be assayed, it becomes more cost-effective to utilize a microarray, though existing microarrays may not always capture the exact CpG sites of interest. Given the pros and cons of different methodological options, a promising approach is to use a genome-wide scan to identify loci of interest, which can then be verified using locusspecific methods. Verification entails replication of DNA

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Fig. 2. Benefits and limitations of methods discussed in the current review. High cost  $1,000/sample; Moderate cost a few $100/sample; Low cost 5 28 million autosomal CpG sites in the human genome, many of which are not located in regions of known functional relevance. In comparing the ability of the array to detect DMRs with other sequencing-based methods, the Infinium assay detected only 20% of those detected by MeDIP or RRBS (Bock et al., 2010). In addition, the array requires relatively large amounts of input DNA (500 ng). RRBS is a promising newer method that provides greater coverage of CpG sites in the genome compared with the Infinium array, with lower requirements for input DNA [at least 200 ng recommended for good reproducibility, but as low as 10–30 ng of high quality nondegraded DNA (Bock et al., 2012; Gu et al., 2011; Meissner et al., 2005)]. RRBS enriches CG-rich regions by isolating small restriction fragments generated by MspI, a methylationinsensitive endonuclease. The DNA is then bisulfite converted, amplified, and sequenced. Because this method enriches for CpG dense regions of the genome, it requires fewer sequencing reads, but also leaves out the less CpG dense regions of the genome, where DNA methylation may be more variable and, therefore, interesting to study (Mill and Heijmans, 2013). It can also be very expensive, American Journal of Human Biology

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although prices can be brought lower at a discounted core facility if more samples are used per lane (more samples per lane trades-off for higher coverage of each sample). The method also requires a labor-intensive protocol with complex statistical analyses, and can be complicated by DNA degradation, incomplete conversion, and poor efficiency of PCR amplification (Meissner et al., 2005). The genome-wide method utilizing MeDIP is less commonly used today relative to the Illumina microarrays or RRBS. MeDIP is an immunoprecipitation method that uses an antibody against 5-methylcytosine to enrich for methylated DNA sequences (Weber et al., 2005). The purified enriched methylated DNA can then be used with high-throughput sequencing (MeDIP-seq) or with microarrays (MeDIP-chip). MeDIP has reduced in popularity, as the enrichment protocol can be susceptible to many sources of bias, including room temperature, humidity, and operator influences, and the results require complex bioinformatics methods for normalization and removal of batch effects (Michels et al., 2013). The most comprehensive method for analyzing methylation across the human genome is WGBS (Cokus et al., 2008). This method, also known as MethylC-seq or BSseq, assays DNA methylation across the entire methylome at single-base pair resolution, via shotgun sequencing of bisulfite converted DNA. A sequencing depth of 30x coverage is generally recommended, which is very expensive and a relatively inefficient approach, as up to half the reads do not cover CpG sites (Stevens et al., 2013). WGBS also generally requires a large amount of input DNA, although newer techniques, such as the Epicentre’s new EpiGenome Methyl-Seq kit, requests only 50 ng of genomic DNA. While WGBS is the only available method to measure DNA methylation at every site in the genome, its performance can be biased by efficiency of amplification of methylated versus unmethylated DNA (Stevens et al., 2013), and by incomplete bisulfite conversion (like all methods that utilize bisulfite conversion protocols). Complex bioinformatics techniques are also required to accurately align bisulfite-converted sequencing reads (Krueger et al., 2012). Challenges and considerations There are many challenges to consider when analyzing DNA methylation. Below, we briefly review several such challenges and provide some suggestions for addressing them in future analyses. 1. Tissue specificity. Tissue specificity may be one of the greatest challenges in analyses of DNA methylation. Many studies have demonstrated that the degree of DNA methylation at a particular locus depends on the tissue under consideration (Davies et al., 2012; De Bustos et al., 2009; Lowe et al., 2013). Tissues are differentially methylated because cellular differentiation is mediated and maintained, in part through changes in methylation (Ohgane et al., 2008). In fact, differences in methylation between tissues in a single individual can greatly exceed the amount of variation between individuals (Davies et al., 2012); for example, in one study interindividual variation explained 6.4% of the variance in DNA methylation while intertissue variation explained 51.2% (Lokk et al., 2014). The degree of correlation between tissues varies not only by tissue type but also by the placement and density of particular CpGs in the region of analysis. As methylation American Journal of Human Biology

varies in complex ways across tissues, tissue specificity must be accounted for when comparing across studies and inferring functional significance of methylation changes. It is also important to note that most tissue samples are composed of heterogeneous cell types. Except in cases where particular cell lines are enriched or isolated, methylation readouts reflect an average across diverse cell types present within a particular tissue sample (Reinius et al., 2012). Unfortunately, cell sorting to isolate individual cell types can be cost prohibitive, and requires fresh samples that are often unfeasible to obtain. Heterogeneity of cell types in a tissue can confound statistical analysis when there is a difference in cellular composition between cases and controls (Bock, 2012). This may occur if, for example, all cases carry a disease that elevates white blood cell counts. There are a number of bioinformatic methods that can be used to control for variation across different cell types in blood samples, even in the absence of isolating cell types (Houseman et al., 2012, 2014; Zou et al., 2014). However, the accuracy of these methods is yet to be fully validated in direct comparison with cell sorting, and it may not be appropriate to use these methods if the environmental exposures or health of the reference samples differ from those of the population under study. This point is of particular note for anthropologists who may be working with populations coming from ecological and social environments that vary substantially from Western samples upon which these methods are based.

2. Stability of Epigenetic Marks and Timing of Sample Collection. The stability of DNA methylation patterns over time is still unknown. Although it is clear that DNA methylation marks are lost and re-established early in embryonic development, it is not clear how stable they remain throughout the life course. Recent findings suggest that DNA methylation marks are dynamic across time. For example, a recent study found that methylation was associated with age in 28% of CpG sites (Xu and Taylor, 2014). The nature of this association may vary depending on the location of the CpG (Christensen et al., 2009). As a result of the dynamic nature of methylation, epigenetic studies must carefully consider when samples were collected in relation to the exposures and outcomes of interest. Importantly, if samples are collected at the same time as phenotypes are assessed, it is inappropriate to infer causality of epigenetic changes, as the direction of the association is unknown. Therefore, prospective designs and experimental studies are preferred for establishing causal relationships. Although no published studies have demonstrated a diurnal rhythm in DNA methylation patterns as has been found with histone acetylation (Etchegaray et al., 2003), one study evaluating the effects of particulate matter exposure in the work place found an acute increase in methylation in the iNOS gene following welding activities (Kile et al., 2013). Another study evaluating the impacts of the Trier Social Stress Test found changes in DNA methylation in the oxytocin receptor gene (OXTR) in blood cells 10 and 90 min post-test (Unternaehrer et al., 2012). These findings suggest that methylation may be more sensitive to acute exposures than previously considered, and that to the extent possible, such factors should be accounted for in study design and statistical analysis.

3. Batch effects. DNA methylation analyses are prone to batch effects; that is, confounding as a result of laboratory

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conditions, position on a plate, or experiment time (Wilhelm-Benartzi et al., 2013). The best way to reduce batch effects in an EWAS is to distribute samples randomly across chips. Duplicates (ideally at least 10% of the sample) along with control samples of known methylation levels should be included in order to calculate coefficients of variation across batches (Michels et al., 2013). Validation of findings is also recommended through use of an independent set of samples assayed with a different technique to avoid any bias from the original technique or sample set (Michels et al., 2013). Several statistical programs have been developed to aid in correcting for batch effects (Jaffe et al., 2012; Wilhelm-Benartzi et al., 2013). 4. Statistical Considerations. The continuous and finite nature of DNA methylation data results in statistical properties different from genotype or gene expression data that must be considered during analysis. For example, DNA methylation data are Beta distributed, which means the variance is influenced by the mean. Therefore, when modeling a normal regression, one should consider using variance stabilizing transformations so as not to violate the assumption of constant variance (Laird, 2010). Additionally, DNA methylation marks are often nonnormally distributed. These properties imply that ordinary least squares regression is not always the most appropriate method of analyses. Instead substitutes, such as Beta regression, should be considered. Additionally, with microarrays, many probes are nonspecific, such that they bind to more than one location in the genome, and thus must be removed from analyses (Chen et al., 2011). A more thorough discussion of challenges in statistical analysis as well as a list of bioinformatics resources is available in Laird, 2010; Siegmund, 2011. For microarray analyses, a number of useful pipelines have been published (Morris et al., 2014), and a few Bioconductor packages in R are available for aid with normalization and analysis of microarray data (Du et al., 2008; Pidsley et al., 2013). It is important to note that power analyses for EWAS are more complex relative to those used for GWAS, given that there are so little data to draw upon regarding frequency of DNA methylation variants and effect sizes with diseases. Although methods for estimating power are still under development, a simulation-based power estimate has been demonstrated under a few different case control scenarios (Rakyan et al., 2011). It is clear that very large sample sizes (up to 800 cases and controls) may be needed to provide 80% power to detect relatively small effects (e.g. Odds Ratios 1.25), but power estimates vary greatly by the estimated methylation frequency spectrum across cases and controls. Note this estimate is much smaller than the standard sample size recommended for GWAS of 2,000 cases and controls, but estimates depend greatly on region of the genome assayed; that is, frequency spectrums differ across regions of high versus low CG density (Rakyan et al., 2011). One additional statistical consideration is that demographic and environmental factors such as age, race, socioeconomic status, and health behaviors, could serve as potential confounders, because these factors can be associated with both the epigenetic marker and the phenotype (unlike genetic studies, where genotype is not affected by the environment) (Rakyan et al., 2011). These factors

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should be measured and adjusted for in regression analyses to avoid spurious associations, or overestimation of effect sizes. 5. Analysis and interpretation—what do small differences mean? Oftentimes, studies will report a statistically significant difference of as little as a few percent change in methylation. Interpretation of these small changes should be made with caution, especially considering the errors inherent in many of the measurement techniques (Mill and Heijmans, 2013). It is difficult to find evidence regarding what a biologically significant difference in DNA methylation would be. Presumably, the difference should have a measurable effect on expression, though this is complicated by the fact that DNA methylation changes at one time period can affect gene expression at later time periods in life, and that DNA methylation at one gene may influence expression of distant genes, which can be very difficult to detect (Aran et al., 2013). Further, DNA methylation may have other functional effects besides direct effects on expression, such as controlling production of alternative transcripts (Maunakea et al., 2010). In some cases, it may be preferable to analyze regional changes in DNA methylation, rather than individual sites, to increase the chance of identifying functionally relevant results. Regional analyses can be performed by averaging DNA methylation across functional elements (e.g., CpG islands, shelves, or shores) or sliding window blocks of 1 kb, that is, bump hunting (Jaffe et al., 2012). Researchers have also utilized a factor analysis approach across a range of CpGs to explore variation in methylation in relation to a particular exposure (Mulligan et al., 2012). However, too narrow a focus on regional effects could miss detection of individual CpG sites that could be functionally relevant if located in key regulatory regions, such as a transcription factor binding site. For an example of an analysis using both site by site and regional approaches, see Non et al. (2014). Until more functional genetics studies can determine the biological meaning of different methylation changes, statistically significant associations should ideally be followed up with RNA or protein expression analyses. Notably, these types of analyses require careful planning at the time of sample collection to properly preserve the sample, for example, storing blood in a buffer that inhibits RNase and freezing immediately. 6. Manage expectations. For many years, it was believed that genetics research would be the key to solving the greatest mysteries in human variation and disease. However, since the publication of the Human Genome Project, many questions remain unanswered. Epigenetics research is receiving a similar hype, and we must be careful to manage expectations (Bakulski and Fallin, 2014; Miller, 2010). As anthropologists, we are concerned with understanding natural human variation, and yet still know very little about the extent or importance of variation in DNA methylation within and among individuals or populations. Such an understanding is critical before we can appreciate the significance of changes in methylation associated with particular environmental exposures or phenotypes (Bock et al., 2008). Further, it is important to note that much excitement over transgenerational American Journal of Human Biology

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inheritance of epigenetic marks may be premature, given that no evidence of this phenomenon yet exists in humans (Aiken and Ozanne, 2014; Morgan and Whitelaw, 2008). Although this review has focused extensively on DNA methylation at CpG sites, it is important to remember that DNA methylation can occur outside of CpG contexts, and can occur as 5-hydroxymethylcytosine (5hmC or HmeC) as opposed to 5-methylcytosine (5meC). HmeC is an oxidized methylated cytosine which contains a hydroxy group, and it cannot be distinguished from 5meC by traditional bisulfite sequencing. HmeC is highly abundant in neuronal cells and represents a promising new area of research (Cheng et al., in press; Kriaucionis and Heintz, 2009). Importantly, there are also many other types of epigenetic mechanisms, the stability and sensitivity of which remain poorly understood (Bakulski and Fallin, 2014). It is likely that epigenetic modifications at many levels of regulation work together to influence the phenotype, and thus by only focusing on DNA methylation we can only understand a limited part of the overall picture. As methods improve and costs are reduced for measurement of microRNAs and methylation or acetylation of histones, anthropologists and other human biologists should begin to consider these other mechanisms in their analyses. One study that demonstrates a typical workflow, from 450 k Illumina array, through pyrovalidation and candidate gene analyses, is a study of maternal depression/anxiety on genome-wide DNA methylation patterns in cord blood (Non et al., 2014). This study demonstrates many of the methods described above, using some of the most recent recommendations for genome-wide analyses, as well as many of the challenges in interpreting epigenetic data in an observational study from a peripheral tissue. CONCLUSION Epigenetic data have tremendous potential to transform the field of anthropology by adding a new layer of complexity to our understanding of human biological variation, and a new way to explore human adaptation to changing environments. We hope this article has served as a primer to assist more investigators in incorporating epigenetic data into their research programs. ACKNOWLEDGMENTS The authors thank Rebecca Rancourt for her helpful feedback on the manuscript and Brittany Hollister for help creating the figures. The authors also benefitted from participation in the NSF/NIH/Research Councils UK funded Workshop on Social and Behavioral Epigenetics, July 29–30, 2014, Potomac, MD. The authors have no relevant conflicts of interest. LITERATURE CITED Aiken CE, Ozanne SE. 2014. Transgenerational developmental programming. Hum Reprod Update 20:63–75. Aran D, Sabato S, Hellman A. 2013. DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol 14:R21. Bakulski KM, Fallin MD. 2014. Epigenetic epidemiology: promises for public health research. Environ Mol Mutagen 55:171–183. Barault L, Rancourt RC. 2012. Laboratory methods in epigenetic epidemiology. In: Michels KB, editor. Epigenetic epidemiology. Dordrecht, New York: Springer Verlag. p 37256. Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, Gunderson KL. 2009. Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics 1:177–200.

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