An ecologist’s guide for studying DNA methylation variation in wild vertebrates

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ An ecologist’s guide for studying DNA methylation variation in wild vertebrates © 2022 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. Accepted version (Final draft) Laine, Veronika N.; Sepers, Bernice; Lindner, Melanie; Gawehns, Fleur; Ruuskanen, Suvi; Oers, Kees Laine, V. N., Sepers, B., Lindner, M., Gawehns, F., Ruuskanen, S., & Oers, K. (2023). An ecologist’s guide for studying DNA methylation variation in wild vertebrates. Molecular Ecology Resources, 23(7), 1488-1508. https://doi.org/10.1111/1755-0998.13624 2023 Mol Ecol Resour. 2022;00:1–21. | 1wileyonlinelibrary.com/journal/men 1 | INTRODUCTION The field of molecular ecology is constantly advancing with the development of new powerful technologies, methods and analysis software. Tools for sequencingbased data analyses are increasingly used in ecological and evolutionary research as costs decrease and analyses become more timeefficient (Aguirre et al., 2019; van Gurp et al., 2016; Solares et al., 2018). In particular, the interest for a role of epigenetics in ecology and evolution is rapidly growing (Bossdorf et al., 2008; Hawes et al., 2018; Kilvitis et al., 2014; LedónRettig et al., 2013; Schrey et al., 2013; Sepers et al., 2019; Verhoeven et al., 2016; Vogt, 2021). In ecological epigenetics studies, DNA methylation is the most widely studied epigenetic mechanism. DNA methylation involves the addition of a methyl group to a DNA nucleotide, usually a cytosine (C), and it affects the binding of proteins required for transcription initiation (Yin et al., 2017). This Received: 3 September 2021 | Revised: 29 March 2022 | Accepted: 13 April 2022 DOI: 10.1111/1755-0998.13624 FROM THE COVER An ecologist's guide for studying DNA methylation variation in wild vertebrates Veronika N. Laine1 | Bernice Sepers2,3 | Melanie Lindner2,4 | Fleur Gawehns2 | Suvi Ruuskanen5,6 | Kees van Oers2,3 1Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland 2Department of Animal Ecology, Netherlands Institute of Ecology (NIOOKNAW), Wageningen, The Netherlands 3Behavioural Ecology Group, Wageningen University & Research (WUR), Wageningen, The Netherlands 4Chronobiology Unit, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, The Netherlands 5Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland 6Department of Biology, University of Turku, Finland Correspondence Veronika N. Laine, Kees van Oers, Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland. Emails: [email protected]; K.vanOer[email protected].nl Funding information NWO open competition grant, Grant/ Award Number: ALWOP.314; Turku Collegium for Science and Medicine Handling Editor: Shawn Narum Abstract The field of molecular biology is advancing fast with new powerful technologies, sequencing methods and analysis software being developed constantly. Commonly used tools originally developed for research on humans and model species are now regularly used in ecological and evolutionary research. There is also a growing interest in the causes and consequences of epigenetic variation in natural populations. Studying ecological epigenetics is currently challenging, especially for vertebrate systems, because of the required technical expertise, complications with analyses and interpretation, and limitations in acquiring sufficiently high sample sizes. Importantly, neglecting the limitations of the experimental setup, technology and analyses may affect the reliability and reproducibility, and the extent to which unbiased conclusions can be drawn from these studies. Here, we provide a practical guide for researchers aiming to study DNA methylation variation in wild vertebrates. We review the technical aspects of epigenetic research, concentrating on DNA methylation using bisulfite sequencing, discuss the limitations and possible pitfalls, and how to overcome them through rigid and reproducible data analysis. This review provides a solid foundation for the proper design of epigenetic studies, a clear roadmap on the best practices for correct data analysis and a realistic view on the limitations for studying ecological epigenetics in vertebrates. This review will help researchers studying the ecological and evolutionary implications of epigenetic variation in wild populations. KEYWORDS bisulfite sequencing, DNA methylation, ecology, epigenetics, evolution This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd. 2 | LAINE Et AL. process usually represses gene expression (Bird, 2002; Goldberg et al., 2007; Moore et al., 2013), especially if methylated sites are located close to the transcription start site in the promoter region (Bird, 2002; Laine et al., 2016; Li et al., 2011; Moore et al., 2013), but the relationship between DNA methylation and gene expression is rather complex and hard to generalize as DNA methylation can also increase transcription (Korochkin, 2006). Because DNA methylation can affect gene expression, this epigenetic mechanism is generally accepted to mediate the expression of phenotypic traits (Law & Jacobsen, 2010). 1.1 | Mechanisms causing variation in DNA methylation and phenotypic consequences DNA methylation can be induced by genetic variation (Richards, 2006), by spontaneous epimutations (Becker et al., 2011) and by environmental induction (Pértille et al., 2017; Weaver et al., 2004; Zimmer et al., 2017; for more studies see below), although environmentally induced epigenetic variation might depend on genetic variation as well. Given the effects on transcription and subsequently gene expression, DNA methylation is, in theory, able to finetune phenotypic expression of ecologically relevant traits under environmental influence (Law & Jacobsen, 2010). This implies that epigenetic mechanisms provide an organism with the opportunity to develop a phenotype that is adaptive, in response to the environment (Jablonka & Lamb, 2007). These epigenetic changes may persist and affect the phenotype throughout an individual's lifetime (Roth et al., 2009; StCyr & McGowan, 2015). Thus, DNA methylation can be a mechanism underlying phenotypic plasticity, which can be defined as “the ability of a genotype to produce distinct phenotypes when exposed to different environments throughout its ontogeny” (Pigliucci, 2001, 2005). Ecologists strive to explain the diversity of ecologically important phenotypic traits and to understand how these traits are shaped by the environment. Hence, DNA methylation has become of great interest and, as a result, the number of studies on the causes and consequences of DNA methylation in natural populations is rising. Studies that aim to provide insights into the origin of variation in DNA methylation focus on a wide range of environmental influences, including pH (Massicotte & Angers, 2012), habitat quality (Hu et al., 2019), parasites (Hu et al., 2018; McNew et al., 2021; Wenzel & Piertney, 2014), and anthropogenic causes such as urbanization (Caizergues et al., 2021; Garcia et al., 2019; McNew et al., 2017; Riyahi et al., 2015; Watson et al., 2021) and contaminants (Laine et al., 2021; Mäkinen et al., 2021; McNew et al., 2021; Nilsen et al., 2016; Pierron et al., 2014; Romano et al., 2017). In addition, studies have now included DNA methylation changes as a possible mechanism causing phenotypic changes due to environmental experiences during early development, such as brood size (Sepers et al., 2021; Sheldon et al., 2018), diet or resource availability (Laubach et al., 2019; Lea et al., 2016; Weyrich et al., 2018), predation risk (Noguera & Velando, 2019) and parental effects (Bentz et al., 2016; Rubenstein et al., 2016; Weyrich et al., 2016). DNA methylation might thereby explain developmental phenotypic plasticity (Watson et al., 2019), referred to as irreversible phenotypic changes that are the result of environmental induction during development (Forsman, 2015). Other studies aim to provide insights into the link between epigenetic variation and ecologically relevant phenotypic variation. So far, studies have focused on exploratory behaviour (van Oers et al., 2020; Verhulst et al., 2016), noveltyseeking behaviour (Riyahi et al., 2015), salinity tolerance (Heckwolf et al., 2020), stress resilience (Taff et al., 2019), plumage characteristics (Soulsbury et al., 2018; Taff et al., 2019) and patterns of scutes (Caracappa et al., 2016). Another popular topic in ecological studies is how DNA methylation levels change over time due to, for example, ageing (Ito et al., 2018; PaoliIseppi et al., 2019; Parrott et al., 2014; Polanowski et al., 2014; Soulsbury et al., 2018; Thompson et al., 2017; Wilkinson et al., 2021), where methylation might accumulate over time or in the context of regulation of temporal plastic changes, such as regulation of timing of migration and reproduction (Baerwald et al., 2016; Lindner et al., 2021; Mäkinen et al., 2019; Saino et al., 2017; Viitaniemi et al., 2019) and hibernation (Alvarado et al., 2015). 1.2 | Role of epigenetic variation in adaptation to changing environments and evolution Studies have shown that environmentally induced epigenetics patterns can be transmitted stably to future generations with effects on offspring phenotypes (Anway et al., 2005; Franklin et al., 2010). Thus, epigenetic modifications harbour a source of nongenetic phenotypic variation that potentially can be a fast and heritable response to the environment. If epigenetically mediated phenotypic variation is truly heritable, natural selection might act on it and it might hold evolutionary potential. Evolutionary ecologists now study epigenetic mechanisms to provide insights into the potential role of epigenetic variation in adaptation to changing environments. To assess the evolutionary implications, DNA methylation has been studied in relation to heritability (Hu et al., 2021; van Oers et al., 2020), whether it might be under selection (Laine et al., 2016; Skinner et al., 2014) and whether it might be involved in range expansion and adaptation (Caizergues et al., 2021; Gore et al., 2018; Heckwolf et al., 2020; Liebl et al., 2013; Meröndun et al., 2019; Riyahi et al., 2017; Schrey et al., 2012; Sheldon et al., 2018). DNA methylation is also studied in historical and ancient samples to characterize environmental and regulatory changes that possibly underlie adaptation and speciation (Gokhman et al., 2017; Orlando et al., 2015; Orlando & Willerslev, 2014; Rubi et al., 2019). However, it is important to note that the heritability of DNA methylation and its role in evolution is still an ongoing point of discussion (see Burggren, 2016; GuerreroBosagna et al., 2018; Heard & Martienssen, 2014; Laland et al., 2014; Liberman et al., 2019; Lind & Spagopoulou, 2018; Perez & Lehner, 2019; Richards & Pigliucci, 2020; Sarkies, 2020; Vogt, 2021). | 3 LAINE Et AL. 1.3 | Methods to study DNA methylation There are several methods for detecting DNA methylation (Feng & Lou, 2019; Tang et al., 2015) which are based on distinguishing unmethylated cytosines from methylated cytosines (5mC) in the DNA sequence and the three common principles are: (i) digestion of DNA with methylationsensitive restriction enzymes, (ii) enrichment of methylated genomic DNA fragments using antimethylcytosine antibody or methylbinding domain (MBD) proteins, and lastly, (iii) sequencing of bisulfiteconverted DNA (BSseq). In this review we will focus on the last, but see Box 1 for alternative and upcoming methods. Currently, ecological epigenetics is a field without wellestablished best practices compared to other types of molecular studies (Alvarez et al., 2015). However, studying DNA methylation comes with methodological and technical challenges, especially in vertebrate study systems in an evolutionary or ecological context. For example, sampling is often challenging, especially if samples are needed from different developmental stages. Therefore, blood is commonly used in studies in wild vertebrates, and thus may function as a proxy for patterns in other tissues. Yet if blood cells are not the main target, for example if questions are specifically related to liver function or neurological effects, it is important to establish correlations between DNA methylation patterns (of genes of interest) across tissues in the study species (see, Derks et al., 2016; Lindner et al., 2021; McKay et al., 2011). Furthermore, vertebrates are highly mobile, have relatively long generation times and often only a few offspring per generation, which can make studying epigenetics difficult. Many vertebrates also rely on parental care, BOX 1 Drawbacks of bisulfite sequencing and alternative methods The sequencing of bisulfiteconverted DNA is a very versatile method for methylation calling at single nucleotide resolution that can be used across taxa, which is especially relevant in the context of wild epigenetics. However, the method and especially the treatment of DNA with bisulfite has some drawbacks. The treatment creates a harsh environment for the DNA, which can lead to the degradation of genomic DNA (Grunau et al., 2001) and affect the quality of the sequencing reads. Furthermore, bisulfiteinduced DNA degradation is biased towards genomic regions that are enriched for unmethylated cytosines, which can result in an overestimation of global methylation levels. How the bisulfite treatment affects the quality of sequencing reads and biases the estimation of methylation levels differ between bisulfite treatment protocols (Olova et al., 2018). Recently, methods have been developed to overcome the degradation of DNA in BSseq (Wang et al., 2017). Conversion of nonmethylated cytosines can be achieved without the use of bisulfite, either with (i) enzymes in enzymatic methylseq (EMseq), which uses two enzymatic steps to differentiate between cytosine and its modified forms, 5mC and 5hmC, and shows little DNA degradation (Vaisvila et al., 2021), or (ii) using teneleven translocation (TET)- assisted pyridine borane sequencing (TAPS) that has been developed for detecting 5mC (Liu, SiejkaZielińska, et al., 2019). TAPS uses milder conditions for converting 5mC to thymine compared to bisulfite conversion. However, TAPS has multiple steps of enzymatic and chemical reactions and needs more input DNA than for example EMseq. Moreover, EMseq was shown to have higher CpG coverage, a better CpG site overlap between samples and higher consistency in methylation levels across input series compared to WGBS (Vaisvila et al., 2021) and PBAT (Han et al., 2021), making EMseq the preferred nonbisulfite whole genome method to date. To overcome many of the challenges that BSseq possesses, the thirdgeneration sequencing technologies also show promising results, such as nanopore sequencing by Oxford Nanopore Technologies (ONT) and singlemolecule realtime sequencing (SMRT), such as from Pacific BioSciences, PacBio. These technologies offer in addition to single molecule sequencing in real time also the opportunity to detect DNA methylation from the same data sets without any additional DNA treatment (Flusberg et al., 2010; Gouil & Keniry, 2019; Liu, Fang, et al., 2019; Tse et al., 2021). Although both ONT and SMRT sequencing have their challenges, such as sequencing errors, and possible low detection rate of 5mC in SMRT, these issues can be overcome (Liu et al., 2019; Stoiber et al., 2017; Tse et al., 2021) and thus thirdgeneration technologies are promising for future studies also in wild epigenetics (see Gouil & Keniry, 2019 for method comparison). Lastly, in some cases a more targeted approach is needed where methylation levels are counted from specific areas of the genome. In both bisulfite pyrosequencing and methylationspecific qPCR, single loci or regions can be analysed, for example in the case of candidate gene studies when the sequence of the region of interest is known (De Chiara et al., 2020). In the microarraybased Infinium Methylation Assay, several thousands probes are designed to target CpGs. This method has been mostly used in humans (Bibikova et al., 2011) but recently a custommade array was developed for other mammals (Arneson et al., 2021) and was used successfully in bats (Wilkinson et al., 2021). There is also a method called MEBS, which enriches methylated sequences by combining the MBD2 (methylbinding domain2) protein with bisulfite treatment, and nextgeneration sequencing (Weyrich et al., 2014). Lastly, MALDITOF— mass spectrometry of bisulphiteconverted DNA (commercialized as Sequenom's EpiTYPER assay using the MassArray system)— can be also used for more targeted approaches (Thompson et al., 2009). 4 | LAINE Et AL. making it even more challenging to separate true environmentally induced transgenerational epigenetic effects from intra- , interand multigenerational effects (Heard & Martienssen, 2014; Skinner, 2008; Skvortsova et al., 2018). It is essential to acknowledge these challenges, since they can have huge effects on the reliability of the results and conclusions. Overall, ecological epigenetics studies differ greatly in their experimental setup, study species, tissue, developmental stage, conditions, sample size, sequencing methods, and the choices made during the bioinformatics and statistical analyses (Husby, 2020; Lea et al., 2017), despite the similarities in research questions. A lack of consistency across studies complicates the interpretation of the obtained results and hinders comparisons across those studies. Published information to guide researchers new to the field of ecological epigenetics is scattered across reviews, research papers and manuals. Papers discussing the whole procedure of DNA methylation from start to end are missing for studies on wild vertebrates. Furthermore, several tips and tricks stem from experience and rely on “personal communication” that have not yet been published. Therefore, in this technical review, we provide a practical guide to researchers new to the field of ecological epigenetics. This technical review provides an inclusive overview of considerations when designing and executing an evolutionary ecological study involving nonmodel vertebrates and BSseq, in a stepbystep manner. First, we outline the different considerations of designing a study, followed by the bioinformatics steps involved in preparing the data for downstream analyses. Next, we discuss statistical considerations for the analysis of BSseq data, and the validation, interpretation and presentation of the results. Overall, this review offers recommendations focusing on BSseq, its potential biases and future directions. 2 | DESIGNING YOUR BISULFITE SEQUENCING STUDY 2.1 | Before starting your bisulfite project It is always advisable to start a project with a project and data management plan. Ideally, an experimental setup and design file is created which includes, in addition to the design, the sample names with additional metadata such as treatments, locations and conditions. We also recommend thinking about which technologies and laboratory protocols will be used. This information is often needed for collaborations and during the process of writing the manuscript (at what stage you tend to have forgotten about such details), but also when submitting data files to the repositories. Stage this file in such a way that you can follow the project life cycle from samples all the way to the final results and that you can always get back to this file and update it when needed. It is also crucial to consider the computing resources needed for a specific data set. For genomewide experiments normal desktop computers are often not sufficient as file sizes can be as big as several hundreds of megabytes or even gigabytes, depending on the genome size of the species and sequencing depth used. Analysing these files will consume a large amount of memory (RAM) and thus highperformance clusters (HPCs) are often needed (Wreczycka et al., 2017). To make your epigenetic project open and FAIR (Findable, Accessible, Interoperable and Reusable; Wilkinson et al., 2016) and also comparable to other studies, it is important to make both the pipeline including the code used and the (meta)data openly available (Gallagher et al., 2020). While the raw data and general pipeline information are relatively easy to access in the papers and data repositories, the actual code is often not provided even though some journals encourage codesharing. In past ecological studies code availability has been alarmingly low, which can be an important limiting factor for computational reproducibility in ecology (Culina et al., 2020). In biology in general, code is an important player in analysis pipelines and should be made available to promote openness and FAIRness of the project. Common places for code sharing are Github and Gitlab, which also organize version control of your scripts with git. Zenodo, an openaccess repository, can be used for both permanent data and code storage and it also provides citable DOI code. There are several good guidelines for code sharing available (Barnes, 2010; Osborne et al., 2014; Wilson et al., 2017). 2.2 | Sampling As in designing any scientific study, also in a DNA methylation project, there are some key decisions to ensure that the collected data can actually answer the research questions in both exploratory and experimental study settings. First, taxa differ considerably in the type and extent of DNA methylation variation (reviewed, e.g., in Feng et al., 2010; Zemach et al., 2010), and thus the researcher should be aware of the details of their target species. For example, in many insects DNA methylation levels are low and sporadic, and in plants and fungi DNA methylation regularly occurs outside CpG context (CG dinucleotides) (Feng et al., 2010; de Mendoza et al., 2020; Provataris et al., 2018). In vertebrates, cytosines in CpG context are a major target of DNA methylation (Bernstein et al., 2007; Bird, 2002). However, it seems that nonCpG methylation plays an important role in specific tissues, cell types and developmental states in vertebrates (Ross et al., 2021; Zabet et al., 2017; Ziller et al., 2011). Second, whether a reference genome is available will influence the choice of methods and downstream analysis that can be performed. For a large part of the wild study species a highquality reference genome is still lacking, yet methodologies have been developed also for species without a reference genome (van Gurp et al., 2016; Klughammer et al., 2015). Third, the timing of sampling needs to be tailored to the study question, for example targeting developing or adult individuals (DNA methylation patterns change over age across organisms; see, e.g. Parrott et al., 2014; Thompson et al., 2017), and to account for seasonal differences in DNA methylation (Viitaniemi et al., 2019). Fourth, given that DNA methylation patterns can differ among tissues (Lindner et al., 2021), the target tissue should also | 5 LAINE Et AL. be chosen appropriately to the study question. Finally, it has been clearly shown in both domestic and captive models, and recently in wild organisms, that the genetic background explains a relatively large proportion of DNA methylation differences (van Oers et al., 2020; Viitaniemi et al., 2019). Therefore, understanding and accounting for any genetic structure and relatedness in the data is crucial (see indepth discussion in Lea et al., 2017). 2.2.1 | To pool or not to pool? During study design it is also important to decide whether samples are sequenced individually or pooled before sequencing. Pooling of samples is a costeffective way of accurately assessing the average methylation level of single CpGs over all samples, without acquiring the DNA methylation level of each individual sample (Docherty et al., 2010). Pooling can also be used when individual sample quantities are small. Using DNA methylation levels calculated as the total number of converted and unconverted reads off all individuals within a pool, however, prohibits assessing variation between individuals. This, therefore, creates uncertainty around the average DNA methylation levels, which potentially leads to false confidence in the average DNA methylation levels. When, for example, certain individuals contribute more in reads to the overall pooled sample than others, the DNA methylation levels of the pool might be biased towards these individuals. Furthermore, answering most contemporary questions in ecology and evolution requires sample sizes exceeding those currently used (Lea et al., 2017). We therefore strongly advise against pooling, as it reduces the power to detect moderate effect size by collapsing the number of biological replicates. This advice is supported by Ziller et al. (2015), who emphasize that biological replicates should be sequenced and analysed separately rather than being pooled to increase power. However, pooling might still be a useful tool for initial exploration of differences between experimental groups or populations, but only if pools are chosen in such a way that relatedness is taken into account (see, van Oers et al., 2020; Sepers et al., 2021) and the variation among pools is minimized. This can be achieved by balancing pools carefully based on factors such as sex, age and starting material. 2.3 | Choosing the methods 2.3.1 | The bisulfite sequencing methods When treating DNA with bisulfite, cytosines are converted to uracil but 5methylcytosines are unaffected (Fraga & Esteller, 2002). Thus, DNA that has been treated with bisulfite retains only methylated cytosines. After bisulfite conversion, it is possible to combine PCR with highthroughput sequencing or microarraybased methods to determine the methylation levels of individual CpGs in the samples (also called MethylCSeq, Figure 1). The two most common approaches for sequencing of bisulfiteconverted DNA target the whole genome (wholegenome BSseq, WGBS) or a reduced and biased representation of the genome by using restriction enzymes (reduced representation bisulfite sequencing, RRBS). WGBS is still widely considered the gold standard for DNA methylation profiling as it captures about 90% of the CpG sites within the FIGURE 1 Bisulfite conversion of DNA and the PCR amplification results in two PCR products. Unmethylated cytosines (in blue) are converted to uracils and then to thymines, and methylated cytosines (in red) are unaffected. In directional libraries, the first read in pairedend sequencing originates from the original strands. The second read of pairedend sequencing is derived from the complementary strand. In nondirectional methods, the first read can be from any of the four strands and the second read from the strand complementary to the first read. mC, 5methylcytosine; OT (Watson), original top strand; CTOT, strand complementary to the original top strand; OB (Crick), original bottom strand; and CTOB, strand complementary to the original bottom strand; R1, read 1; R2, read 2; arrows point to the sequencing direction; complementary bases to methylated and unmethylated cytosines are in orange 6 | LAINE Et AL. genome at single base pair resolution (Lister et al., 2009). In addition to traditional BSseq as mentioned above (MethylCSeq), other methods for WGBS are also available. These include techniques such as “postbisulfite adaptor tagging” (PBAT; Miura et al., 2012) and tagmentationbased wholegenome bisulfite sequencing (TWGBS; Wang et al., 2013). PBAT is an amplificationfree method, which has shown low degradation bias, insignificant CGcontext coverage bias and better matched methylation levels measured by liquid chromatographytandem mass spectrometry (LCMS) (Olova et al., 2018). TWGBS uses Tn5 transposome and bisulfite conversion to study 5mC and is especially suited when starting material is limited (Wang et al., 2013; Weichenhan et al., 2019). Most current studies on ecological or evolutionary questions require sample sizes that by far exceed the sample sizes used (Lea et al., 2017), highlighting the need for approaches that target a reduced and biased representation of the genome. RRBS (Gu et al., 2011; Meissner et al., 2008), like other reduced representation approaches, makes use of restriction enzymes that nonrandomly cut the DNA at or close to the recognition sequence of the chosen restriction enzyme(s). There is a huge variety in restriction enzymes with different properties concerning, for example, the enzyme cofactor requirements and the nature of their target sequence (Williams, 2003). Depending on the recognition sequence and the frequency of this motif within the genomic sequence, restriction enzymes also differ in how frequently they cut genomic DNA. For example, the restriction enzyme MspI cuts DNA in CGrich areas often in coding regions and, in this way, RRBS requires a reduced number of reads to obtain a modest coverage of a reproducible fraction of genomewide CpG sites. These sites are often enriched for promotor regions in vertebrate systems, where CpG methylation is known to affect gene expression (see, Laine et al., 2016). This makes RRBS more cost effective than WGBS and also avoids conducting analyses on hundreds of thousands of CpG sites that are expected to have no functional significance (Sun et al., 2015). Certain studies such as when DNA methylation is studied in other contexts than genes only, however, might require an unbiased representation of DNA methylation at the individual CpG site level across the whole genome, for which WGBS is needed. Furthermore, when looking at differentially methylated regions over sites, more caution is needed in RRBS (see section 4.2.3). 2.3.2 | Sequencing depth For largescale projects, there is a tradeoff between the number of biological replicates and sequencing depth per replicate when funding is limited. Since both factors are important for statistical power, we need a large sample size to detect small effect sizes and (depending on the sequencing method) a high sequencing depth per replicate to cover CpG sites at sufficient coverage. In general, the sequencing depth of a replicate refers to the total number of sequencing reads per replicate. Often, the average sequencing depth is used, defined as the product of read length (L) and the number of sequencing reads (N) divided by the genome length (G), that is L × N/G (Sims et al., 2014). Although sequencing depth and coverage are often used interchangeably, the sequencing depth differs from the CpG site coverage, which is defined as the number of unique sequencing reads that cover a certain CpG site. Ziller et al. (2015) provide datadriven guidance on the tradeoff between the number of biological replicates and sequencing depth per replicate for highquality WGBS data sets when a regionbased analysis is performed for a betweengroup comparison; they recommend an average sequencing depth of at least 5– 15× per sample depending on the magnitude of DNA methylation differences between groups and the strategy used for identifying regions (smoothing or single CpG site). At 15× depth the fraction of true positives discovered is expected to range up to 70%, while the fraction of false negatives stays below 30%, although using replicates of each sample (i.e., acquiring a depth of 30×) is generally preferred. Ziller et al. (2015) also emphasize that, in terms of power, additional resources would be better spent on increasing the number of replicates rather than sequencing depth. Recently, a tool has been developed, poweredbiseq, that helps to predict the power of specific studies taking in to account readdepth and coverage filtering (Seiler Vellame et al., 2021). This is specifically relevant when trying to assess small effects, when read depth might be limiting, although there is still much debate across research fields whether such small effects are meaningful or not (Breton et al., 2017). In RRBS, enzymes with different properties and recognition sites might be combined to optimize the number and coverage of CpG sites (or the representation of any other targeted subset of the genome) and the average sequencing depth. The choice of enzymes can be tested in silico to optimize the RRBS approach for specific experiments (Fu et al., 2016; MartinHerranz et al., 2017). The number and coverage of CpG sites and the average sequencing depth not only depend on the choice of restriction enzymes, but also on the number of fragments and the selection of fragment lengths in combination with the chosen restriction enzymes (Fu et al., 2016). Size selection is an important part of the RRBS protocol as it prohibits the sequencing of long and uninformative sequences (e.g., sequences that do not contain CpG sites) and can be performed via cutting the desired fragment length range from a gel (Meissner et al., 2008) or via the use of beads (Boyle et al., 2012). 2.3.3 | Pairedend or singleend? In both WGBS and RRBS, libraries can be sequenced on singleor pairedend mode (Figure 1). In general, pairedend sequencing offers the potential to better map repeat regions (Grehl et al., 2018) and was reported to reduce error rates and enhance sensitivity (Tsuji & Weng, 2016). Particularly in RRBS approaches where fragment sizes are small, it is important to consider the read length relative to the fragment size to avoid overlap of readpairs (e.g., when overlap is not removed, a position is covered by the same readpair twice and counted twice, which results in bias). Yet, in cases where the interest is in assessing strandspecific DNA methylation levels, such as when | 7 LAINE Et AL. studying genomic imprinting (PatiñoParrado et al., 2017), a pairedend approach is indispensable. 2.3.4 | Choice of library type For a comprehensive and systematic evaluation of library preparation methods and sequencing platforms for highthroughput BSseq we refer the reader to Olova et al. (2018) and Zhou et al. (2019). One important aspect of bisulfite libraries is directionality (Figure 1). Directional libraries, in which only the original top and bottom strands are sequenced in singleend mode (complementary strands are also covered in pairedend mode), constitute the most common library type. In nondirectional libraries also the strands complementary to original top and bottom strands are sequenced in addition to the original top and bottom strands in singleend mode (Tsuji & Weng, 2016). This means that for nondirectional libraries, alignments to all four strands are required, which can increase processing time and the computational resources needed compared to alignments performed with directional data. In theory, directional data can be used to prevent the misidentification of C/T substitutions as unmethylated cytosines. Directional library protocols are strandspecific, which means that guanines on the strand opposing a cytosine are not affected by the bisulfite conversion (Krueger et al., 2012). Consequently, reads of a cytosine position that map to the cytosinestrand can be used to quantify the methylation level of that cytosine position, but yield no information on a potential C/T substitution, while reads that map to the guaninestrand do not yield information of the methylation status of that cytosine position but can be used to identify the C/T substitution (Liu et al., 2012). 3 | BISULFITE SEQUENCING BIOINFORMATICS 3.1 | Quality control (QC) QC is an essential step in the analysis to ensure that the data are of sufficient quality and to determine samplespecific biases. There are three technical biases that should be checked for when evaluating the QC reports. Some quality issues are the result of technical biases that are general in nextgeneration sequencing data. The first one is sequencing into the 3′- end adapter which occurs when the fragment size is smaller than the read length and the sequencing read continues into the adapter sequence of the opposite fragment end (Krueger et al., 2012). As a result, short read sequences will consist of adapter sequence at the 3′ end, which will negatively affect the mapping efficiency of the data. The second general issue is a decrease in sequencing quality towards the 3′ end due to accumulation of sequencing errors. As these errors can affect mapping efficiency and DNA methylation calling accuracy, it is important to pay attention to the quality scores of the reads and nucleotides during QC (see below for trimming). The third issue is restricted to RRBS data. In RRBS, the fragments are endrepaired after digestion to allow adapter ligation. This introduces either an unmethylated or a methylated cytosine on the 3′ end or both ends of the DNA fragments (Bock, 2012). Usually, unmethylated cytosines are introduced. If the fragment size is smaller than the read length and the sequencing process includes the inserted cytosine position, this will introduce a biased DNA methylation estimation at the read ends. Therefore, it is important to check for the presence of such a bias and, if necessary, to trim the introduced cytosine position off by removing at least two bases. QC will reveal a nucleotide content bias, but a biased DNA methylation estimation will only be visible after methylation calling (see section 3.3). fastqc (Andrews, 2010) can be used to check for quality of the reads and adapter content and fastq screen (Wingett & Andrews, 2018) in bisulfite mode to detect possible contaminations. The report files can be summarized using multiqc (Ewels et al., 2016) (Figure 2). Please be aware that bisulfite treatment affects the results of GC and perbase sequence graphs in fastqc due to the treatment converting most of the cytosines to thymines. After alignment (see section 3.2), there are tools available for more indepth inspection of the quality of bisulfite data, such as bseqc (Lin et al., 2013). 3.1.1 | Estimating bisulfite conversion efficiency The DNA methylation analysis quality of BSseqbased studies relies on the efficiency of the bisulfite treatment. Two types of errors can arise when aiming at converting unmethylated Cs. First, overconversion occurs when methylated Cs are inappropriately converted to uracils. This occurs predominantly when the conversion of unmethylated cytosines is almost complete (Genereux et al., 2008), but it is also affected by a range of factors, such as DNA quality and quantity and bisulfite conversion settings (Olova et al., 2018). Overconversion rates are difficult to measure (Liu et al., 2012), but are typically far below 1% (Genereux et al., 2008). Second, if conversion of unmethylated Cs fails, DNA methylation will be overestimated. Hence, we highly recommend calculation of the bisulfite conversion rate. In general, two different approaches can be distinguished to estimate the number of unconverted cytosines based on the analysis of (i) native methylation patterns or spikein controls or (ii) nonnative spikein controls. For vertebrates it is often assumed that Cs outside a CpG context remain always unmethylated (Reik et al., 2003). So, the conversion rate of a sequence can be calculated as the ratio between the number of correctly converted Cs outside a CpG context divided by the sum of converted and unconverted Cs outside a CpG context as done by, for example the biq analyzer (Bock et al., 2005). However, this assumption is not true as mentioned above in section 2.2. Alternatively, a native spikein approach was developed for vertebrates that uses telomeric sequences, in which “TTAGGG” is repeated ~3000 times. Its complementary DNA strand contains “CCCTAA” repeats, which have three nonCpG sites (one CpT and 8 | LAINE Et AL. | 15 LAINE Et AL. Molecular Ecology, 25(8), 1785– 1800. https://doi.org/10.1111/ mec.13231 Barnes, N. (2010). Publish your computer code: It is good enough. Nature, 467(7317), 753. https://doi.org/10.1038/467753a Barton, S. J., Crozier, S. R., Lillycrop, K. A., Godfrey, K. M., & Inskip, H. M. (2013). Correction of unexpected distributions of P values from analysis of whole genome arrays by rectifying violation of statistical assumptions. BMC Genomics, 14(1), 161. ht tps://doi. org/10.1186/1471216414161 Becker, C., Hagmann, J., Müller, J., Koenig, D., Stegle, O., Borgwardt, K., & Weigel, D. (2011). Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature, 480(7376), 245– 249. https://doi.org/10.1038/natur e10555 Bentz, A. B., Sirman, A. E., Wada, H., Navara, K. J., & Hood, W. R. (2016). Relationship between maternal environment and DNA methylation patterns of estrogen receptor alpha in wild Eastern Bluebird (Sialia sialis) nestlings: A pilot study. Ecology and Evolution, 6(14), 4741– 4752. https://doi.org/10.1002/ece3.2162 Bernstein, B. E., Meissner, A., & Lander, E. S. (2007). The Mammalian Epigenome. Cell, 128(4), 669– 681. https://doi.org/10.1016/j. cell.2007.01.033 Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J. M., Delano, D., Zhang, L. U., Schroth, G. P., Gunderson, K. L., Fan, J.- B., & Shen, R. (2011). High density DNA methylation array with single CpG site resolution. Genomics, 98(4), 288– 295. https://doi.org/10.1016/j. ygeno.2011.07.007 Bird, A. (2002). DNA methylation patterns and epigenetic memory. Genes & Development, 16(1), 6– 21. https://doi.org/10.1101/gad.947102 Bock, C. (2012). Analysing and interpreting DNA methylation data. Nature Reviews Genetics, 13(10), 705– 719. https://doi.org/10.1038/ nrg3273 Bock, C., Reither, S., Mikeska, T., Paulsen, M., Walter, J., & Lengauer, T. (2005). BiQ Analyzer: Visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics, 21(21), 4067– 4068. https://doi.org/10.1093/bioin forma tics/bti652 Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114– 2120. https://doi.org/10.1093/bioin forma tics/btu170 Bossdorf, O., Richards, C. L., & Pigliucci, M. (2008). Epigenetics for ecologists. Ecology Letters, 11(2), 106– 115. https://doi. org/10.1111/j.14610248.2007.01130.x Boyle, P., Clement, K., Gu, H., Smith, Z. D., Ziller, M., Fostel, J. L., Holmes, L., Meldrim, J., Kelley, F., Gnirke, A., & Meissner, A. (2012). Gelfree multiplexed reduced representation bisulfite sequencing for largescale DNA methylation profiling. Genome Biology, 13(10), R92. https://doi.org/10.1186/gb20121310R92 Breton, C. V., Marsit, C. J., Faustman, E., Nadeau, K., Goodrich, J. M., Dolinoy, D. C., Herbstman, J., Holland, N., LaSalle, J. M., Schmidt, R., Yousefi, P., Perera, F., Joubert, B. R., Wiemels, J., Taylor, M., Yang, I. V., Chen, R., Hew, K. M., Freeland, D. M. H., … Murphy, S. K. (2017). Smallmagnitude effect sizes in epigenetic end points are important in children’s environmental health studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environmental Health Perspectives, 125(4), 511– 526. https://doi.org/10.1289/EHP595 Burggren, W. (2016). Epigenetic inheritance and its role in evolutionary biology: Reevaluation and new perspectives. Biology, 5(2), 24. https://doi.org/10.3390/biolo gy502 0024 Caizergues, A. E., Luyer, J. L., Grégoire, A., Szulkin, M., Senar, J.- C., Charmantier, A., & Perrier, C. (2021). Testing for parallel genomic and epigenomic footprints of adaptation to urban life in a passerine bird. BioRxiv. 2021.02.10.430452. https://doi. org/10.1101/2021.02.10.430452 Caracappa, S., Pisciotta, A., Persichetti, M. F., Caracappa, G., Alduina, R., & Arculeo, M. (2016). Nonmodal scutes patterns in the Loggerhead Sea Turtle (Caretta caretta): A possible epigenetic effect? Canadian Journal of Zoology, 94(5), 379383. https://doi.org/10.1139/cjz20150248 Carmona, J. J., Accomando, W. P., Binder, A. M., Hutchinson, J. N., Pantano, L., Izzi, B., Just, A. C., Lin, X., Schwartz, J., Vokonas, P. S., Amr, S. S., Baccarelli, A. A., & Michels, K. B. (2017). Empirical comparison of reduced representation bisulfite sequencing and Infinium BeadChip reproducibility and coverage of DNA methylation in humans. Npj Genomic Medicine, 2(1), 13. https://doi. org/10.1038/s4152 501700129 Catoni, M., Tsang, J. M., Greco, A. P., & Zabet, N. R. (2018). DMRcaller: A versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and nonCpG contexts. Nucleic Acids Research, 46(19), e114. https://doi.org/10.1093/nar/ gky602 Chatterjee, A., Stockwell, P. A., Rodger, E. J., & Morison, I. M. (2012). Comparison of alignment software for genomewide bisulphite sequence data. Nucleic Acids Research, 40(10), e79. https://doi. org/10.1093/nar/gks150 Chen, P.- Y., Cokus, S. J., & Pellegrini, M. (2010). BS Seeker: Precise mapping for bisulfite sequencing. BMC Bioinformatics, 11(1), 203. https://doi.org/10.1186/1471210511203 Chen, Z., & Riggs, A. D. (2005). Maintenance and regulation of DNA methylation patterns in mammals. Biochemistry and Cell Biology, 83(4), 438– 448. https://doi.org/10.1139/o05138 CraryDooley, F. K., Tam, M. E., Dunaway, K. W., HertzPicciotto, I., Schmidt, R. J., & LaSalle, J. M. (2017). A comparison of existing global DNA methylation assays to lowcoverage wholegenome bisulfite sequencing for epidemiological studies. Epigenetics, 12(3), 206– 214. https://doi.org/10.1080/15592 294.2016.1276680 Culina, A., van den Berg, I., Evans, S., & SánchezTójar, A. (2020). Low availability of code in ecology: A call for urgent action. PLOS Biology, 18(7), e3000763. https://doi.org/10.1371/journ al.pbio.3000763 De Chiara, L., LeiroFernandez, V., RodríguezGirondo, M., Valverde, D., BotanaRial, M. I., & FernándezVillar, A. (2020). Comparison of bisulfite pyrosequencing and methylationspecific qPCR for methylation assessment. International Journal of Molecular Sciences, 21(23), 9242. https://doi.org/10.3390/ijms2 1239242 de Mendoza, A., Lister, R., & Bogdanovic, O. (2020). Evolution of DNA Methylome Diversity in Eukaryotes. Journal of Molecular Biology, S0022– 2836(19), 30659X. https://doi.org/10.1016/j. jmb.2019.11.003 Derks, M. F. L., Schachtschneider, K. M., Madsen, O., Schijlen, E., Verhoeven, K. J. F., & van Oers, K. (2016). Gene and transposable element methylation in great tit (Parus major) brain and blood. BMC Genomics, 17(1), 332. https://doi.org/10.1186/s1286 40162653y Docherty, S. J., Davis, O. S. P., Haworth, C. M. A., Plomin, R., & Mill, J. (2010). DNA methylation profiling using bisulfitebased epityping of pooled genomic DNA. Methods, 52(3), 255– 258. ht tps://doi. org/10.1016/j.ymeth.2010.06.017 Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19), 3047– 3048. https://doi. org/10.1093/bioin forma tics/btw354 Feng, H., Conneely, K. N., & Wu, H. (2014). A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Research, 42(8), e69. https:// doi.org/10.1093/nar/gku154 Feng, L., & Lou, J. (2019). DNA methylation analysis. In Q. Zhang (Ed.), Nanotoxicity (pp. 181– 227). Springer New York. https://doi. org/10.1007/9781493989164_12 Feng, S., Cokus, S. J., Zhang, X., Chen, P.- Y., Bostick, M., Goll, M. G., Hetzel, J., Jain, J., Strauss, S. H., Halpern, M. E., Ukomadu, C., Sadler, K. C., Pradhan, S., Pellegrini, M., & Jacobsen, S. E. (2010). Conservation and divergence of methylation patterning in plants and animals. Proceedings of the National Academy of Sciences of 16 | LAINE Et AL. the United States of America, 107(19), 8689– 8694. https://doi. org/10.1073/pnas.10027 20107 Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., Korlach, J., & Turner, S. W. (2010). Direct detection of DNA methylation during singlemolecule, realtime sequencing. Nature Methods, 7(6), 461– 465. https://doi.org/10.1038/ nmeth.1459 Forsman, A. (2015). Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity, 115(4), 276– 284. https://doi.org/10.1038/hdy.2014.92 Fraga, M. F., & Esteller, M. (2002). DNA methylation: A profile of methods and applications. BioTechniques, 33(3), 632, 634, 636– 649. https://doi.org/10.2144/02333 rv01 Franklin, T. B., Russig, H., Weiss, I. C., Gräff, J., Linder, N., Michalon, A., Vizi, S., & Mansuy, I. M. (2010). Epigenetic transmission of the impact of early stress across generations. Biological Psychiatry, 68(5), 408– 415. https://doi.org/10.1016/j.biops ych.2010.05.036 Fu, Y.- B., Peterson, G. W., & Dong, Y. (2016). Increasing genome sampling and improving SNP genotyping for genotypingbysequencing with new combinations of restriction enzymes. G3: Genes|genomes|genetics, 6(4), 845– 856. https://doi.org/10.1534/ g3.115.025775 Gallagher, R. V., Falster, D. S., Maitner, B. S., SalgueroGómez, R., Vandvik, V., Pearse, W. D., Schneider, F. D., Kattge, J., Poelen, J. H., Madin, J. S., Ankenbrand, M. J., Penone, C., Feng, X., Adams, V. M., Alroy, J., Andrew, S. C., Balk, M. A., Bland, L. M., Boyle, B. L., … Enquist, B. J. (2020). Open Science principles for accelerating traitbased science across the Tree of Life. Nature Ecology & Evolution, 4(3), 294– 303. https://doi.org/10.1038/s4155 902011096 Garcia, M. J., RodríguezBrenes, S., Kobisk, A., Adler, L., Ryan, M. J., Taylor, R. C., & Hunter, K. L. (2019). Epigenomic changes in the túngara frog (Physalaemus pustulosus): Possible effects of introduced fungal pathogen and urbanization. Evolutionary Ecology, 33(5), 671– 686. https://doi.org/10.1007/s1068 201910001 - 8 Gawehns, F., Postuma, M., Antro, M. V., Nunn, A., Sepers, B., van Gurp, T. P., Wagemaker, N. C. A. M., Mateman, A. C., MilanovicIvanovic, S., Groβe, I., van Oers, K., Vergeer, P., & Verhoeven, K. J. F. (2022). epiGBS2: Improvements and evaluation of highly multiplexed, epiGBSbased reduced representation bisulfite sequencing. Molecular Ecology Resources, 1– 8. https://doi. org/10.1111/17550998.13597 Genereux, D. P., Johnson, W. C., Burden, A. F., Stoger, R., & Laird, C. D. (2008). Errors in the bisulfite conversion of DNA: Modulating inappropriateand failedconversion frequencies. Nucleic Acids Research, 36(22), e150. https://doi.org/10.1093/nar/gkn691 Gokhman, D., Malul, A., & Carmel, L. (2017). Inferring past environments from ancient epigenomes. Molecular Biology and Evolution, 34(10), 2429– 2438. https://doi.org/10.1093/molbe v/msx211 Goldberg, A. D., Allis, C. D., & Bernstein, E. (2007). Epigenetics: A landscape takes shape. Cell, 128(4), 635– 638. https://doi.org/10.1016/j. cell.2007.02.006 Gore, A. V., Tomins, K. A., Iben, J., Ma, L. I., Castranova, D., Davis, A. E., Parkhurst, A., Jeffery, W. R., & Weinstein, B. M. (2018). An epigenetic mechanism for cavefish eye degeneration. Nature Ecology & Evolution, 2(7), 1155– 1160. https://doi.org/10.1038/s4155 901805694 Gouil, Q., & Keniry, A. (2019). Latest techniques to study DNA methylation. Essays in Biochemistry, 63(6), 639– 648. https://doi. org/10.1042/EBC20 190027 Grehl, C., Kuhlmann, M., Becker, C., Glaser, B., & Grosse, I. (2018). How to design a wholegenome bisulfite sequencing experiment. Epigenomes, 2(4), 21. https://doi.org/10.3390/epige nomes 2040021 Grunau, C., Clark, S. J., & Rosenthal, A. (2001). Bisulfite genomic sequencing: Systematic investigation of critical experimental parameters. Nucleic Acids Research, 29(13), e65. https://doi. org/10.1093/nar/29.13.e65 Gu, H., Smith, Z. D., Bock, C., Boyle, P., Gnirke, A., & Meissner, A. (2011). Preparation of reduced representation bisulfite sequencing libraries for genomescale DNA methylation profiling. Nature Protocols, 6(4), 468– 481. https://doi.org/10.1038/nprot.2010.190 GuerreroBosagna, C., Morisson, M., Liaubet, L., Rodenburg, T. B., de Haas, E. N., Košťál, Ľ., & Pitel, F. (2018). Transgenerational epigenetic inheritance in birds. Environmental. Epigenetics, 4(2), https://doi. org/10.1093/eep/dvy008 Guo, W., Fiziev, P., Yan, W., Cokus, S., Sun, X., Zhang, M. Q., Chen, P.- Y., & Pellegrini, M. (2013). BSSeeker2: A versatile aligning pipeline for bisulfite sequencing data. BMC Genomics, 14(1), 774. https://doi. org/10.1186/1471216414774 Guo, W., Zhu, P., Pellegrini, M., Zhang, M. Q., Wang, X., & Ni, Z. (2018). CGmapTools improves the precision of heterozygous SNV calls and supports allelespecific methylation detection and visualization in bisulfitesequencing data. Bioinformatics, 34(3), 381– 387. https:// doi.org/10.1093/bioin forma tics/btx595 Han, Y., Zheleznyakova, G. Y., MarincevicZuniga, Y., Kakhki, M. P., Raine, A., Needhamsen, M., & Jagodic, M. (2021). Comparison of EMseq and PBAT methylome library methods for lowinput DNA. Epigenetics, 1– 10, https://doi.org/10.1080/15592 294.2021.1997406 Hansen, K. D., Langmead, B., & Irizarry, R. A. (2012). BSmooth: From whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology, 13(10), R83. https://doi.org/10.1186/ gb20121310r83 Hawes, N. A., Fidler, A. E., Tremblay, L. A., Pochon, X., Dunphy, B. J., & Smith, K. F. (2018). Understanding the role of DNA methylation in successful biological invasions: A review. Biological Invasions, 20(9), 2285– 2300. https://doi.org/10.1007/s1053 001817036 Heard, E., & Martienssen, R. A. (2014). Transgenerational epigenetic inheritance: Myths and mechanisms. Cell, 157(1), 95– 109. https://doi. org/10.1016/j.cell.2014.02.045 Hebestreit, K., Dugas, M., & Klein, H.- U. (2013). Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics, 29(13), 1647– 1653. https://doi.org/10.1093/ bioin forma tics/btt263 Heckwolf, M. J., Meyer, B. S., Häsler, R., Höppner, M. P., Eizaguirre, C., & Reusch, T. B. H. (2020). Two different epigenetic information channels in wild threespined sticklebacks are involved in salinity adaptation. Science Advances, 6(12), eaaz1138. https://doi.org/10.1126/ sciadv.aaz1138 Holmes, E. E., Jung, M., Meller, S., Leisse, A., Sailer, V., Zech, J., Mengdehl, M., Garbe, L.- A., Uhl, B., Kristiansen, G., & Dietrich, D. (2014). Performance evaluation of kits for bisulfiteconversion of DNA from tissues, cell lines, FFPE tissues, aspirates, lavages, effusions, plasma, serum, and urine. PLoS One, 9(4), 15. https://doi. org/10.1371/journ al.pone.0093933 Hu, J., Askary, A. M., Thurman, T. J., Spiller, D. A., Palmer, T. M., Pringle, R. M., & Barrett, R. D. H. (2019). The epigenetic signature of colonizing new environments in anolis lizards. Molecular Biology and Evolution, 36(10), 2165– 2170. https://doi.org/10.1093/molbe v/ msz133 Hu, J., PérezJvostov, F., Blondel, L., & Barrett, R. D. H. (2018). Genomewide DNA methylation signatures of infection status in Trinidadian guppies (Poecilia reticulata). Molecular Ecology, 27(15), 3087– 3102. https://doi.org/10.1111/mec.14771 Hu, J., Wuitchik, S. J. S., Barry, T. N., Jamniczky, H. A., Rogers, S. M., & Barrett, R. D. H. (2021). Heritability of DNA methylation in threespine stickleback (Gasterosteus aculeatus). Genetics, 217(1), 1– 15. https://doi.org/10.1093/genet ics/iyab001 Husby, A. (2020). On the use of blood samples for measuring DNA methylation in ecological epigenetic studies. Integrative and Comparative Biology, 60(6), 1558– 1566. https://doi.org/10.1093/icb/icaa123 | 17 LAINE Et AL. Ito, H., Udono, T., Hirata, S., & InoueMurayama, M. (2018). Estimation of chimpanzee age based on DNA methylation. Scientific Reports, 8(1), 9998. https://doi.org/10.1038/s4159 801828318 - 9 Jablonka, E., & Lamb, M. J. (2007). Précis of evolution in four dimensions. Behavioral and Brain Sciences, 30(4), 353– 365. https://doi. org/10.1017/S0140 525X0 7002221 Kilvitis, H. J., Alvarez, M., Foust, C. M., Schrey, A. W., Robertson, M., & Richards, C. L. (2014). Ecological epigenetics. In C. R. Landry, & N. AubinHorth (Eds.), Ecological genomics (pp. 191– 210). Springer Netherlands. https://doi.org/10.1007/9789400773479_10 Klughammer, J., Datlinger, P., Printz, D., Sheffield, N. C., Farlik, M., Hadler, J., Fritsch, G., & Bock, C. (2015). Differential DNA methylation analysis without a reference genome. Cell Reports, 13(11), 2621– 2633. https://doi.org/10.1016/j.celrep.2015.11.024 Korochkin, L. I. (2006). What is epigenetics. Russian Journal of Genetics, 42(9), 958– 965. https://doi.org/10.1134/S1022 79540 609002X Krueger, F. (2016). QC Fail Sequencing » Softclipping of reads may add potentially unwanted alignments to repetitive regions. Retrieved June 17, 2021, from https://seque ncing.qcfail.com/artic les/softclipp ingofreads - mayaddpoten tiall yunwan tedalign ments - torepet itive - regio ns/ Krueger, F. (2020). UmiGrinder [Perl]. Retrieved from https://github.com/ Felix Krueg er/UmiGrinder Krueger, F., & Andrews, S. R. (2011). Bismark: A flexible aligner and methylation caller for BisulfiteSeq applications. Bioinformatics, 27(11), 1571– 1572. https://doi.org/10.1093/bioin forma tics/ btr167 Krueger, F., Kreck, B., Franke, A., & Andrews, S. R. (2012). DNA methylome analysis using short bisulfite sequencing data. Nature Methods, 9(2), 145– 151. https://doi.org/10.1038/nmeth.1828 Laine, V. N., Gossmann, T. I., Schachtschneider, K. M., Garroway, C. J., Madsen, O., Verhoeven, K. J. F., de Jager, V., Megens, H.- J., Warren, W. C., Minx, P., Crooijmans, R. P. M. A., Corcoran, P., Sheldon, B. C., Slate, J., Zeng, K., van Oers, K., Visser, M. E., & Groenen, M. A. M. (2016). Evolutionary signals of selection on cognition from the great tit genome and methylome. Nature Communications, 7(1), 10474. https://doi.org/10.1038/ncomm s10474 Laine, V. N., Verschuuren, M., van Oers, K., Espín, S., SánchezVirosta, P., Eeva, T., & Ruuskanen, S. (2021). Does arsenic contamination affect DNA methylation patterns in a wild bird population? An experimental approach. Environmental Science & Technology, 55(13), 8947– 8954. https://doi.org/10.1021/acs.est.0c08621 Laland, K., Uller, T., Feldman, M., Sterelny, K., Müller, G. B., Moczek, A., Jablonka, E., OdlingSmee, J., Wray, G. A., Hoekstra, H. E., Futuyma, D. J., Lenski, R. E., Mackay, T. F. C., Schluter, D., & Strassmann, J. E. (2014). Does evolutionary theory need a rethink? Nature, 514(7521), 161– 164. https://doi.org/10.1038/514161a Langmead, B., & Salzberg, S. L. (2012). Fast gappedread alignment with Bowtie 2. Nature Methods, 9(4), 357– 359. https://doi.org/10.1038/ nmeth.1923 Laubach, Z. M., Faulk, C. D., Dolinoy, D. C., Montrose, L., Jones, T. R., Ray, D., Pioon, M. O., & Holekamp, K. E. (2019). Early life social and ecological determinants of global DNA methylation in wild spotted hyenas. Molecular Ecology, 28(16), 3799– 3812. https://doi. org/10.1111/mec.15174 Law, J. A., & Jacobsen, S. E. (2010). Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nature Reviews Genetics, 11(3), 204– 220. https://doi.org/10.1038/nrg2719 Lea, A. J., Altmann, J., Alberts, S. C., & Tung, J. (2016). Resource base influences genomewide DNA methylation levels in wild baboons (Papio cynocephalus). Molecular Ecology, 25(8), 1681– 1696. https:// doi.org/10.1111/mec.13436 Lea, A. J., Tung, J., & Zhou, X. (2015). A Flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. PLOS Genetics, 11(11), e1005650. ht tps://doi. org/10.1371/journ al.pgen.1005650 Lea, A. J., Vilgalys, T. P., Durst, P. A. P., & Tung, J. (2017). Maximizing ecological and evolutionary insight in bisulfite sequencing data sets. Nature Ecology & Evolution, 1(8), 1074– 1083. https://doi. org/10.1038/s4155 901702290 LedónRettig, C. C., Richards, C. L., & Martin, L. B. (2013). Epigenetics for behavioral ecologists. Behavioral Ecology, 24(2), 311– 324. https:// doi.org/10.1093/behec o/ars145 Lee, J.- H., Park, S.- J., & Kenta, N. (2015). An integrative approach for efficient analysis of whole genome bisulfite sequencing data. BMC Genomics, 16(Suppl 12), S14. https://doi. org/10.1186/1471216416S12S14 Li, Q., Li, N., Hu, X., Li, J., Du, Z., Chen, L. I., Yin, G., Duan, J., Zhang, H., Zhao, Y., Wang, J., & Li, N. (2011). Genomewide mapping of DNA methylation in chicken. PLoS One, 6(5), e19428. https://doi. org/10.1371/journ al.pone.0019428 Liberman, N., Wang, S. Y., & Greer, E. L. (2019). Transgenerational epigenetic inheritance: From phenomena to molecular mechanisms. Current Opinion in Neurobiology, 59, 189– 206. https://doi. org/10.1016/j.conb.2019.09.012 Liebl, A. L., Schrey, A. W., Richards, C. L., & Martin, L. B. (2013). Patterns of DNA methylation throughout a range expansion of an introduced songbird. Integrative and Comparative Biology, 53(2), 351– 358. https://doi.org/10.1093/icb/ict007 Lin, X., Sun, D., Rodriguez, B., Zhao, Q., Sun, H., Zhang, Y., & Li, W. (2013). BSeQC: Quality control of bisulfite sequencing experiments. Bioinformatics, 29(24), 3227– 3229. https://doi.org/10.1093/bioin forma tics/btt548 Lind, M. I., & Spagopoulou, F. (2018). Evolutionary consequences of epigenetic inheritance. Heredity, 121(3), 205– 209. https://doi. org/10.1038/s4143 70180113y Lindner, M., Gawehns, F., te Molder, S., Visser, M. E., van Oers, K., & Laine, V. N. (2022). Performance of methods to detect genetic variants from bisulfite sequencing data in a nonmodel species. Molecular Ecology Resources, 22, 834– 846. https://doi. org/10.1111/17550998.13493 Lindner, M., Verhagen, I., Viitaniemi, H. M., Laine, V. N., Visser, M. E., Husby, A., & van Oers, K. (2021). Temporal changes in DNA methylation and RNA expression in a small song bird: Withinand betweentissue comparisons. BMC Genomics, 22(1), 36. https://doi. org/10.1186/s1286 402007329 - 9 Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., TontiFilippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.- M., Edsall, L., AntosiewiczBourget, J., Stewart, R., Ruotti, V., Millar, A. H., Thomson, J. A., Ren, B., & Ecker, J. R. (2009). Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462(7271), 315– 322. https://doi.org/10.1038/natur e08514 Liu, Q., Fang, L., Yu, G., Wang, D., Xiao, C.- L., & Wang, K. (2019). Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data. Nature Communications, 10, https://doi.org/10.1038/s4146 701910168 - 2 Liu, Q., Georgieva, D. C., Egli, D., & Wang, K. (2019). NanoMod: A computational tool to detect DNA modifications using Nanopore longread sequencing data. BMC Genomics, 20(Suppl 1). ht tps://doi. org/10.1186/s1286 401853728 Liu, Y., Siegmund, K. D., Laird, P. W., & Berman, B. P. (2012). BisSNP: Combined DNA methylation and SNP calling for Bisulfiteseq data. Genome Biology, 13(7), R61. https://doi.org/10.1186/ gb2012137r61 Liu, Y., SiejkaZielińska, P., Velikova, G., Bi, Y., Yuan, F., Tomkova, M., Bai, C., Chen, L., SchusterBöckler, B., & Song, C.- X. (2019). Bisulfitefree direct detection of 5methylcytosine and 5hydroxymethylcytosine at base resolution. Nature Biotechnology, 37(4), 424– 429. https:// doi.org/10.1038/s4158 701900412 Mäkinen, H., van Oers, K., Eeva, T., & Ruuskanen, S. (2021). The effect of experimental lead pollution on DNA methylation in a wild bird 18 | LAINE Et AL. population. Epigenetics, 1– 17. https://doi.org/10.1080/15592 294.2021.1943863 Mäkinen, H., Viitaniemi, H. M., Visser, M. E., Verhagen, I., van Oers, K., & Husby, A. (2019). Temporally replicated DNA methylation patterns in great tit using reduced representation bisulfite sequencing. Scientific Data, 6(1), 136. https://doi.org/10.1038/s4159 701901360 Mansell, G., GorrieStone, T. J., Bao, Y., Kumari, M., Schalkwyk, L. S., Mill, J., & Hannon, E. (2019). Guidance for DNA methylation studies: Statistical insights from the Illumina EPIC array. BMC Genomics, 20(1), 366. https://doi.org/10.1186/s1286 401957617 MarcoSola, S., Sammeth, M., Guigó, R., & Ribeca, P. (2012). The GEM mapper: Fast, accurate and versatile alignment by filtration. Nature Methods, 9(12), 1185– 1188. https://doi.org/10.1038/nmeth.2221 Martin, M. (2011). Cutadapt removes adapter sequences from highthroughput sequencing reads. EMBnet.Journal, 17(1), 10– 12. https://doi.org/10.14806/ ej.17.1.200 MartinHerranz, D. E., Ribeiro, A. J. M., Krueger, F., Thornton, J. M., Reik, W., & Stubbs, T. M. (2017). cuRRBS: Simple and robust evaluation of enzyme combinations for reduced representation approaches. Nucleic Acids Research, 45(20), 11559– 11569. https://doi. org/10.1093/nar/gkx814 Massicotte, R., & Angers, B. (2012). Generalpurpose genotype or how epigenetics extend the flexibility of a genotype. Genetics Research International, 2012, 1– 7. https://doi.org/10.1155/2012/317175 McKay, J. A., Xie, L., Harris, S., Wong, Y. K., Ford, D., & Mathers, J. C. (2011). Blood as a surrogate marker for tissuespecific DNA methylation and changes due to folate depletion in postpartum female mice. Molecular Nutrition & Food Research, 55(7), 1026– 1035. https://doi.org/10.1002/mnfr.20110 0008 McNew, S. M., Beck, D., SadlerRiggleman, I., Knutie, S. A., Koop, J. A. H., Clayton, D. H., & Skinner, M. K. (2017). Epigenetic variation between urban and rural populations of Darwin’s finches. BMC Evolutionary Biology, 17(1), 183. https://doi.org/10.1186/s1286 201710259 McNew, S. M., Boquete, M. T., EspinozaUlloa, S., Andres, J. A., Wagemaker, N. C. A. M., Knutie, S. A., Richards, C. L., & Clayton, D. H. (2021). Epigenetic effects of parasites and pesticides on captive and wild nestling birds. Ecology and Evolution, https://doi. org/10.1002/ece3.7606 Meissner, A., Mikkelsen, T. S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B. E., Nusbaum, C., Jaffe, D. B., Gnirke, A., Jaenisch, R., & Lander, E. S. (2008). Genomescale DNA methylation maps of pluripotent and differentiated cells. Nature, 454(7205), 766– 770. https://doi.org/10.1038/natur e07107 Meröndun, J., Murray, D. L., & Shafer, A. B. A. (2019). Genomescale sampling suggests cryptic epigenetic structuring and insular divergence in Canada lynx. Molecular Ecology, 28(13), 3186– 3196. https://doi. org/10.1111/mec.15131 Miura, F., Enomoto, Y., Dairiki, R., & Ito, T. (2012). Amplificationfree wholegenome bisulfite sequencing by postbisulfite adaptor tagging. Nucleic Acids Research, 40(17), e136. https://doi.org/10.1093/ nar/gks454 Moore, L. D., Le, T., & Fan, G. (2013). DNA Methylation and its basic function. Neuropsychopharmacology, 38(1), 23– 38. https://doi. org/10.1038/npp.2012.112 Nilsen, F. M., Parrott, B. B., Bowden, J. A., Kassim, B. L., Somerville, S. E., Bryan, T. A., Bryan, C. E., Lange, T. R., Delaney, J. P., Brunell, A. M., Long, S. E., & Guillette, L. J. (2016). Global DNA methylation loss associated with mercury contamination and aging in the American alligator (Alligator mississippiensis). Science of the Total Environment, 545– 546, 389– 397. https://doi.org/10.1016/j.scito tenv.2015.12.059 Noguera, J. C., & Velando, A. (2019). Bird embryos perceive vibratory cues of predation risk from clutch mates. Nature Ecology & Evolution, 3(8), 1225– 1232. https://doi.org/10.1038/ s4155 901909298 Nunn, A., Otto, C., Stadler, P. F., & Langenberger, D. (2021). Comprehensive benchmarking of software for mapping whole genome bisulfite data: From read alignment to DNA methylation analysis. Briefings in Bioinformatics, 22, bbab021. https://doi.org/10.1093/bib/bbab021 Olova, N., Krueger, F., Andrews, S., Oxley, D., Berrens, R. V., Branco, M. R., & Reik, W. (2018). Comparison of wholegenome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data. Genome Biology, 19(1), 33. https:// doi.org/10.1186/s1305 901814082 Orlando, L., Gilbert, M. T. P., & Willerslev, E. (2015). Reconstructing ancient genomes and epigenomes. Nature Reviews Genetics, 16(7), 395– 408. https://doi.org/10.1038/nrg3935 Orlando, L., & Willerslev, E. (2014). An epigenetic window into the past? Science, 345(6196), 511– 512. https://doi.org/10.1126/scien ce.1256515 Osborne, J. M., Bernabeu, M. O., Bruna, M., Calderhead, B., Cooper, J., Dalchau, N., Dunn, S.- J., Fletcher, A. G., Freeman, R., Groen, D., Knapp, B., McInerny, G. J., Mirams, G. R., PittFrancis, J., Sengupta, B., Wright, D. W., Yates, C. A., Gavaghan, D. J., Emmott, S., & Deane, C. (2014). Ten simple rules for effective computational research. PLOS Computational Biology, 10(3), e1003506. https://doi. org/10.1371/journ al.pcbi.1003506 Otto, C., Stadler, P. F., & Hoffmann, S. (2012). Fast and sensitive mapping of bisulfitetreated sequencing data. Bioinformatics, 28(13), 1698– 1704. https://doi.org/10.1093/bioin forma tics/bts254 PaoliIseppi, R. D., Deagle, B. E., Polanowski, A. M., McMahon, C. R., Dickinson, J. L., Hindell, M. A., & Jarman, S. N. (2019). Age estimation in a longlived seabird (Ardenna tenuirostris) using DNA methylationbased biomarkers. Molecular Ecology Resources, 19(2), 411– 425. https://doi.org/10.1111/17550998.12981 Parrott, B. B., Bowden, J. A., Kohno, S., CloyMcCoy, J. A., Hale, M. D., Bangma, J. T., Rainwater, T. R., Wilkinson, P. M., Kucklick, J. R., & Guillette, L. J. (2014). Influence of tissue, age, and environmental quality on DNA methylation in Alligator mississippiensis. Reproduction, 147(4), 503– 513. https://doi.org/10.1530/ REP130498 PatiñoParrado, I., GómezJiménez, Á., LópezSánchez, N., & Frade, J. M. (2017). Strandspecific CpG hemimethylation, a novel epigenetic modification functional for genomic imprinting. Nucleic Acids Research, 45(15), 8822– 8834. https://doi.org/10.1093/nar/gkx518 Perez, M. F., & Lehner, B. (2019). Intergenerational and transgenerational epigenetic inheritance in animals. Nature Cell Biology, 21(2), 143– 151. https://doi.org/10.1038/s4155 601802429 Pértille, F., Brantsæter, M., Nordgreen, J., Coutinho, L. L., Janczak, A. M., Jensen, P., & GuerreroBosagna, C. (2017). DNA methylation profiles in red blood cells of adult hens correlate with their rearing conditions. Journal of Experimental Biology, 220(19), 3579– 3587. https://doi.org/10.1242/jeb.157891 Picard toolkit [Java] (2019). Broad Institute. Retrieved from https:// github.com/broad insti tute/picard Pierron, F., Bureau du Colombier, S., Moffett, A., Caron, A., Peluhet, L., Daffe, G., Lambert, P., Elie, P., Labadie, P., Budzinski, H., Dufour, S., Couture, P., & Baudrimont, M. (2014). Abnormal ovarian DNA methylation programming during gonad maturation in wild contaminated fish. Environmental Science & Technology, 48(19), 11688– 11695. https://doi.org/10.1021/es503 712c Pigliucci, M. (2001). Phenotypic plasticity: Beyond nature and nurture. JHU Press. Pigliucci, M. (2005). Evolution of phenotypic plasticity: Where are we going now? Trends in Ecology & Evolution, 20(9), 481– 486. https:// doi.org/10.1016/j.tree.2005.06.001 Polanowski, A. M., Robbins, J., Chandler, D., & Jarman, S. N. (2014). Epigenetic estimation of age in humpback whales. | 19 LAINE Et AL. Molecular Ecology Resources, 14(5), 976– 987. ht tps://doi. org/10.1111/17550998.12247 Provataris, P., Meusemann, K., Niehuis, O., Grath, S., & Misof, B. (2018). Signatures of DNA methylation across insects suggest reduced DNA methylation levels in Holometabola. Genome Biology and Evolution, 10(4), 1185– 1197. https://doi.org/10.1093/gbe/evy066 R Core Team (2020). R Foundation for Statistical Computing. R foundation for Statistical Computing. Retrieved from http://www.Rproje ct.org Reik, W., Santos, F., & Dean, W. (2003). Mammalian epigenomics: Reprogramming the genome for development and therapy. Theriogenology, 59(1), 21– 32. https://doi.org/10.1016/S0093 - 691X(02)01269 - 4 Richards, C. L., & Pigliucci, M. (2020). Epigenetic inheritance. A decade into the extended evolutionary synthesis. Paradigmi, 38(3):463– 494. https://doi.org/10.30460/ 99624 Richards, E. J. (2006). Inherited epigenetic variation— Revisiting soft inheritance. Nature Reviews Genetics, 7(5), 395– 401. https://doi. org/10.1038/nrg1834 Riyahi, S., SánchezDelgado, M., Calafell, F., Monk, D., & Senar, J. C. (2015). Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behavior in great tit Parus major. Epigenetics, 10(6), 516– 525. https://doi.org/10.1080/15592 294.2015.1046027 Riyahi, S., Vilatersana, R., Schrey, A. W., Ghorbani Node, H., Aliabadian, M., & Senar, J. C. (2017). Natural epigenetic variation within and among six subspecies of the house sparrow, Passer domesticus. Journal of Experimental Biology, 220(21), 4016– 4023. ht tps://doi. org/10.1242/jeb.169268 Robinson, M. D., Kahraman, A., Law, C. W., Lindsay, H., Nowicka, M., Weber, L. M., & Zhou, X. (2014). Statistical methods for detecting differentially methylated loci and regions. Frontiers in Genetics, 5, 324. https://doi.org/10.3389/fgene.2014.00324 Rochette, N. C., RiveraColón, A. G., & Catchen, J. M. (2019). Stacks 2: Analytical methods for pairedend sequencing improve RADseqbased population genomics. Molecular Ecology, 28(21), 4737– 4754. https://doi.org/10.1111/mec.15253 Romano, A., De Giorgio, B., Parolini, M., Favero, C., Possenti, C. D., Iodice, S., Caprioli, M., Rubolini, D., Ambrosini, R., Gianfranceschi, L., Saino, N., & Bollati, V. (2017). Methylation of the circadian Clock gene in the offspring of a freeliving passerine bird increases with maternal and individual exposure to PM10. Environmental Pollution, 220, 29– 37. https://doi.org/10.1016/j.envpol.2016.08.060 Ross, S. E., Hesselson, D., & Bogdanovic, O. (2021). Developmental accumulation of gene body and transposon NonCpG methylation in the zebrafish brain. Frontiers in Cell and Developmental Biology, 9, 643603. https://doi.org/10.3389/fcell.2021.643603 Roth, T. L., Lubin, F. D., Funk, A. J., & Sweatt, J. D. (2009). Lasting epigenetic influence of earlylife adversity on the BDNF gene. Biological Psychiatry, 65(9), 760– 769. https://doi.org/10.1016/j.biops ych.2008.11.028 Rubenstein, D. R., Skolnik, H., Berrio, A., Champagne, F. A., Phelps, S., & Solomon, J. (2016). Sexspecific fitness effects of unpredictable early life conditions are associated with DNA methylation in the avian glucocorticoid receptor. Molecular Ecology, 25(8), 1714– 1728. https://doi.org/10.1111/mec.13483 Rubi, T. L., Knowles, L. L., & Dantzer, B. (2019). Museum epigenomics: Characterizing cytosine methylation in historic museum specimens. Molecular Ecology Resources, 20(5), 1161– 1170. ht tps://doi. org/10.1111/17550998.13115 Saino, N., Ambrosini, R., Albetti, B., Caprioli, M., De Giorgio, B., Gatti, E., Liechti, F., Parolini, M., Romano, A., Romano, M., Scandolara, C., Gianfranceschi, L., Bollati, V., & Rubolini, D. (2017). Migration phenology and breeding success are predicted by methylation of a photoperiodic gene in the barn swallow. Scientific Reports, 7(1), 45412. https://doi.org/10.1038/srep4 5412 Sarkies, P. (2020). Molecular mechanisms of epigenetic inheritance: Possible evolutionary implications. Seminars in Cell & Developmental Biology, 97, 106– 115. https://doi.org/10.1016/j. semcdb.2019.06.005 Schmieder, R., & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863– 864. https:// doi.org/10.1093/bioin forma tics/btr026 Schrey, A. W., Alvarez, M., Foust, C. M., Kilvitis, H. J., Lee, J. D., Liebl, A. L., Martin, L. B., Richards, C. L., & Robertson, M. (2013). Ecological epigenetics: Beyond MSAFLP. Integrative and Comparative Biology, 53(2), 340– 350. https://doi.org/10.1093/icb/ict012 Schrey, A. W., Coon, C. A. C., Grispo, M. T., Awad, M., Imboma, T., McCoy, E. D., Mushinsky, H. R., Richards, C. L., & Martin, L. B. (2012). Epigenetic variation may compensate for decreased genetic variation with introductions: A case study using house sparrows (Passer domesticus) on two continents. Genetics Research International, 2012, 1– 7. https://doi.org/10.1155/2012/979751 Schultz, M. D., Schmitz, R. J., & Ecker, J. R. (2012). ‘Leveling’ the playing field for analyses of singlebase resolution DNA methylomes. Trends in Genetics, 28(12), 583– 585. https://doi.org/10.1016/j. tig.2012.10.012 Seiler Vellame, D., Castanho, I., Dahir, A., Mill, J., & Hannon, E. (2021). Characterizing the properties of bisulfite sequencing data: Maximizing power and sensitivity to identify betweengroup differences in DNA methylation. BMC Genomics, 22(1), 446. https:// doi.org/10.1186/s1286 402107721 - z Sepers, B., Erven, J. A. M., Gawehns, F., Laine, V. N., & van Oers, K. (2021). Epigenetics and early life stress: Experimental brood size affects DNA methylation in great tits (Parus major). Frontiers in Ecology and Evolution, 9, 609061. https://doi.org/10.3389/fevo.2021.609061 Sepers, B., van den Heuvel, K., Lindner, M., Viitaniemi, H., Husby, A., & van Oers, K. (2019). Avian ecological epigenetics: Pitfalls and promises. Journal of Ornithology, 160(4), 1183– 1203. https://doi. org/10.1007/s1033 601901684 - 5 Shafi, A., Mitrea, C., Nguyen, T., & Draghici, S. (2017). A survey of the approaches for identifying differential methylation using bisulfite sequencing data. Briefings in Bioinformatics, 19(5), 737– 753. https:// doi.org/10.1093/bib/bbx013 Sharif, J., & Koseki, H. (2018). Hemimethylation: DNA’s lasting odd couple. Science, 359(6380), 1102– 1103. https://doi.org/10.1126/scien ce.aat0789 Sheldon, E. L., Schrey, A., Andrew, S. C., Ragsdale, A., & Griffith, S. C. (2018). Epigenetic and genetic variation among three separate introductions of the house sparrow (Passer domesticus) into Australia. Royal Society Open Science, 5(4), 172185. https://doi.org/10.1098/ rsos.172185 Sims, D., Sudbery, I., Ilott, N. E., Heger, A., & Ponting, C. P. (2014). Sequencing depth and coverage: Key considerations in genomic analyses. Nature Reviews Genetics, 15(2), 121– 132. https://doi. org/10.1038/nrg3642 Skinner, M. K. (2008). What is an epigenetic transgenerational phenotype?: F3 or F2. Reproductive Toxicology, 25(1), 2– 6. https://doi. org/10.1016/j.repro tox.2007.09.001 Skinner, M. K., GurerreroBosagna, C., Haque, M. M., Nilsson, E. E., Koop, J. A. H., Knutie, S. A., & Clayton, D. H. (2014). Epigenetics and the evolution of Darwin’s finches. Genome Biology and Evolution, 6(8), 1972– 1989. https://doi.org/10.1093/gbe/evu158 Skvortsova, K., Iovino, N., & Bogdanović, O. (2018). Functions and mechanisms of epigenetic inheritance in animals. Nature Reviews Molecular Cell Biology, 19(12), 774– 790. https://doi.org/10.1038/ s4158 001800742 Solares, E. A., Chakraborty, M., Miller, D. E., Kalsow, S., Hall, K., Perera, A. G., Emerson, J. J., & Hawley, R. S. (2018). Rapid lowcost assembly of the Drosophila melanogaster reference genome using lowcoverage, longread sequencing. G3: Genes|genomes|genetics, 8, 3143– 3154. https://doi.org/10.1534/g3.118.200162 20 | LAINE Et AL. Soulsbury, C. D., Lipponen, A., Wood, K., Mein, C. A., Hoffman, J. I., & Lebigre, C. (2018). Ageand qualitydependent DNA methylation correlate with melaninbased coloration in a wild bird. Ecology and Evolution, 8(13), 6547– 6557. https://doi.org/10.1002/ece3.4132 Srivastava, A., Karpievitch, Y. V., Eichten, S. R., Borevitz, J. O., & Lister, R. (2019). HOME: A histogram based machine learning approach for effective identification of differentially methylated regions. BMC Bioinformatics, 20(1), 253. https://doi.org/10.1186/s1285 90192845y StCyr, S., & McGowan, P. O. (2015). Programming of stressrelated behavior and epigenetic neural gene regulation in mice offspring through maternal exposure to predator odor. Frontiers in Behavioral Neuroscience, 9, 145. https://doi.org/10.3389/ fnbeh.2015.00145 Stoiber, M., Quick, J., Egan, R., Lee, J. E., Celniker, S., Neely, R. K., & Brown, J. (2017). De novo Identification of DNA modifications enabled by genomeguided nanopore signal processing. BioRxiv, 094672. https://doi.org/10.1101/094672 Sun, X., Han, Y. I., Zhou, L., Chen, E., Lu, B., Liu, Y., Pan, X., Cowley, A. W., Liang, M., Wu, Q., Lu, Y., & Liu, P. (2018). A comprehensive evaluation of alignment software for reduced representation bisulfite sequencing data. Bioinformatics, 34(16), 2715– 2723. https://doi. org/10.1093/bioin forma tics/bty174 Sun, Z., Cunningham, J., Slager, S., & Kocher, J.- P. (2015). Base resolution methylome profiling: Considerations in platform selection, data preprocessing and analysis. Epigenomics, 7(5), 813– 828. ht tps://doi. org/10.2217/epi.15.21 Taff, C. C., Campagna, L., & Vitousek, M. N. (2019). Genomewide variation in DNA methylation is associated with stress resilience and plumage brightness in a wild bird. Molecular Ecology, 28(16), 3722– 3737. https://doi.org/10.1111/mec.15186 Takuno, S., & Gaut, B. S. (2012). Bodymethylated genes in Arabidopsis thaliana are functionally important and evolve slowly. Molecular Biology and Evolution, 29(1), 219– 227. https://doi.org/10.1093/ molbe v/msr188 Tang, J., Fang, F., Miller, D. F., Pilrose, J. M., Matei, D., Huang, T.- H.- M., & Nephew, K. P. (2015). Global DNA methylation profiling technologies and the ovarian cancer methylome. In M. Verma (Ed.), Cancer epigenetics: Risk assessment, diagnosis, treatment, and prognosis (pp. 653– 675). Springer. https://doi. org/10.1007/9781493918041_34 Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J., & Prins, P. (2015). Sambamba: Fast processing of NGS alignment formats. Bioinformatics, 31(12), 2032– 2034. https://doi.org/10.1093/bioin forma tics/btv098 Taub, M. A., Corrada Bravo, H., & Irizarry, R. A. (2010). Overcoming bias and systematic errors in next generation sequencing data. Genome Medicine, 2(12), 87. https://doi.org/10.1186/gm208 Thompson, M. J., vonHoldt, B., Horvath, S., & Pellegrini, M. (2017). An epigenetic aging clock for dogs and wolves. Aging, 9(3), 1055– 1068. https://doi.org/10.18632/ aging.101211 Thompson, R. F., Suzuki, M., Lau, K. W., & Greally, J. M. (2009). A pipeline for the quantitative analysis of CG dinucleotide methylation using mass spectrometry. Bioinformatics, 25(17), 2164– 2170. https://doi. org/10.1093/bioin forma tics/btp382 Tomso, D. J., & Bell, D. A. (2003). Sequence context at human single nucleotide polymorphisms: Overrepresentation of CpG dinucleotide at polymorphic sites and suppression of variation in CpG islands. Journal of Molecular Biology, 327(2), 303– 308. https://doi. org/10.1016/s0022 - 2836(03)00120 - 7 Tse, O. Y. O., Jiang, P., Cheng, S. H., Peng, W., Shang, H., Wong, J., Chan, S. L., Poon, L. C. Y., Leung, T. Y., Chan, K. C. A., Chiu, R. W. K., & Lo, Y. M. D. (2021). Genomewide detection of cytosine methylation by single molecule realtime sequencing. Proceedings of the National Academy of Sciences of the United States of America, 118(5), e2019768118. https://doi.org/10.1073/pnas.20197 68118 Tsuji, J., & Weng, Z. (2016). Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data. Briefings in Bioinformatics, 16(6), 938– 952. Vaisvila, R., Ponnaluri, V. K. C., Sun, Z., Langhorst, B. W., Saleh, L., Guan, S., Dai, N., Campbell, M. A., Sexton, B. S., Marks, K., Samaranayake, M., Samuelson, J. C., Church, H. E., Tamanaha, E., Corrêa, I. R., Pradhan, S., Dimalanta, E. T., Evans, T. C., Williams, L., & Davis, T. B. (2021). Enzymatic methyl sequencing detects DNA methylation at singlebase resolution from picograms of DNA. Genome Research, 31(7), 1280– 1289. https://doi.org/10.1101/gr.266551.120 van Gurp, T. P., Wagemaker, N. C. A. M., Wouters, B., Vergeer, P., Ouborg, J. N. J., & Verhoeven, K. J. F. (2016). epiGBS: Referencefree reduced representation bisulfite sequencing. Nature Methods, 13(4), 322– 324. https://doi.org/10.1038/nmeth.3763 van Oers, K., Sepers, B., Sies, W., Gawehns, F., Verhoeven, K. J. F., & Laine, V. N. (2020). Epigenetics of animal personality: DNA methylation cannot explain the heritability of exploratory behavior in a songbird. Integrative and Comparative Biology, 60(6), 1517– 1530. https://doi.org/10.1093/icb/icaa138 Verhoeven, K. J. F., Simonsen, K. L., & McIntyre, L. M. (2005). Implementing false discovery rate control: Increasing your power. Oikos, 108(3), 643– 647. https://doi.org/10.1111/j.00301299.2005.13727.x Verhoeven, K. J. F., vonHoldt, B. M., & Sork, V. L. (2016). Epigenetics in ecology and evolution: What we know and what we need to know. Molecular Ecology, 25(8), 1631– 1638. https://doi.org/10.1111/ mec.13617 Verhulst, E. C., Mateman, A. C., Zwier, M. V., Caro, S. P., Verhoeven, K. J. F., & van Oers, K. (2016). Evidence from pyrosequencing indicates that natural variation in animal personality is associated with DRD4 DNA methylation. Molecular Ecology, 25(8), 1801– 1811. https://doi. org/10.1111/mec.13519 Viitaniemi, H. M., Verhagen, I., Visser, M. E., Honkela, A., van Oers, K., & Husby, A. (2019). Seasonal variation in genomewide DNA methylation patterns and the onset of seasonal timing of reproduction in great tits. Genome Biology and Evolution, 11(3), 970– 983. https:// doi.org/10.1093/gbe/evz044 Vogt, G. (2021). Epigenetic variation in animal populations: Sources, extent, phenotypic implications, and ecological and evolutionary relevance. Journal of Biosciences, 46(1), 24. https://doi.org/10.1007/ s1203 802100138 - 6 Wang, H., Ke, H., Zheng, Y., Lai, J., Luo, Q., & Chen, Q. (2017). A modified bisulfite conversion method for the detection of DNA methylation. Epigenomics, 9(7), 955– 969. https://doi.org/10.2217/ epi20160174 Wang, Q. I., Gu, L., Adey, A., Radlwimmer, B., Wang, W., Hovestadt, V., Bähr, M., Wolf, S., Shendure, J., Eils, R., Plass, C., & Weichenhan, D. (2013). Tagmentationbased wholegenome bisulfite sequencing. Nature Protocols, 8(10), 2022– 2032. https://doi.org/10.1038/ nprot.2013.118 Watson, H., Powell, D., Salmón, P., Jacobs, A., & Isaksson, C. (2021). Urbanization is associated with modifications in DNA methylation in a small passerine bird. Evolutionary Applications, 14(1), 85– 98. https://doi.org/10.1111/eva.13160 Watson, H., Salmón, P., & Isaksson, C. (2019). Dynamic changes in DNA methylation during embryonic and postnatal development of an altricial wild bird. Ecology and Evolution, 9(17), 9580– 9585. https:// doi.org/10.1002/ece3.5480 Weaver, I. C. G., Cervoni, N., Champagne, F. A., D'Alessio, A. C., Sharma, S., Seckl, J. R., Dymov, S., Szyf, M., & Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7(8), 847– 854. https://doi.org/10.1038/nn1276 Weichenhan, D., Imbusch, C. D., Wang, Q., Brors, B., & Plass, C. (2019). Generation of Whole Genome Bisulfite Sequencing Libraries from Very Low DNA Input. In R. Küppers (Ed.), Lymphoma: Methods and protocols (pp. 229– 248). Springer. ht tps://doi. org/10.1007/9781493991518_10 | 21 LAINE Et AL. Wenzel, M. A., & Piertney, S. B. (2014). Finescale population epigenetic structure in relation to gastrointestinal parasite load in red grouse (Lagopus lagopus scotica). Molecular Ecology, 23(17), 4256– 4273. https://doi.org/10.1111/mec.12833 Weyrich, A., Benz, S., Karl, S., Jeschek, M., Jewgenow, K., & Fickel, J. (2016). Paternal heat exposure causes DNA methylation and gene expression changes of Stat3 in Wild guinea pig sons. Ecology and Evolution, 6(9), 2657– 2666. https://doi.org/10.1002/ece3.1993 Weyrich, A., Jeschek, M., Schrapers, K. T., Lenz, D., Chung, T. H., Rübensam, K., Yasar, S., Schneemann, M., Ortmann, S., Jewgenow, K., & Fickel, J. (2018). Diet changes alter paternally inherited epigenetic pattern in male Wild guinea pigs. Environmental Epigenetics, 4(2), 1– 12. https://doi.org/10.1093/eep/dvy011 Weyrich, A., Schüllermann, T., Heeger, F., Jeschek, M., Mazzoni, C. J., Chen, W., Schumann, K., & Fickel, J. (2014). Whole genome sequencing and methylome analysis of the wild guinea pig. BMC Genomics, 15(1), 1036. https://doi.org/10.1186/14712164151036 Wilkinson, G. S., Adams, D. M., Haghani, A., Lu, A. T., Zoller, J., Breeze, C. E., Arnold, B. D., Ball, H. C., Carter, G. G., Cooper, L. N., Dechmann, D. K. N., Devanna, P., Fasel, N. J., Galazyuk, A. V., Günther, L., Hurme, E., Jones, G., Knörnschild, M., Lattenkamp, E. Z., … Horvath, S. (2021). DNA methylation predicts age and provides insight into exceptional longevity of bats. Nature Communications, 12(1), 1615. https://doi.org/10.1038/s4146 702121900 - 2 Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.- W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. ht tps://doi. org/10.1038/sdata.2016.18 Williams, R. J. (2003). Restriction endonucleases: Classification, properties, and applications. Molecular Biotechnology, 23(3), 225– 243. https://doi.org/10.1385/mb:23:3:225 Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. https://doi.org/10.1371/ journ al.pcbi.1005510 Wingett, S. W., & Andrews, S. (2018). FastQ Screen: A tool for multigenome mapping and quality control. F1000Research, 7, 1338. https://doi.org/10.12688/ f1000 resea rch.15931.2 Wreczycka, K., Gosdschan, A., Yusuf, D., Grüning, B., Assenov, Y., & Akalin, A. (2017). Strategies for analyzing bisulfite sequencing data. Journal of Biotechnology, 261, 105– 115. https://doi.org/10.1016/j. jbiot ec.2017.08.007 Wulfridge, P., Langmead, B., Feinberg, A. P., & Hansen, K. D. (2019). Analyzing whole genome bisulfite sequencing data from highly divergent genotypes. Nucleic Acids Research, 47(19), e117. https://doi. org/10.1093/nar/gkz674 Xi, Y., & Li, W. (2009). BSMAP: Whole genome bisulfite sequence MAPping program. BMC Bioinformatics, 10(1), 232. https://doi. org/10.1186/1471210510232 Xu, H., Luo, X., Qian, J., Pang, X., Song, J., Qian, G., Chen, J., & Chen, S. (2012). FastUniq: A fast de novo duplicates removal tool for paired short reads. PLoS One, 7(12), e52249. https://doi.org/10.1371/ journ al.pone.0052249 Yin, Y., Morgunova, E., Jolma, A., Kaasinen, E., Sahu, B., KhundSayeed, S., Das, P. K., Kivioja, T., Dave, K., Zhong, F., Nitta, K. R., Taipale, M., Popov, A., Ginno, P. A., Domcke, S., Yan, J., Schübeler, D., Vinson, C., & Taipale, J. (2017). Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science, 356(6337), eaaj2239. https://doi.org/10.1126/scien ce.aaj2239 Zabet, N. R., Catoni, M., Prischi, F., & Paszkowski, J. (2017). Cytosine methylation at CpCpG sites triggers accumulation of nonCpG methylation in gene bodies. Nucleic Acids Research, 45(7), 3777– 3784. https://doi.org/10.1093/nar/gkw1330 Zemach, A., McDaniel, I. E., Silva, P., & Zilberman, D. (2010). Genomewide evolutionary analysis of eukaryotic DNA methylation. Science, 328(5980), 916– 919. https://doi.org/10.1126/scien ce.1186366 Zhang, Y., Baheti, S., & Sun, Z. (2018). Statistical method evaluation for differentially methylated CpGs in base resolution nextgeneration DNA sequencing data. Briefings in Bioinformatics, 19(3), 374– 386. https://doi.org/10.1093/bib/bbw133 Zhou, J., Zhao, M., Sun, Z., Wu, F., Liu, Y., Liu, X., He, Z., He, Q., & He, Q. (2020). BCREval: A computational method to estimate the bisulfite conversion ratio in WGBS. BMC Bioinformatics, 21(1), 38. https:// doi.org/10.1186/s1285 90193334z Zhou, L. I., Ng, H. K., DrautzMoses, D. I., Schuster, S. C., Beck, S., Kim, C., Chambers, J. C., & Loh, M. (2019). Systematic evaluation of library preparation methods and sequencing platforms for highthroughput whole genome bisulfite sequencing. Scientific Reports, 9(1), 10383. https://doi.org/10.1038/s4159 801946875 - 5 Ziller, M. J., Müller, F., Liao, J., Zhang, Y., Gu, H., Bock, C., & Meissner, A. (2011). Genomic Distribution and InterSample Variation of NonCpG Methylation across Human Cell Types. PLOS Genetics, 7(12), e1002389. https://doi.org/10.1371/journ al.pgen.1002389 Ziller, M. J., Hansen, K. D., Meissner, A., & Aryee, M. J. (2015). Coverage recommendations for methylation analysis by wholegenome bisulfite sequencing. Nature Methods, 12(3), 230– 232. https://doi. org/10.1038/nmeth.3152 Zimmer, C., Larriva, M., Boogert, N. J., & Spencer, K. A. (2017). Transgenerational transmission of a stresscoping phenotype programmed by earlylife stress in the Japanese quail. Scientific Reports, 7(1), 46125. https://doi.org/10.1038/srep4 6125 How to cite this article: Laine, V. N., Sepers, B., Lindner, M., Gawehns, F., Ruuskanen, S., & van Oers, K. (2022). An ecologist's guide for studying DNA methylation variation in wild vertebrates. Molecular Ecology Resources, 00, 1– 21. https://doi.org/10.1111/17550998.13624