The expanding scope and scale of next generation sequencing experiments in ecological plant epigenetics brings new challenges for computational analysis. Existing tools built for model data may not address the needs of users looking to apply these techniques to non-model species, particularly on a population or community level. Here we present a toolkit suitable for plant ecologists working with whole genome bisulfite sequencing; it includes pipelines for mapping, the calling of methylation values and differential methylation between groups, epigenome-wide association studies, and a novel implementation for both variant calling and discriminating between genetic and epigenetic variation.

Summary: Due to the accelerating climate change, it is crucial to understand how plants adapt to rapid environmental changes. Such adaptation may be mediated by epigenetic mechanisms like DNA methylation, which could heritably alter phenotypes without changing the DNA sequence, especially across clonal generations. However, we are still missing robust evidence of the adaptive potential of DNA methylation in wild clonal populations. Here, we studied genetic, epigenetic and transcriptomic variation of Fragaria vesca, a predominantly clonally reproducing herb. We examined samples from 21 natural populations across three climatically distinct geographic regions, as well as clones of the same individuals grown in a common garden. We found that epigenetic variation was partly associated with climate of origin, particularly in non‐CG contexts. Importantly, a large proportion of this variation was heritable across clonal generations. Additionally, a subset of these epigenetic changes affected the expression of genes mainly involved in plant growth and responses to pathogen and abiotic stress. These findings highlight the potential influence of epigenetic changes on phenotypic traits. Our findings indicate that variation in DNA methylation, which can be environmentally inducible and heritable, may enable clonal plant populations to adjust to their environmental conditions even in the absence of genetic adaptation.

Natural plant populations often harbour substantial heritable variation in DNA methylation. However, a thorough understanding of the genetic and environmental drivers of this epigenetic variation requires large-scale and high-resolution data, which currently exist only for a few model species. Here, we studied 207 lines of the annual weedThlaspi arvense(field pennycress), collected across a large latitudinal gradient in Europe and propagated in a common environment. By screening for variation in DNA sequence and DNA methylation using whole-genome (bisulfite) sequencing, we found significant epigenetic population structure across Europe. Average levels of DNA methylation were strongly context-dependent, with highest DNA methylation in CG context, particularly in transposable elements and in intergenic regions. Residual DNA methylation variation within all contexts was associated with genetic variants, which often co-localized with annotated methylation machinery genes but also with new candidates. Variation in DNA methylation was also significantly associated with climate of origin, with methylation levels being lower in colder regions and in more variable climates. Finally, we used variance decomposition to assess genetic versus environmental associations with differentially methylated regions (DMRs). We found that while genetic variation was generally the strongest predictor of DMRs, the strength of environmental associations increased from CG to CHG and CHH, with climate-of-origin as the strongest predictor in about one third of the CHH DMRs. In summary, our data show that natural epigenetic variation inThlaspi arvenseis significantly associated with both DNA sequence and environment of origin, and that the relative importance of the two factors strongly depends on the sequence context of DNA methylation.T.arvenseis an emerging biofuel and winter cover crop; our results may hence be relevant for breeding efforts and agricultural practices in the context of rapidly changing environmental conditions.

Bisulfite sequencing is a widely used technique for determining DNA methylation and its relationship with epigenetics, genetics, and environmental parameters. Various techniques were implemented for epigenome-wide association studies (EWAS) to reveal meaningful associations; however, there are only very few plant studies available to date. Here, we developed the EpiDiverse EWAS pipeline and tested it using two plant datasets, from P. abies (Norway spruce) and Q. lobata (valley oak). Hence, we present an EWAS implementation tested for non-model plant species and describe its use.

Summary: - Environmental changes can trigger phenotypic variation in plants through epigenetic mechanisms, but strong genetic influences make it difficult to isolate and study epigenetic effects. Clonal trees with low genetic variation, such as the Lombardy poplar (Populus nigra cv. ‘Italica’ Duroi), offer a unique system to study epigenetic variation associated with the environment. - We collected cuttings (ramets) of Lombardy poplar along a wide geographical range in Europe. We performed whole-genome-bisulfite sequencing of 164 ramets grown in a common garden and of a subset of 35 of the original parental individuals. Using historical bioclimatic data, we tested the relationship between DNA methylation and climatic gradients. - We found that average methylation levels in TEs and promoter regions correlate with biologically relevant climatic variables. Furthermore, we observed that DNA methylation was transmitted to the next clonal generation, but a fraction of the methylome changed relatively fast when comparing the parental individuals with the clonal offspring. - Our results suggest that the poplar methylome is a dynamic layer of information that can be transmitted to the clonal offspring and potentially affect how poplars acclimate to new environmental conditions.

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore .