Genetic and environmental drivers of large-scale epigenetic variation in Thlaspi arvense

Abstract

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 weed Thlaspi 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 in Thlaspi arvense is 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. arvense is 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.

Fig 1. Geographic distribution and population structure of the 207 sampled Thlaspi arvense lines.

(A) Geographic locations of the 36 populations. The background colours are gridded satellite data of average daily temperature (T.) from the Copernicus programme [44]. (B) PCA plots of all 207 lines based on DNA sequence (“Genetic”) and DNA methylation in different sequence contexts (“mCG”, “mCHG” and “mCHH”).

Fig 2. Average methylation and distributions of methylation values for different sequence contexts and genomic features in T. arvense.

(A) Weighted average methylation levels of genomic features; violin plots represent variation between lines. (B) Distributions of individual methylation values for coding sequences (CDS), promoters and transposable elements (TEs) obtained averaging across all 207 lines.

Fig 3. Genome-wide association analyses for genetic control of average DNA methylation.

We show only the results for intergenic methylation; for full results see S3 Fig. (A) Manhattan plots, with the top variants labelled with the neighbouring genes potentially affecting methylation. The genome-wide significance (horizontal red lines), was calculated based on unlinked variants as in Sobota et al. (2015) [49], the suggestive-line (blue) corresponds to–log(p) = 5. (B) Corresponding to each Manhattan plot on the left, enrichment of a priori candidates and expected false discovery rates (both as in Atwell et al. 2010 [50]) for stepwise significance thresholds. (C) The allelic effects of the red-marked variants in the corresponding Manhattan plots on the left, with genotypes on the x-axes and the average methylation on the y-axes. (D) The candidate genes marked in panel A, their putative functions and distances to the top variant of the neighbouring peaks. Bold font indicates a priori candidates that were included in the enrichment analyses.

Fig 4. Climate-methylation associations.

A Heatmap of the correlations between mean methylation and different climatic variables (Precip: precipitation; Temp: temperature), separately for different sequence contexts and genomic features (prom: promoter; interg: intergenic; TEs: Transposable Elements; CDS: coding sequences). Both rows and columns are clustered by their multivariate similarity in association patterns.

Fig 5. Genetic versus environmental predictors of DMR variance.

(A) The variance in DMR weighted methylation explained by genetic similarity in cis, genetic similarity in trans and climatic similarity, averaged across all DMRs. (B) The number of DMRs identified in different genomic features and sequence contexts, and (C) the fractions of these individual DMRs where cis-variation, trans-variation or climatic variation are the major predictors. DMRs where none of the three predictors explained >10% of the variance are classified as “unexplained”.