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. 2018 Sep 10;19(1):128.
doi: 10.1186/s13059-018-1516-z.

DNA methylation footprints during soybean domestication and improvement

Yanting Shen  1   2 , Jixiang Zhang  1   2 , Yucheng Liu  1   2 , Shulin Liu  1   2 , Zhi Liu  1   2 , Zongbiao Duan  1   2 , Zheng Wang  1 , Baoge Zhu  1 , Ya-Long Guo  3   2 , Zhixi Tian  4   5
Affiliations

DNA methylation footprints during soybean domestication and improvement

Yanting Shen et al. Genome Biol. .

Abstract

Background: In addition to genetic variation, epigenetic variation plays an important role in determining various biological processes. The importance of natural genetic variation to crop domestication and improvement has been widely investigated. However, the contribution of epigenetic variation in crop domestication at population level has rarely been explored.

Results: To understand the impact of epigenetics on crop domestication, we investigate the variation of DNA methylation during soybean domestication and improvement by whole-genome bisulfite sequencing of 45 soybean accessions, including wild soybeans, landraces, and cultivars. Through methylomic analysis, we identify 5412 differentially methylated regions (DMRs). These DMRs exhibit characters distinct from those of genetically selected regions. In particular, they have significantly higher genetic diversity. Association analyses suggest only 22.54% of DMRs can be explained by local genetic variations. Intriguingly, genes in the DMRs that are not associated with any genetic variation are enriched in carbohydrate metabolism pathways.

Conclusions: This study provides a valuable map of DNA methylation across diverse accessions and dissects the relationship between DNA methylation variation and genetic variation during soybean domestication, thus expanding our understanding of soybean domestication and improvement.

Keywords: DNA methylation; Domestication; Soybean.

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Figures

Fig. 1
Fig. 1
Accession information and methylation sequencing. a Geographical distribution and phylogenetic tree of the 45 sequenced accessions. b Summary of whole-genome bisulfite sequencing. Statistics for reads pairs were the sum of all sequenced accessions, statistics for genome coverage and depth were the average of all sequenced accessions
Fig. 2
Fig. 2
Differentially methylated region (DMR) detection and comparison to DNA sequence regions under selection (DSRs). a DMRs detected in soybean domestication and improvement. b Genome-wide distributions of DMRs and DSRs. c Genomic compositions of DMRs and DSRs. TE regions were defined as regions masked by RepeatMasker using soybean annotated TEs as the library. d Length comparison between DMRs and DSRs. ***p < 0.001 by ANOVA. e Genetic diversity comparisons between DMRs, DSRs, and non-selected regions (NSRs) from different genomic regions. ANOVA were performed for each genomic region. Different letters at the top of each column indicate significant differences by ANOVA (p < 0.001). f Genetic diversity comparisons between high methylation variation windows (MVWs) and low methylation variation windows for different genomic regions. ***p < 0.001 by t-test
Fig. 3
Fig. 3
Characterization of DMRs in different cytosine contexts. a Overlapping DMRs from different methylation contexts in the domestication and improvement processes. b Genomic compositions of different DMRs types. c Length comparisons between different DMR types. Different letters at the top of each column indicate significant differences by ANOVA (p < 0.001). d Genetic diversity comparisons among different DMR types from different genomic regions. Mann–Whitney U-test was performed between NSR and other DMR types for different genomic regions. ***p < 0.001, **p < 0.01
Fig. 4
Fig. 4
Local association study between DMRs and genetic variations. a Summary of the associations between DMRs and local siRNA expression variation, TE variation and SNPs. b Plot of methylation levels (x-axis) and siRNA expression values (y-axis). Methylation level and siRNA RPM were mean-centered and normalized. c Correlation between DMR methylation and TE variant state at different distances. The DMR/TE variation pairs were divided into five groups according to the distance between DMR and TE variant. Different letters at the top of each column indicate significant differences by ANOVA (p < 0.001). d The proportion of DMRs associated and not associated with local genetic variations (top) and the proportion of different DMR types (bottom left) and different genetic variation combinations (bottom right) for locally associated DMRs. e An example (Dos_CHG-DMR, Chr14:45,221,203–45,222,398) DMR that was simultaneously associated with local siRNA expression, TE variant, and SNP sites. Rectangles in the TE variant panel indicate reads supporting the TE variant and rectangles in the SNP panel indicate SNP sites
Fig. 5
Fig. 5
KEGG enrichment analysis of “pure Dos_CG-DMR” overlapping genes. a The pathways significantly enriched for “pure Dos_CG-DMR” overlapping genes. Pathways that contained > 5 overlapping genes with enrichment q-values < 0.05 were considered significantly enriched. b An integrated carbohydrate metabolism pathway composed of pathways enriched in “pure Dos_CG-DMR” overlapping genes. c Genome enrichment of six key enzymes in carbohydrate metabolism pathways. The background for “pure Dos_CG-DMR” overlapping genes was 1503 and that for genome annotation genes was 55,583; enrichment was analyzed by Fisher’s exact test
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