The objective of this study was to understand and measure epigenetic changes associated with the occurrence of CHDs by utilizing the discordant monozygotic twin model. A unique set of monozygotic twins discordant for double-outlet right ventricles (DORVs) was used for this multiomics study. The cardiac and muscle tissue samples from the twins were subjected to whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and liquid chromatography-tandem mass spectrometry analysis. Sporadic DORV cases and control fetuses were used for validation. Global hypomethylation status was observed in heart tissue samples from the affected twins. Among 36,228 differentially methylated regions (DMRs), 1097 DMRs involving 1039 genes were located in promoter regions. A total of 419 genes, and lncRNA–mRNA pairs involved 30 genes, and 62 proteins were significantly differentially expressed. Multiple omics integrative analysis revealed that five genes, including BGN, COL1A1, COL3A1, FBLN5, and FLAN, and three pathways, including ECM-receptor interaction, focal adhesion and TGF-β signaling pathway, exhibited differences at all three levels. This study demonstrates a multiomics profile of discordant twins and explores the possible mechanism of DORV development. Global hypomethylation might be associated with the risk of CHDs. Specific genes and specific pathways, particularly those involving ECM–receptor interaction, focal adhesion and TGF–β signaling, might be involved in the occurrence of CHDs.

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Different tools have been developed to extract differentially methylated regions (DMRs), often built upon assumptions from mammalian data. Here, we present MethylScore, a pipeline to analyse WGBS data and to account for the substantially more complex and variable nature of plant DNA methylation. MethylScore uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation. It processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. 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 1,001 Genomes dataset to unveil known and unknown genotype–epigenotype associations .