Inferring and modeling inheritance of differentially methylated changes across multiple generations

Area of Science:

• Epigenetics
• Genomics
• Toxicology

Background:

• High-throughput sequencing identifies genome-wide differentially methylated sites (DMS) and regions (DMR).
• Treatment-induced DMS transmission across generations is increasingly recognized.
• Analyzing multi-generational methylation changes is challenging due to a lack of robust statistical methods for significance evaluation.

Purpose of the Study:

• To introduce a novel statistical method, methylInheritance, for evaluating conserved DMS across generations.
• To develop a simulation package, methInheritSim, for assessing the performance and power of the methylInheritance method.
• To apply methylInheritance to identify treatment-induced inherited methylation changes in a rat model.

Main Methods:

• Development of methylInheritance, a method based on Monte Carlo sampling for assessing inherited DMS.
• Creation of methInheritSim, a simulation package for evaluating experimental designs and method performance.
• Application of methylInheritance to a DNA methylation dataset from rats exposed to persistent organic pollutants (POPs) and their descendants.

Main Results:

• methylInheritance efficiently identifies treatment-induced inherited methylation changes.
• Two intergenerationally conserved DMS were identified at transcription start sites (TSS), with one persisting transgenerationally.
• Three transgenerationally conserved DMR were identified in intra- or intergenic regions.

Conclusions:

• The methylInheritance method provides a sound statistical framework for analyzing multi-generational epigenetic inheritance.
• This study demonstrates the identification of transgenerational epigenetic inheritance of methylation changes induced by early-life POP exposure.
• The findings highlight the potential for environmental factors to induce heritable epigenetic modifications.