A Recursive Model Approach to Include Epigenetic Effects in Genetic Evaluations Using Simulated DNA Methylation...

Area of Science:

• Animal genetics and genomics
• Epigenetics and quantitative genetics
• Bioinformatics and statistical modeling

Background:

• DNA methylation is a key epigenetic mechanism influencing gene regulation and animal traits.
• Advances in high-throughput sequencing enable large-scale, affordable capture of methylation data.
• Integrating multiomic data into genetic evaluation models is crucial for improving animal breeding.

Purpose of the Study:

• To adapt and evaluate a multiomic model (GOBLUP) for incorporating DNA methylation data into genetic evaluations.
• To assess the model's ability to separate additive genetic and epigenetic variances.
• To develop and validate an Estimated Epigenetic Value (EEV) for improved breeding decisions.

Main Methods:

• Simulated methylation profiles for 13,183 genotyped animals based on data from six dairy cows.
• Treated liability to methylation as an additive trait and simulated a methylation-moderated trait.
• Adapted the GOBLUP multiomic model and compared it with the traditional BLUP method.

Main Results:

• GOBLUP accurately estimated heritability for methylation liability and epigenetics-moderated traits.
• The model effectively separated additive genetic and epigenetic variances.
• The novel Estimated Epigenetic Value (EEV) showed higher correlations with true breeding values than traditional EBVs.

Conclusions:

• The GOBLUP multiomic model effectively integrates DNA methylation data for improved genetic evaluations in animal breeding.
• Accounting for genetic liability to DNA methylation enhances breeding decisions and selection accuracy.
• Cost-effective simultaneous acquisition of genetic and epigenetic data will further boost breeding accuracy.