A cost-effective method for combining the power of genetic and epigenetic selection in animal production.

ABSTRACT: Traditional breeding programs have largely focused on genetics, often overlooking environmental and epigenetic influences on phenotypic variability. Current methods for developing epigenetic biomarkers (EBs) with machine learning (ML) algorithms require extensive data, making them costly and time-intensive. In this study, using a fish as a model, we analysed ~500 000 CpG loci in samples from 60 different families to develop EBs for broodstock selection. To address limited sample sizes at the sequencing stage, we combined careful sample selection, statistical filtering, and various feature selection and ML algorithms. As a result, we identified three heritable CpGs sites in sire sperm associated with three key performance indicators in their offspring: biomass, fast-growing females, and res

SUBMITTER: Sanchez-Baizan N 

PROVIDER: S-EPMC12581941 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature