# Harnessing RUMIGEN EpiChip data to predict dairy cow performance

M.P. Sanchez, Florian Besnard, Valentin Costes, Corentin Fouéré, Clotilde Patry, Chrystelle Le Danvic, Gabriel Costa Monteiro Moreira, Alexandre Asset, Mekki Boussaha, Hélène Jammes, et al.

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To cite this version:

M.P. Sanchez, Florian Besnard, Valentin Costes, Corentin Fouéré, Clotilde Patry, et al. Harnessing RUMIGEN EpiChip data to predict dairy cow performance. 76th Annual Meeting of the European Federation of Animal Science, EAAP, Aug 2025, Innsbrück, Austria. **hal-05238688**

HAL Id: hal-05238688

https://hal.inrae.fr/hal-05238688v1

Submitted on 3 Sep 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Harnessing RUMIGEN EpiChip data to predict dairy cow performance

M. P. Sanchez¹, F. Besnard¹,², V. Costes², C. Fouéré¹,², C. Patry², C. Le Danvic², G. Costa Monteiro Moreira³, A. Asset³, M. Boussaha¹, H. Jammes³, S. Fritz¹,², H. Kiefer³, D. Boichard¹

¹ Université Paris-Saclay, INRAE, AgroParisTech, GABI, Domaine de Vilvert, 78350 Jouy-en-Josas, France, ² Eliance, 149 Rue de Bercy, 75012 Paris, France, ³ Université Paris-Saclay, INRAE, Ecole Nationale Vétérinaire d'Alfort, BREED, Domaine de Vilvert, 78350 Jouy-en-Josas, France

The DNA methylation BeadChip (EpiChip), developed in the H2020 RUMIGEN project, offers a powerful approach to understanding the epigenetic factors influencing dairy cow performance. This study used the RUMIGEN EpiChip on blood samples from 3,087 Holstein cows to evaluate the extent to which epigenetic biomarkers may help predict cow phenotypes. Key performance indicators such as milk production, fertility, and udder health were analyzed using a mixed model that included the random effects of genetic and/or epigenetic values. Genetic covariance structure was computed from SNP genotypes obtained from the EuroGMD chip, while epigenome covariance structure was derived from methylation similarity between individuals estimated from EpiChip data, after adjusting data from leucocyte composition. Three distinct models were applied to estimate genetic and/or epigenetic variances, all including environmental effects along with i) a genetic component, ii) an epigenetic component, or iii) both genetic and epigenetic components. The population of cows was divided into a training and validation set to estimate prediction accuracy across the three models. Results presented in detail include the proportion of epigenetic variance relative to the total variance for all traits considered in this study. Specifically, we compare the performance of the three models in terms of accuracy and bias in predicting cow performance. These findings pave the way for EpiChip data to improve predictive capacity beyond conventional genetic and environmental factors. This provides a more comprehensive framework for animal performance management, supporting more informed breeding and management decisions to advance efficiency and sustainability in the dairy industry. This project has received funding from the European Union's Horizon 2020 Programme for Research & Innovation under grant agreement n°101000226 (RUMIGEN) and from APIS-GENE (PolyPheme).

Applying the RUMIGEN Methylation Array to Identify Epigenetic Markers Associated to fetal development during maternal lactation in Cattle

A. López-Catalina¹, M. Gutiérrez-Rivas¹, Ó. González-Recio¹

¹ INIA-CSIC, Crta. de la Coruña km 7.5, 28040 Madrid, Spain

This work was supported by European Union's Horizon 2020 Research and Innovation Program, 101000226. Genotyping arrays have become an essential tool for genetic evaluations. Positions of interest are selected to estimate individuals' genetic merit and identify the best parents to enhance the rate of genetic gain. Alongside improvements in phenotype recording and computation, the genetic rate of gain has doubled over the past decade. However, genomic information and genetic biomarkers such as SNPs explain only part of the phenotypic variance and the heritability of economically relevant traits in livestock. In this context, epigenetic biomarkers, particularly DNA methylation marks, are being explored for their potential role in livestock breeding programmes. These marks provide insights into the heritability of traits and the environmental influences on gene expression, ultimately improving selection strategies. To assess the impact of methylation marks, we epi-genotyped 470 samples from new-born calves from lactating or non-lactating cows using the H2020 RUMIGEN methylation array designed within the RUMIGEN consortium and manufactured by Illumina Inc., which includes approximately 44,000 CpGs, of which 37,337 passed quality control filters. We combined this epigenetic data with genomic information from a 60k SNP chip to identify CpGs associated with maternal lactation status and estimate the genetic control of DNA methylation at different genomic regions using the GibbsF90+ software. We identified 155 CpGs significantly associated with maternal lactation status (Bonferroni-adjusted p-value < 0.05). These CpGs were linked to genes such as RCAN3 and NOBOX, which are involved in nervous system development, and pre-weaning survival; ALDH1A3, associated with growth traits; WNT7A, which plays a role in embryo development; and several HOX family genes, which are key regulators of embryonic development and body plan organisation. The heritability of DNA methylation at these CpGs ranged from 0.30 to 0.95. Given the nature of DNA methylation marks, this tool could enhance our knowledge of the epigenetic importance on dairy cattle production and may serve as a tool to personalized breeding strategies.