Microbiome‐Informed Genomic Prediction of CH4:CO2 Ratio in Holstein Dairy Cattle Using 16S rRNA Profiles and SNP Markers

Ruminant animals produce methane as a by‐product of microbial fermentation in the rumen, with cattle being the largest contributors among ruminants. Methane emissions may be influenced directly by the host's genetics and indirectly through host‐mediated effects on rumen microbiome composition. In this study, we investigated whether modelling both direct and indirect genetic effects could improve the genomic prediction of breeding values for methane emissions. We used genotype, methane CH 4 :CO 2 ratio and 16S rRNA gene amplicon sequencing data (16S), including 16S genus (16S‐G), 16S species (16S‐S) from Danish Holstein cows. A conventional genomic best linear unbiased prediction (GBLUP) model was used as a benchmark and compared with a microbiome‐informed GBLUP approach based on three linked linear mixed models for the prediction of breeding values. In GMBLUP, the first model regresses methane emissions on three random animal effects (archaeal 16S, bacterial 16S and polygenic effects) using corresponding microbiome and genomic relationship matrices. The second and third models separately regress archaeal and bacterial 16S on random animal effects defined by the genomic relationship matrix. We evaluated prediction accuracy using five‐fold cross‐validation. The GBLUP model, which used only genomic marker information, achieved a mean accuracy of 0.20 across the five folds when evaluated in the animal subsets corresponding to the 16S‐G and 16S‐S datasets, while GMBLUP showed higher accuracy, particularly for the 16S‐G dataset, with a mean accuracy of 0.48. These results highlight that incorporating microbiome information into genomic prediction models may improve predictive performance for the CH 4 :CO 2 ratio, although further validation in larger and independent populations is required before practical implementation in breeding programmes.