Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Rumen microbiota has been previously related to phenotypic complex traits of relevance in dairy cattle. The joint analysis the host's genetic background and its can be statistically modelled using similarity matrices between microorganism communities different hosts. Microbiota relationship (K) enable considering whole cumbersome interrelations taxa, rather than analyzing single taxa one at time. Several methods have proposed ordinate these matrices. aim this study was compare performance twelve K built from microbiome distance metrics, within a variance component estimation framework for methane concentration Phenotypic, genomic rumen information simulations (n = 1000) real data (cows 437) were analyzed. Four models considered: an additive model (GBLUP), (MBLUP), effects (HBLUP) genetic, × interaction (HiBLUP). Results simulation obtained 25 replicates. simulated suggested that Ks with flattened off-diagonal elements more accurate components all compared included (MBLUP, HBLUP HiBLUP). Multidimensional scaling (MDS), redundancy (RDA) constrained correspondence (CCA) performed better estimate heritability microbiability. including MDS, RDA CCA also most plausible set, according deviance criteria (DIC). Real analyzed under same as simulation. HiBLUP. Estimates variated depending on K; (0.15–0.17) microbiability (0.15–0.21) lower proportion attributable host-microbiome holobiont effect (0.42–0.59), which we defined here “holobiability”. holobiability HiBLUP 0.01 0.15 larger explained sum without them, HBLUP, K. findings support potential information. Accounting hologenome (genomic microbiome) could improve accuracy relevant livestock science.