ApplicationMicrobiome-driven breeding, as a cost-effective strategy to mitigate methane (CH4)emissions, is recommended to be used in a multiple trait model with correlated productiontraits, as it substantially increased the accuracy of estimation of breeding values (EBVs) andthus enhances selection response.IntroductionRoehe et al. (2016) found that rumen microbial gene abundances are closely linked to CH4emissions and highlighted these as a highly informative proxy for breeding low CH4 emittingcattle. Later, Martinez-Alvaro et al. (2022) demonstrated the effectiveness of usingmicrobial genes in microbiome-driven breeding to reduce CH4 yield (expressed as g ofCH4/kg of dry matter intake). In this study, we applied microbiome-driven breeding formitigating daily CH4 emissions (g of CH4 /day), and incorporated information from keyperformance traits genetically associated with CH4 emissions, such as daily feed intake (DFI),average daily gain (ADG) and carcass weight (CCW).Materials and MethodsThe experiment was conducted following the UK Animals Act 1986 and was approved by theAnimal Experiment Committee of SRUC. Three hundred sixty-three steers raised under thesame housing conditions on the same research farm were used in this project. The animalswere balanced for different breeds (Aberdeen Angus, Limousin, Charolais crosses andpurebred Luing) and basal diets (two diets of 520:480 and 920:80 forage:concentrate ratios).Blood and rumen fluid samples were collected at slaughter. Microbial DNA sequence readsfrom rumen fluid samples were aligned to the Kyoto Encyclopedia of Genes and Genomesdatabase, resulting in the identification of 3362 microbial genes. To account for thecompositionality of microbiome data, microbial gene abundance data were transformedusing the additive log-ratio method. CH4 production was measured individually for 285 ofthe 363 animals over a 48-hour period using six respiration chambers and expressed as CH4emissions per day (CH4p).Firstly, we conducted multiple bivariate genomic (37K SNPs) analyses to obtain geneticvariances and covariances between CH4p and microbial genes. Secondly, we identified themost informative microbial genes that yielded the largest correlated response in CH4p.Thirdly, we conducted genomic bivariate analyses between the identified microbial genesand the performance traits DFI, ADG, CCW to obtain the genomic (co)variances. Lastly, weused these genomic (co)variances for different breeding strategies to reduce CH4production: 1) univariate analyses, using measured CH4p only (CH4p measured), 2)multivariate analysis using only the most informative microbial gene abundances geneticallycorrelated with CH4p, i.e., microbiome-driven breeding (MDB.43), 3) multivariate analysis,including DFI, ADG, CCW, and measured CH4p (Four traits measures), and 4) multivariateanalysis, including DFI, ADG, CCW, and predicted CH4p using microbiome-driven breeding(Three traits & MDB.43). Three selection intensities (1.159, 1.400, and 1.755) wereconsidered for each strategy.ResultsWe identified 43 informative microbial genes, of which 17 were positively geneticallycorrelated with CH4p (rgCH4p, ranging from 0.45 to 0.80) and 26 microbial genes werenegatively correlated (rgCH4p ranged from -0.32 to -0.75) with CH4p. All correlations had morethan 80% probability of being greater or lower than zero (Pr0). The heritability of thesemicrobial genes ranged from 0.19 to 0.50.Of all performance traits, DFI showed strong positive genetic correlations (ranging from 0.84to 0.93, Pr0 = 100%) with ADG, CCW, and CH4p. CCW had a marginally higher geneticcorrelation with CH4p (0.61, Pr0 = 96%) than ADG (0.58, Pr0 = 96%).Selection using microbiome-driven breeding (MDB.43) resulted in similar selectionresponses to those based on measured CH4p using respiration chambers (Figure 1, -17.76 ±2.30% vs -16.76 ± 2.26% at highest selection intensity). Including measured CH4p in themultiple-trait model with ADG, DFI and CCW increased the accuracy of the EBVs from 0.63 ±0.15 to 0.81 ± 0.06 and the selection response to -20.59 ± 2.13%). Replacing measured CH4pby microbiome-driven breeding resulted in a further increase in response at -23.12 ± 2.88%.Figure 1. Methane mitigation using different selection strategies, considering threeselection intensities (1.159, 1.400, 1.755, equivalent to selection of the best 30%, 20% and10% of the population, respectively)ConclusionsMicrobiome-driven breeding for reduced CH4p was successfully integrated into a multipletrait model with production traits by considering all genetic and residual covariancesbetween microbial gene abundances and those traits. Since microbiome-driven breeding issubstantially more cost-effective than using measured CH4 emissions and provide at leastsimilar selection response to that obtained using the gold standard method of respirationchambers, this methodology provides large potential to effectively reduce this highly potentGHG gas in beef populations.ReferencesRoehe, R., Dewhurst, R.J., Duthie, C.A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J.,Waterhouse, A., Freeman, T.C., Watson, M. and Wallace, R.J., 2016. Bovine host geneticvariation influences rumen microbial methane production with best selection criterion forlow methane emitting and efficiently feed converting hosts based on metagenomic geneabundance. PLOS Genetics 12, e1005846.Martínez-Álvaro, M., Auffret, M.D., Duthie, C.A., Dewhurst, R.J., Cleveland, M.A., Watson,M. and Roehe, R., 2022. Bovine host genome acts on rumen microbiome function linked tomethane emissions. Communications Biology 5, 35
