Multi-omics analysis

Improving genomic prediction for feed efficiency traits in beef cattle using microbiome as an intermediate omics

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Abstract

Feed efficiency in beef cattle has a significant impact on the beef industry, as it represents more than 60% of the total production cost. Among the different factors that affect feed efficiency traits, the rumen microbiome plays a key role. Recent microbiome research has revealed the rumen microbiome to be both heritable and influential for feed efficiency traits such as average daily gain (ADG), average daily dry matter intake (ADDMI), and feed conversion ratio (FCR = ADDMI/ADG). Our study evaluates the prediction accuracy of incorporating rumen microbiome data as an intermediate layer of omics between genotypes and phenotypes via the Neural Network Mixed Model (NNMM) method, which hierarchically models the genotype-microbiome-phenotype relationship as a unified multi-layer regulatory network. We analyzed 722 beef cattle with three feed efficiency traits (FCR, ADG, and ADDMI), microbiome, and genotype data. After quality control, 922,208 SNPs were retained for analysis. Rumen microbiome data were generated through shotgun metagenomic sequencing, producing 16,583 high-quality open reading frames (ORFs). To reduce the computational burden of NNMM given the high dimensionality of microbiome data, we tested (1) principal components (PCs) of microbiome data capturing 50%, 75%, 90%, and 100% of microbiome variance (yielding 8, 77, 307, and 705 PCs, respectively), and (2) feed efficiency traits-relevant microbiome features (top 77/307/705 ORFs) from microbiome-wide association studies. Two benchmark models were used to compare the prediction accuracy of NNMM: (1) Genomic Best Linear Unbiased Prediction (GBLUP) and (2) a two-kernel genomic-microbiome model (G+M) incorporating microbiome as an additional non-genetic random effect in a linear mixed model. Model performance was evaluated using cross-validation with 20 replicates of a 70/30 train-test split, we masked test phenotypes during training and genomic prediction accuracy was calculated as Pearson's correlation between the estimated genomic breeding value and observed phenotypes in the withheld test set. Our results showed that NNMM consistently outperformed other approaches. In detail, for low-heritability traits ADG (h² = 0.19) and FCR (h² = 0.13), significant improvements were observed when trait-relevant ORFs were used as intermediate omics in NNMM: the NNMM model improved prediction accuracy for ADG from 0.08 (GBLUP) to 0.28 and for FCR from 0.08 (G+M) to 0.26. For the high-heritability trait ADDMI (h² = 0.44), the best NNMM model increased prediction accuracy from 0.30 (GBLUP and G+M) to 0.32 when using PCs of microbiome data as intermediate omics that captured 100% of microbiome variance. Our results demonstrate the practical advantage of the NNMM model in improving the genomic prediction accuracy of feed efficiency in beef cattle by incorporating the microbiome as intermediate omics, establishing this approach as a microbiome-enabled genomic selection strategy for sustainable national beef production.

Keywords: 2026

How to Cite:

Samarakoon, B., Kuehn, L., Snelling, W., Wells, J., Hales, K., Neville, B., Fernando, S., Spangler, M. & Zhao, T., (2026) “Improving genomic prediction for feed efficiency traits in beef cattle using microbiome as an intermediate omics”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2285119. doi: https://doi.org/10.31274/wcgalp.23633

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