Multi-omics analysis

Genomic and Metagenomic Parameters for Economically Important Traits in Angus Cattle

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Abstract

Including microbiome information in genomic models is a growing area of focus in animal breeding and genetics. The possibility of using gut microbiome information to enhance prediction accuracy represents a milestone in the selection process. This study aimed to incorporate different microbiome similarity matrices (fecal and ruminal) into BLUP-based models to assess their feasibility and impact on the estimation of genetic parameters in American Angus cattle. Between June 2022 and January 2023, fecal and ruminal samples were collected from 1,460 yearling Angus bulls across seven feed efficiency testing centers in the U.S. After data editing, microbiome information was available for 906 (ruminal) and 913 (fecal) animals. Amplicon sequence variant (ASV) abundances were inferred using the DADA2 database. After rarefying samples to 10,000 reads and filtering ASVs with total abundance < 1,200 and prevalence < 5%, 1,451 ruminal and 1,388 fecal ASVs remained. Standardized ASV relative abundances were used to compute a microbial relationship matrix based on the inner product of the standardized values, with a small diagonal value added to avoid singularity. Phenotypes and genotypes were provided by the American Angus Association and included four traits: weaning weight (WW), post-weaning gain (PWG), dry matter intake (DMI), and ultrasound backfat thickness (uFAT). Variance components were estimated using AI-REML via BLUPF90+ with single-trait models that included fixed effects for contemporary group, age classes (for DMI), general mean, and birth year. Three models were tested: (1) GBLUP (additive genetic effects only), (2) MBLUP (microbiome effects only), and (3) GMBLUP (combined effects). Additionally, predicted microbiome effects were used as pseudo-phenotypes to estimate microbiome heritability, defined as the proportion of variance in microbiome-mediated effects explained by host genetics. In GBLUP, heritability ranged from 0.17 to 0.31, and in MBLUP, the percentage of phenotypic variance explained by microbiome information ranged from 0.01 to 0.08 for the ruminal microbiome and from 0.00 to 0.08 for the fecal microbiome. The lowest values were for uFAT and DMI. For GMBLUP, trait heritability ranged from 0.16 to 0.29 (ruminal) and between 0.18 and 0.27 (fecal). The microbiability for ruminal ranged from 0.09 to 0.23, and for fecal from 0.01 to 0.23, with higher values for DMI in both cases. Heritability of the genetic effect mediated by the microbiome ranged from 0.10 to 0.17 for the ruminal microbiome and from 0.01 to 0.20 for the fecal microbiome, with the highest values for DMI and uFAT (ruminal) and for WW (fecal). These results indicate that both ruminal and fecal microbiomes are partly under host genetic control, with slightly higher variance explained by the rumen than by the fecal microbiome. Incorporating microbiome information into genetic models can affect the estimation of genetic parameters and help better characterize phenotypic variation in beef cattle.

Keywords: 2026

How to Cite:

Costa, R., Bussiman, F., Garcia, A., Retallick, K. & Lourenco, J., (2026) “Genomic and Metagenomic Parameters for Economically Important Traits in Angus Cattle”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2280334. doi: https://doi.org/10.31274/wcgalp.23442

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