Work

Comparison of 16S rRNA sequencing and shotgun metagenome sequencing for estimating genotypic and phenotypic parameters of enteric methane emission in dairy cattle

Year: 2026

Type: article

Abstract: Methane emissions from ruminants significantly contribute to GHG, making it crucial for sustainable livestock breeding to understand how both genetic and microbial factors influence methane production. We compared the heritability and microbiability for enteric methane in cows using microbial features derived from 16S rRNA amplicon data and shotgun metagenomics data, together with genome-wide marker data. The features derived from 16S rRNA data were 16S genus (16S-G), 16S species (16S-S), 16S predicted microbial genes (16S-PMG), and 16S predicted metabolic pathways (16S-PMP). The features derived from metagenomic data were metagenomic species (M-S) and metagenomic genus (M-G) considering 3 different databases (MGnify [https://www.ebi.ac.uk/about/news/updates-from-data-resources/cow-rumen-catalogue-v10-released-mgnify/], Genome Taxonomy Database [https://gtdb.ecogenomic.org/], and National Center for Biotechnology Information [NCBI; https://www.ncbi.nlm.nih.gov/]). The heritability of methane ranged from 0.08 to 0.14. The 16S-G explained 28% of phenotypic variation in methane and contributed the most to the heritability estimate for methane among other features. For the same feature datasets, we estimated the heritability of each microbial feature. Most microbial features had low heritability, although a subset had high values (up to 0.8). The highest heritabilities were observed for M-S MGnify feature RUG592 sp902767285 (0.95) and M-G NCBI genus feature Leadbettera (0.98). We found that the microbiota in the rumen is primarily determined by environmental factors, whereas host genetics has a significant impact on the abundance of certain functionally important microbes. To the best of our knowledge, this study presents the first comparison of methane heritability in dairy cattle incorporating microbial data (1) from multiple techniques such as 16S rRNA amplicon sequencing and shotgun metagenomic sequencing and (2) from multiple levels of microbial features such as 16S-G, 16S-S, 16S-PMG, 16S-PMP, and M-S and M-G. Our results highlight heritable microbial species/genus as potential targets for microbiome-informed breeding strategies to reduce methane emissions in dairy cattle.

Source: Journal of Dairy Science

DOI: 10.3168/jds.2025-28157