Data from: Bayesian modelling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows

Reducing methane emissions from livestock production is of great
importance for the sustainable management of the Earth’s environment.
Rumen microbiota play an important role in producing biogenic methane.
However, knowledge of how host genetics influences variation in ruminal
microbiota and their joint effects on methane emission is limited. We
analyzed data from 750 dairy cows, using a Bayesian model to
simultaneously assess the impact of host genetics and microbiota on host
methane emission. We estimated that host genetics and microbiota explained
24% and 7%, respectively, of variation in host methane levels. In this
Bayesian model, one bacterial genus explained up to 1.6% of the total
microbiota variance. Further analysis was performed by a mixed linear
model to estimate variance explained by host genomics in abundances of
microbial genera and operational taxonomic units (OTU). Highest estimates
were observed for a bacterial OTU with 33%, for an archaeal OTU with 26%,
and for a microbial genus with 41% heritability. However, after multiple
testing correction for the number of genera and OTUs modelled, none of the
effects remained significant. We also used a mixed linear model to test
effects of individual host genetic markers on microbial genera and OTUs.
In this analysis, genetic markers inside host genes ABS4 and DNAJC10 were
found associated with microbiota composition. We show that a Bayesian
model can be utilized to model complex structure and relationship between
microbiota simultaneously and their interaction with host genetics on
methane emission. The host genome explains a significant fraction of
between-individual variation in microbial abundance. Individual microbial
taxonomic groups each only explain a small amount of variation in methane
emissions. The identification of genes and genetic markers suggests that
it is possible to design strategies for breeding cows with desired
microbiota composition associated with phenotypes.