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.降低畜牧生产中的甲烷排放，对于地球环境的可持续管理具有重要意义。瘤胃微生物群（rumen microbiota）在生物源甲烷的产生过程中发挥着关键作用。然而，目前学界对于宿主遗传学如何影响瘤胃微生物群的变异，以及二者对甲烷排放的联合效应的认知仍较为有限。 我们对750头奶牛的数据集进行了分析，采用贝叶斯模型同时评估宿主遗传学与微生物群对宿主甲烷排放的影响。经估算，宿主遗传学与微生物群分别可解释宿主甲烷水平变异的24%与7%。在该贝叶斯模型中，单个细菌属可解释最高达1.6%的总微生物群变异。 后续我们通过混合线性模型开展进一步分析，以估算宿主基因组对微生物属与操作分类单元（operational taxonomic units, OTU）丰度的变异解释率。其中，细菌操作分类单元的估算值最高可达33%，古菌操作分类单元为26%，微生物属的遗传力最高达41%。不过，在对所建模的微生物属与操作分类单元数量进行多重检验校正后，所有效应均不再显著。 我们还通过混合线性模型，检验了单个宿主遗传标记对微生物属与操作分类单元的影响。在该分析中，宿主基因ABS4与DNAJC10内部的遗传标记被发现与微生物群组成存在关联。 本研究表明，贝叶斯模型可用于同时建模微生物群的复杂结构与相互关系，以及二者与宿主遗传学在甲烷排放方面的交互作用。宿主基因组可解释微生物丰度个体间变异中的显著比例。单个微生物分类群仅能解释甲烷排放变异中的极小部分。相关基因与遗传标记的鉴定提示，我们可通过设计育种策略，培育出具有与目标表型相关的理想微生物群组成的奶牛。