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Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study
Author(s) -
SaboríoMontero Alejandro,
GutiérrezRivas Mónica,
GarcíaRodríguez Aser,
Atxaerandio Raquel,
Goiri Idoia,
López de Maturana Evangelina,
JiménezMontero José Antonio,
Alenda Rafael,
GonzálezRecio Oscar
Publication year - 2020
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12444
Subject(s) - heritability , metagenomics , biology , bivariate analysis , markov chain monte carlo , statistics , mathematics , computational biology , evolutionary biology , genetics , bayesian probability , gene
Abstract The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host–metagenome–phenotype relationship. A non‐recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH 4 ) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north‐west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian ® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH 4 . Heritability of CH 4 was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH 4 ranged from −0.76 to 0.65 in the non‐recursive bivariate model and from −0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota ( Methanobrevibacter sp .), Chytridiomycota ( Neocallimastix sp. ) and Fibrobacteres ( Fibrobacter sp .) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH 4 and the microbiota compositions are partially controlled by the host genotype.