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Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

Digital.CSIC. Repositorio Institucional del CSIC
  • López-García, Adrián
  • Saborío-Montero, Alejandro
  • Gutiérrez-Rivas, Mónica
  • Atxaerandio, Raquel
  • Goiri, Idoia
  • García-Rodríguez, Aser
  • Jiménez-Montero, Jose A
  • González, Carmen
  • Tamames, Javier
  • Puente-Sánchez, Fernando
  • Serrano, Magdalena
  • Carrasco, Rafael
  • Óvilo, Cristina
  • González-Recio, Oscar
14 Pág.
Departamento de Mejora Genetica Animal, Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions., This research was financed by RTA2015-00022-C03-02 (METALGEN) project from the National Plan of Research, Development and Innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). A.L.G. was funded by FPI-INIA grant with reference FPI-SGIT2016-06., Peer reviewed




SUPREMET Hackathon

Digital.CSIC. Repositorio Institucional del CSIC
  • González-Recio, Oscar
This data set contains information on the rumen microbiome of 340 dairy cows, sequenced within the METALGEN project (RTA2015-00022-C03).-- Sequenced with a MinION from Oxford Nanopore Technology., The purpose of this data set is to serve as a training exercise to predict a complex phenotype using metagenomic data within the workshop SUPREMET 2019 "Supercomputación para la predicción de enfermedades y caracteres complejos usando información del metagenoma"., METALGEN, Ministerio de Ciencia, Innovación y Universidades, RTA2015-00022-C03., Peer reviewed




A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

Digital.CSIC. Repositorio Institucional del CSIC
  • Saborío-Montero, Alejandro
  • López-García, Adrían
  • Gutiérrez-Rivas, Mónica
  • Atxaerandio, Raquel
  • Goiri, Idoia
  • García-Rodriguez, Aser
  • Jiménez-Montero, José A
  • González, Carmen
  • Tamames, Javier
  • Puente-Sánchez, Fernando
  • Varona, Luis
  • Serrano, Magdalena
  • Ovilo, Cristina
  • González-Recio, Oscar
17 Pág.
 Departamento de ​Mejora Genética Animal (INIA), The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations., This research was financed by the METALGEN project (RTA2015-00022-C03) from the national plan for research, development, and innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain)., Peer reviewed




Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Digital.CSIC. Repositorio Institucional del CSIC
  • Saborío-Montero, Alejandro
  • Gutiérrez-Rivas, Mónica
  • López-García, Adrián
  • García-Rodríguez, Aser
  • Atxaerandio, Raquel
  • Goiri, Idoia
  • Jiménez-Montero, José Antonio
  • González-Recio, Oscar
14 Pág.
Departamento de ​Mejora Genética Animal, Rumen microbiota has been previously related to phenotypic complex traits of relevance in dairy cattle. The joint analysis of the host's genetic background and its microbiota can be statistically modelled using similarity matrices between microorganism communities in the different hosts. Microbiota relationship matrices (K) enable considering the whole microbiota and the cumbersome interrelations between taxa, rather than analyzing single taxa one at the time. Several methods have been proposed to ordinate these matrices. The aim of this study was to compare the performance of twelve K built from different microbiome distance metrics, within a variance component estimation framework for methane concentration in dairy cattle. Phenotypic, genomic and rumen microbiome information from simulations (n = 1000) and real data (cows = 437) were analyzed. Four models were considered: an additive genomic model (GBLUP), a microbiome model (MBLUP), a genetic and microbiome effects model (HBLUP) and a genetic, microbiome and genetic × microbiome interaction effects model (HiBLUP). Results from simulation were obtained from 25 replicates. Results from simulated data suggested that Ks with flattened off-diagonal elements were more accurate in variance components estimation for all compared models that included Ks information (MBLUP, HBLUP and HiBLUP). Multidimensional scaling (MDS), redundancy analysis (RDA) and constrained correspondence analysis (CCA) performed better in simulation to estimate heritability and microbiability. The models including Ks from the MDS, RDA and CCA methods were also between the most plausible models in the real data set, according to the deviance information criteria (DIC). Real data was analyzed under the same framework as in the simulation. The most plausible model in real data was HiBLUP. Estimates variated depending on K; methane heritability (0.15–0.17) and microbiability (0.15–0.21) were lower than the proportion of the phenotypic variance attributable to the host-microbiome holobiont effect (0.42–0.59), which we have defined here as “holobiability”. The holobiability including the genomic × microbiome interaction from the HiBLUP was between 0.01 and 0.15 larger than the holobiability explained from the sum of the genetic and microbiome effects without interaction between them, from the HBLUP, depending on K. The findings in this study support the potential of the joint analysis of genomic and microbiome information. Accounting for the hologenome effect (genomic and microbiome) could improve the accuracy in variance component estimation of complex traits relevant in livestock science., This research was financed by RTA2015-00022-C03 (METALGEN) project from the Spanish national plan of research, development, and innovation 2013-2020. Alejandro Saborío-Montero acknowledges the scholarship from Universidad de Costa Rica for his doctorate studies which partially conducted to the progress of this study., Peer reviewed




Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability

Digital.CSIC. Repositorio Institucional del CSIC
  • González-Recio, Oscar
  • Scrobota, Natalia
  • López-Paredes, Javier
  • Saborío-Montero, Alejandro
  • Fernández Muñoz, Almudena
  • López de Maturana, E.
  • Villanueva, Beatriz
  • Goiri, Idoia
  • Atxaerandio, Raquel
  • García-Rodríguez, Aser
9 Pág., Interest on methane emissions from livestock has increased in later years as it is an anthropogenic greenhouse gas with an important warming potential. The rumen microbiota has a large influence on the production of enteric methane. Animals harbour a second genome consisting of microbes, collectively referred to as the "microbiome". The rumen microbial community plays an important role in feed digestion, feed efficiency, methane emission and health status. This review recaps the current knowledge on the genetic control that the cow exerts on the rumen microbiota composition. Heritability estimates for the rumen microbiota composition range between 0.05 and 0.40 in the literature, depending on the taxonomical group or microbial gene function. Variables depicting microbial diversity or aggregating microbial information are also heritable within the same range. This study includes a genome-wide association analysis on the microbiota composition, considering the relative abundance of some microbial taxa previously associated to enteric methane in dairy cattle (Archaea, Dialister, Entodinium, Eukaryota, Lentisphaerae, Methanobrevibacter, Neocallimastix, Prevotella and Stentor). Host genomic regions associated with the relative abundance of these microbial taxa were identified after Benjamini-Hoschberg correction (Padj < 0.05). An in-silico functional analysis using FUMA and DAVID online tools revealed that these gene sets were enriched in tissues like brain cortex, brain amigdala, pituitary, salivary glands and other parts of the digestive system, and are related to appetite, satiety and digestion. These results allow us to have greater knowledge about the composition and function of the rumen microbiome in cattle. The state-of-the art strategies to include methane traits in the selection indices in dairy cattle populations is reviewed. Several strategies to include methane traits in the selection indices have been studied worldwide, using bioeconomical models or economic functions under theoretical frameworks. However, their incorporation in the breeding programmes is still scarce. Some potential strategies to include methane traits in the selection indices of dairy cattle population are presented. Future selection indices will need to increase the weight of traits related to methane emissions and sustainability. This review will serve as a compendium of the current state of the art in genetic strategies to reduce methane emissions in dairy cattle., This research was financed by RTA2015-00022-C03 (METALGEN) project from the National Plan of Research, Development and Innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). BV thanks funding from MCIN/ AEI /10.13039/501100011033 (Project PID2020-114426GB-C22)., Peer reviewed




Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane

Digital.CSIC. Repositorio Institucional del CSIC
  • López-Paredes, Javier
  • Goiri, Idoia
  • Atxaerandio, Raquel
  • García-Rodríguez, Aser
  • Ugarte, E.
  • Jiménez-Montero, J. A.
  • Alenda, R.
  • González-Recio, Oscar
11 Pág., Records of methane emissions from 1,501 cows on 14 commercial farms in 4 regions of Spain were collected from May 2018 to June 2019. Methane concentrations (MeC) were measured using a nondispersive infrared methane detector installed within the feed bin of the automatic milking system during 14- to 21-d periods. Rumination time (RT; min/d) was collected using collars with a tag that registered time (minutes) spent eating and ruminating. The means of MeC and methane production (MeP) were 1,254.28 ppm and 182.49 g/d, respectively; mean RT was 473.38 min/d. Variance components for MeC, MeP, and RT were estimated with REML using pedigree and genomic information in a single-step model. Heritabilities for MeC and MeP were 0.11 and 0.12, respectively. Rumination time showed a slightly larger heritability estimate (0.17). The genetic correlation between MeP and MeC was high (>0.95), suggesting that selection on either trait would lead to a positive correlated response on the other. Negative correlations were estimated between RT and MeC (-0.24 ± 0.38) and MeP (-0.43 ± 0.35). Methane concentration and MeP had slightly positive correlations with milk yield (0.17 ± 0.39 and 0.21 ± 0.36), protein percentage (0.08 ± 0.32 and 0.30 ± 0.45), protein yield (0.22 ± 0.41 and 0.31 ± 0.35), fat percentage (0.02 ± 0.40 and 0.27 ± 0.36), and fat yield (0.27 ± 0.28 and 0.29 ± 0.28) from bivariate analyses. Rumination time had positive correlations with milk yield (0.41 ± 0.75) and protein yield (0.26 ± 0.57) and negative correlations with fat yield (-0.45 ± 0.32), protein percentage (-0.15 ± 0.38), and fat percentage (-0.40 ± 0.47). A positive approximated genetic correlation was estimated between fertility and MeC (0.10 ± 0.05) and MeP (0.18 ± 0.05), resulting in slightly higher CH4 production when selecting for better fertility [days open estimated breeding values (EBV) are expressed with mean 100 and SD 10, inversely related to days from calving to conception; that is, greater days open EBV implies better fertility]. Positive correlations were also estimated for stature with MeC and MeP (0.30 ± 0.04 and 0.43 ± 0.04, respectively). Other type traits (chest width, udder depth, angularity, and capacity) were positively correlated with methane traits, possibly because of higher milk yield and higher feed intake from these animals. Rumination time showed positive EBV correlations with production traits and type traits, and negative correlations with somatic cell count and body condition score. Based on the genetic correlations and heritabilities estimated in this study, methane is measurable and heritable, and estimates of genetic correlations suggest no strong opposition to current breeding objectives in Spanish Holsteins., This research was funded by RTA2015-00022-C03 (METALGEN) project from the Spanish National Plan of Research, development and innovation 2013-2020 and the Department of Economic Development and Competitiveness (Basque Government, Vitoria-Gasteiz, Spain). Farmers and breeders' associations (EFRIFE, AFNA, GIFE, AFCA) are acknowledge for data sharing and infrastructure. Data were analyzed using the CESGA Supercomputing infrastructure (Galicia, Spain). The editor and three reviewers are acknowledged for thorough and insightful comments. The authors have not stated any conflicts of interest., Peer reviewed




Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal

Digital.CSIC. Repositorio Institucional del CSIC
  • González-Recio, Oscar
  • López-Paredes, Javier
  • Ouatahar, L.
  • Charfeddine, N.
  • Ugarte, E.
  • Alenda, R.
  • Jiménez-Montero, J. A.
12 Pág., The objective of this study was to analyze the impact of incorporating enteric methane into the breeding objective of dairy cattle in Spain, and to evaluate both genetic and economic response of traits in the selection index under 4 scenarios: (1) the current ICO (Spanish total merit index), used as benchmark; (2) a hypothetical penalization of methane emissions through a carbon tax; (3) considering methane as a net energy loss for the animal; and (4) desired genetic response to reduce methane production by 20% in 10 yr. A bio-economic model was developed to derive the economic values for production and methane traits in each scenario. The estimated economic values for methane were estimated at -€1.21/kg and -€0.32/kg for scenarios 2 and 3, respectively. When merged with other traits in the selection index, methane had less economic importance (1-5%) than milk protein yield (39-42%) or milk fat yield (27-28%). Under these scenarios, selection resulted in an unfavorable response in methane emissions when it was included with an economic weight, with an increase in methane estimated from 0.52 to 0.60 kg/cow per year. Small differences in total profit per cow per year were observed between indices. The incorporation of methane production into the breeding objective had a negligible effect on production, with minor reductions in the expected genetic gain for fat and protein yields and in total economic benefits. However, total methane emissions in the dairy industry in Spain were estimated to decrease between 2 and 5% in the next 10 yr due to positive genetic trends for milk yield and an expected decrease in the total number of dairy cows. Additionally, methane intensity per 1 billion liters of milk would decrease in all scenarios. The uncertainty in the genetic parameters of methane and in carbon prices were tested in a sensitivity analysis, resulting in small deviations from the benchmark scenario. A major effect was observed only under the desired genetic response scenario. In this case, it was possible to achieve a 20% reduction of methane production in 10 yr via selective breeding but at the expense of a larger ad hoc weight (33%) of methane in the selection index and decelerating the genetic gain for production traits from 6 to 18%. This study shows the potential of including environmental traits in the selection indices while retaining populations profitable for producers., This research was financed by RTA2015-00022-C03-02 (METALGEN) project from the national plan of research, development and innovation 2013-2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). We thank the Spanish Holstein Association (CONAFE, Valdemoro, Spain) for providing the productive and economic data. L. Ouatahar acknowledges funding from the Mediterranean Agronomic Institute of Zaragoza (Spain). The authors have not stated any conflicts of interest., Peer reviewed




Study of the Host Genetic Control over the Ruminal Microbiota and their Relationships with Methane Emissions in Dairy Cattle

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
  • Saborío Montero, Alejandro
[ES] El análisis del control genético del hospedador sobre su microbiota ha sido señalado recientemente como un tema prometedor en diferentes campos de estudio. La relación entre el holobionte hospedador-microbioma y los fenotipos en el ganado lechero podría conducir a nuevos conocimientos en los programas de selección genética. Dentro de esta tesis doctoral, se realizó la estimación y análisis a través de diferentes enfoques estadísticos con el objetivo de desentrañar el control genético del hospedador sobre la microbiota en ganado lechero. Además, se analizó el rasgo de concentración de metano como un fenotipo potencial para ser incluido en el programa de mejora de ganado lechero español. Mayor abundancia relativa de la mayoría de los eucariotas (principalmente protozoos ciliados y hongos) y algunas arqueas (Methanobrevibacter spp. Methanothermus spp. y Methanosphaera spp.) fueron factores de riesgo para ser clasificadas en la categoría alta. Se propuso un conjunto de modelos de ecuaciones estructurales (SEM) de tipo recursivo dentro de un marco de Cadenas de Markov Monte Carlo (MCMC) para analizar conjuntamente la relación hospedador-metagenoma-fenotipo. Se estableció un modelo bivariado no-recursivo como punto de referencia. La heredabilidad de CH4 se estimó en 0,12 ± 0,01 en ambos modelos, recursivo y no recursivo. Asimismo, las estimaciones de heredabilidad para la abundancia relativa de los taxones se superpusieron entre los modelos y variaron entre 0.08 y 0.48. Las correlaciones genéticas entre la composición microbiana y el CH4 variaron de -0,76 a 0,65 en el modelo bivariado no recursivo y de -0,68 a 0,69 en el modelo recursivo. Doce matrices de relación de microbiota (K) fueron construidas a partir de diferentes métricas de distancia del microbioma, con el objetivo de comparar su desempeño dentro de un marco de estimación de componentes de varianza para CH4 y toda la microbiota. Análisis de simulación (n = 1000) y datos reales fueron desarrollados considerando cuatro modelos posibles: un modelo genómico aditivo (GBLUP), un modelo de microbioma (MBLUP), un modelo de efectos genéticos y microbioma (HBLUP) y un modelo de efectos de interacción genético, microbioma y genético × microbioma (HiBLUP). Un nuevo término "Holobiabilidad" fue definido para referirse a la proporción de la varianza atribuible a los efectos del holobionte hospedador-microbioma. Las estimaciones a partir de datos reales usando HiBLUP variaron dependiendo de la K utilizada y estuvieron entre 0.15-0.17, 0.15-0.21 y 0.42-0.59 para heredabilidad, microbiabilidad y holobiabilidad, respectivamente. El conjunto de datos de microbioma fue agregado a través de análisis de componentes principales (PCA), en pocos componentes principales (PCs) que fueron utilizados como aproximaciones del metagenoma central. Parte de la variabilidad condensada en estos PC está controlada por el genoma de la vaca, con estimaciones de heredabilidad para el primer PC (PC1) de ~ 0,30 en todos los niveles taxonómicos, con una gran probabilidad (> 83%) de que la distribución posterior sea > 0,20 y con un intervalo de mayor densidad posterior al 95% (95% HPD) no conteniendo cero. La mayoría de las estimaciones de correlación genética entre PC1 y metano fueron grandes (>0,70) en todos los niveles taxonómicos, con la mayor parte de la distribución posterior (> 82%) siendo > 0,50 y con su 95% HPD no conteniendo cero. Estos resultados sugieren que todo el metagenoma del rumen regula recursivamente las emisiones de metano en las vacas lecheras, y que tanto el CH4 como las composiciones de la microbiota están parcialmente controladas por el genotipo del hospedador. Las variables agregadas (PC) propuestas podrían ser usadas en programas de mejora de animales para reducir las emisiones de metano en las generaciones futuras., [CA] L'anàlisi del control genètic de l'hoste sobre la seva microbiota s'ha assenyalat recentment com un tema prometedor en diferents camps d'estudi. La relació entre el holobiont hoste-microbioma i els fenotips en bovins de llet podria conduir a nous coneixements en els programes de cria. Dins d'aquest doctorat es van realitzar tesis, estimacions i anàlisis mitjançant diferents enfocaments estadístics amb l'objectiu de desentranyar el control genètic de l'hoste sobre la microbiota en bestiar lleter. A més, es va analitzar el tret de concentració de metà com a fenotip potencial a incloure en el programa espanyol de cria de bestiar lleter. La major abundància relativa de la majoria dels eucariotes (principalment protozous i fongs ciliats) i algunes arquees (Methanobrevibacter spp. Methanothermus spp i Methanosphera spp.) Van ser factors de risc per classificar-se en les categories altes. Es va proposar un conjunt de models d'equacions estructurals (SEM) de tipus recursiu dins d'un marc de cadena Markov Monte Carlo (MCMC) per analitzar conjuntament la relació hoste-metagenoma-fenotip. Es van establir models no recursius com a referència. L'heretabilitat del CH4 es va estimar en 0,12 ± 0,01 en ambdós models, recursius i no recursius. De la mateixa manera, les estimacions d'heretabilitat de l'abundància relativa dels tàxons es van superposar entre models i van oscil·lar entre 0,08 i 0,48. Les correlacions genètiques entre la composició microbiana i el CH4 van oscil·lar entre -0,76 i 0,65 en els models bivariables no recursius i de -0,68 a 0,69 en els models recursius. Dotze matrius de relació de microbiota (K) de diferents mètriques de distància de microbiomes, amb l'objectiu de comparar el seu rendiment dins d'un marc d'estimació de components de variància per CH4 i anàlisi de microbiomes sencers en simulació (n = 1000, 25 rèpliques) i es van realitzar dades reals , considerant quatre possibles models: un model genòmic additiu (GBLUP), un model de microbioma (MBLUP), un model d'efectes genètics i microbiomes (HBLUP) i un model d'efectes d'interacció genètics, microbiomes i genètics × microbiomes (HiBLUP). Es va definir un nou terme "Holobiabilitat" per referir-se a la proporció de la variància fenotípica atribuïble als efectes holobiont del microbioma host. Les estimacions de dades reals mitjançant HiBLUP van variar en funció de la K utilitzada i van oscil·lar entre 0,15-0,17, 0,15-0,21 i 0,42-0,59 per heretabilitat, microbiabilitat i holobiabilitat, respectivament. El conjunt de dades de microbiomes es va agregar mitjançant l'anàlisi de components principals (PCA) en pocs components principals (PC) que es van utilitzar com a proxies del metagenoma principal. Part de la variabilitat condensada en aquestes PC està controlada pel genoma de la vaca, amb estimacions d'heretabilitat per a la primera PC (PC1) de ~ 0,30 a tots els nivells taxonòmics, amb una gran probabilitat (> 83%) de la distribució posterior> 0,20 i amb un 95% més alt interval de densitat posterior (95% HPD) que no conté zero. La majoria de les estimacions de correlació genètica entre PC1 i metà eren grans (>0,70) en tots els nivells taxonòmics, amb una gran part de la distribució posterior (> 82%)> 0,50 i amb un 95% de HPD que no contenia zero. Aquests resultats suggereixen que tot el metagenoma del rumen regula recursivament les emissions de metà en vaques lleteres i que tant el CH4 com les composicions de microbiota estan parcialment controlades pel genotip de l'hoste. Les variables agregades proposades (PC) es podrien utilitzar en programes de cria d'animals per reduir les emissions de metà en les generacions futures., [EN] The analysis of the host genetic control over its microbiota has recently been pointed out as a promising theme in different fields of study. The relationship between the host-microbiome holobiont and phenotypes in dairy cattle could lead to new insights in breeding programs. Within this Ph.D. thesis, estimation and analysis through different statistical approaches were performed aiming to unravel the host genetic control over the microbiota in dairy cattle. Besides, methane concentration trait was analyzed as a potential phenotype to be included in the Spanish dairy cattle breeding program. Higher relative abundance of most eukaryotes (mainly ciliate protozoa and fungi) and some archaea (Methanobrevibacter spp. Methanothermus spp and Methanosphera spp.) were risk factors for being classified in the high categories. a set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed to jointly analyze the host-metagenome-phenotype relationship. Non-recursive models were set as benchmark. Heritability of CH4 was estimated at 0.12 ± 0.01 in both, the recursive and non-recursive, 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 CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate models and from -0.68 to 0.69 in the recursive models. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the 7 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. Twelve microbiota relationship matrices (K) from different microbiome distance metrics were built, aiming to compare its performance within a variance component estimation framework for CH4 and whole microbiome analysis on simulation (n = 1000, 25 replicates) and real data were performed, considering four possible models: an additive genomic model (GBLUP), a microbiome model (MBLUP), a genetic and microbiome effects model (HBLUP) and a genetic, microbiome and genetic × microbiome interaction effects model (HiBLUP). A new term "Holobiability" was defined to refer to the proportion of the phenotypic variance attributable to the host-microbiome holobiont effects. Estimates from real data using HiBLUP varied depending on the K used and ranged between 0.15-0.17, 0.15-0.21 and 0.42-0.59 for heritability, microbiability and holobiability, respectively. The microbiome dataset was aggregated through Principal Component Analysis (PCA) into few principal components (PCs) that were used as proxies of the core metagenome. Part of the variability condensed in these PCs is controlled by the cow genome, with heritability estimates for the first PC (PC1) of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being > 0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (>0.70) at all taxonomic levels, with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. These results suggest that rumen's whole metagenome recursively regulate methane emissions in dairy cows, and that both CH4 and the microbiota compositions are partially controlled by the host genotype. The purposed aggregated variables (PCs) could be used in animal breeding programs to reduce methane emissions in future generations., This research was financed by RTA2015-00022-C03-02 (METALGEN) project from
the national plan of research, development and innovation 2013-2020 and the Department of
Economic Development and Competitiveness (Madrid, Spain). We thank the regional
Holstein Associations and farmers collaborating in the project. Computational support from
the High-Performance Computing Centre in Galicia (Spain) is acknowledged. Alejandro
Saborío-Montero acknowledges the scholarship from Universidad de Costa Rica for his
doctorate studies which partially conducted to the progress of this study.