Opportunities and limits of combining microbiome and genome data for complex trait prediction

Advancing Dairy and Beef Genetics Through Genomic Technologies

The US beef and dairy industries have made remarkable advances in sustainability and productivity through technological advancements, including selective breeding. Yet, challenges persist due to the complex nature of quantitative traits. While the beef industry has progressed in adopting genomic technologies, the availability of phenotypic data remains an obstacle. To meet the need for sustainable production systems, novel traits are being targeted for selection. Additionally, emerging approaches such as genome editing and high-throughput phenotyping hold promise for further genetic progress. Future research should address the challenges of translating functional genomic findings into practical applications, while simultaneously harnessing analytical methods.

Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy

Commercial livestock producers need to prioritize genetic progress for health and efficiency traits to address productivity, welfare, and environmental concerns but face challenges due to limited pedigree information in extensive multi-sire breeding scenarios. Utilizing pooled DNA for genotyping and integrating seminal microbiome information into genomic models could enhance predictions of male fertility traits, thus addressing complexities in reproductive performance and inbreeding effects. Using the Angus Australia database comprising genotypes and pedigree data for 78,555 animals, we simulated percentage of normal sperm (PNS) and prolificacy of sires, resulting in 713 sires and 27,557 progeny in the final dataset. Publicly available microbiome data from 45 bulls was used to simulate data for the 713 sires. By incorporating both genomic and microbiome information our models were able to explain a larger proportion of phenotypic variation in both PNS (0.94) and prolificacy (0.56) compared to models using a single data source (e.g., 0.36 and 0.41, respectively, using only genomic information). Additionally, models containing both genomic and microbiome data revealed larger phenotypic differences between animals in the top and bottom quartile of predictions, indicating potential for improved productivity and sustainability in livestock farming systems. Inbreeding depression was observed to affect fertility traits, which makes the incorporation of microbiome information on the prediction of fertility traits even more actionable. Crucially, our inferences demonstrate the potential of the semen microbiome to contribute to the improvement of fertility traits in cattle and pave the way for the development of targeted microbiome interventions to improve reproductive performance in livestock.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Recent Advances in Enteric Methane Mitigation and the Long Road to Sustainable Ruminant Production

Mitigation of methane emission, a potent greenhouse gas, is a worldwide priority to limit global warming. A substantial part of anthropogenic methane is emitted by the livestock sector, as methane is a normal product of ruminant digestion. We present the latest developments and challenges ahead of the main efficient mitigation strategies of enteric methane production in ruminants. Numerous mitigation strategies have been developed in the last decades, from dietary manipulation and breeding to targeting of methanogens, the microbes that produce methane. The most recent advances focus on specific inhibition of key enzymes involved in methanogenesis. But these inhibitors, although efficient, are not affordable and not adapted to the extensive farming systems prevalent in low- and middle-income countries. Effective global mitigation of methane emissions from livestock should be based not only on scientific progress but also on the feasibility and accessibility of mitigation strategies.

The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet

BACKGROUND

Improving pigs' ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects.

RESULTS

Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P < 0.001).

CONCLUSIONS

The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results.

Leveraging microbiome information for animal genetic improvement

There is growing evidence that the microbiome influences host phenotypic variation. Incorporating information about the holobiont - the host and its microbiome - into genomic prediction models may accelerate genetic improvements in farmed animal populations. Importantly, these models must account for the indirect effects of the host genome on microbiome-mediated phenotypes.

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBV_HMU vs. GBV_LMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBV_LMU cows, whereas GBV_HMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

On the holobiont 'predictome' of immunocompetence in pigs

BACKGROUND

Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.

METHODS

We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only.

RESULTS

Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy.

CONCLUSIONS

Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

BACKGROUND

Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits.

RESULTS

Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26.

CONCLUSIONS

In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models

Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.

Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet

Background

Breeding pigs that can efficiently digest alternative diets with increased fiber content is a viable strategy to mitigate the feed cost in pig production. This study aimed at determining the contribution of the gut microbiota and host genetics to the phenotypic variability of digestive efficiency (DE) traits, such as digestibility coefficients of energy, organic matter and nitrogen, feed efficiency (FE) traits (feed conversion ratio and residual feed intake) and growth traits (average daily gain and daily feed intake). Data were available for 791 pigs fed a conventional diet and 735 of their full-sibs fed a high-fiber diet. Fecal samples were collected at 16 weeks of age to sequence the V3–V4 regions of the 16S ribosomal RNA gene and predict DE with near-infrared spectrometry. The proportions of phenotypic variance explained by the microbiota (microbiability) were estimated under three OTU filtering scenarios. Then, microbiability and heritability were estimated independently (models Micro and Gen) and jointly (model Micro+Gen) using a Bayesian approach for all traits. Breeding values were estimated in models Gen and Micro+Gen.

Results

Differences in microbiability estimates were significant between the two extreme filtering scenarios (14,366 and 803 OTU) within diets, but only for all DE. With the intermediate filtering scenario (2399 OTU) and for DE, microbiability was higher (> 0.44) than heritability (< 0.32) under both diets. For two of the DE traits, microbiability was significantly higher under the high-fiber diet (0.67 ± 0.06 and 0.68 ± 0.06) than under the conventional diet (0.44 ± 0.06). For growth and FE, heritability was higher (from 0.26 ± 0.06 to 0.44 ± 0.07) than microbiability (from 0.17 ± 0.05 to 0.35 ± 0.06). Microbiability and heritability estimates obtained with the Micro+Gen model did not significantly differ from those with the Micro and Gen models for all traits. Finally, based on their estimated breeding values, pigs ranked differently between the Gen and Micro+Gen models, only for the DE traits under both diets.

Conclusions

The microbiota explained a significant proportion of the phenotypic variance of the DE traits, which was even larger than that explained by the host genetics. Thus, the use of microbiota information could improve the selection of DE traits, and to a lesser extent, of growth and FE traits. In addition, our results show that, at least for DE traits, filtering OTU is an important step and influences the microbiability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00742-6.

Genetic relationships between efficiency traits and gut microbiota traits in growing pigs fed a conventional or a high fiber diet

In pigs, the gut microbiota composition plays a major role in the process of digestion, but is influenced by many external factors, especially diet. To be used in breeding applications, genotype by diet interactions on microbiota composition have to be quantified, as well as their impact on genetic covariances with feed efficiency (FE) and digestive efficiency (DE) traits. This study aimed at determining the impact of an alternative diet on variance components of microbiota traits (genera and alpha diversity indices) and estimating genetic correlations between microbiota and efficiency traits for pigs fed a conventional (CO) or a high-fiber (HF) diet. Fecal microbes of 812 full-siblings fed a CO diet and 752 pigs fed the HF diet were characterized at 16 weeks of age by sequencing the V3-V4 region of the 16S rRNA gene. A total of 231 genera were identified. Digestibility coefficients of nitrogen, organic matter, and energy were predicted analyzing the same fecal samples with near infrared spectrometry. Daily feed intake, feed conversion ratio, residual feed intake and average daily gain (ADG) were also recorded. The 71 genera present in more than 20% of individuals were retained for genetic analyses. Heritability (h²) of microbiota traits were similar between diets (from null to 0.38 ± 0.12 in the CO diet and to 0.39 ± 0.12 in the HF diet). Only three out of the 24 genera and two alpha diversity indices with significant h² in both diets had genetic correlations across diets significantly different from 0.99 (P < 0.05), indicating limited genetic by diet interactions for these traits. When both diets were analyzed jointly, 59 genera had h² significantly different from zero. Based on the genetic correlations between these genera and ADG, FE, and DE traits, three groups of genera could be identified. A group of 29 genera had abundances favorably correlated with DE and FE traits, 14 genera were unfavorably correlated with DE traits, and the last group of 16 genera had abundances with correlations close to zero with production traits. However, genera abundances favorably correlated with DE and FE traits were unfavorably correlated with ADG, and vice versa. Alpha diversity indices had correlation patterns similar to the first group. In the end, genetic by diet interactions on gut microbiota composition of growing pigs were limited in this study. Based on this study, microbiota-based traits could be used as proxies to improve FE and DE in growing pigs.

Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Background

The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated.

Results

Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect.

Conclusions

Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00717-7.

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica)

BACKGROUND

Phosphorus is an essential nutrient in all living organisms and, currently, it is the focus of much attention due to its global scarcity, the environmental impact of phosphorus from excreta, and its low digestibility due to its storage in the form of phytates in plants. In poultry, phosphorus utilization is influenced by composition of the ileum microbiota and host genetics. In our study, we analyzed the impact of host genetics on composition of the ileum microbiota and the relationship of the relative abundance of ileal bacterial genera with phosphorus utilization and related quantitative traits in Japanese quail. An F2 cross of 758 quails was genotyped with 4k genome-wide single nucleotide polymorphisms (SNPs) and composition of the ileum microbiota was characterized using target amplicon sequencing. Heritabilities of the relative abundance of bacterial genera were estimated and quantitative trait locus (QTL) linkage mapping for the host was conducted for the heritable genera. Phenotypic and genetic correlations and recursive relationships between bacterial genera and quantitative traits were estimated using structural equation models. A genomic best linear unbiased prediction (GBLUP) and microbial (M)BLUP hologenomic selection approach was applied to assess the feasibility of breeding for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

RESULTS

Among the 59 bacterial genera examined, 24 showed a significant heritability (nominal p ≤ 0.05), ranging from 0.04 to 0.17. For these genera, six genome-wide significant QTL were mapped. Significant recursive effects were found, which support the indirect host genetic effects on the host's quantitative traits via microbiota composition in the ileum of quail. Cross-validated microbial and genomic prediction accuracies confirmed the strong impact of microbial composition and host genetics on the host's quantitative traits, as the GBLUP accuracies based on the heritable microbiota-mediated components of the traits were similar to the accuracies of conventional GBLUP based on genome-wide SNPs.

CONCLUSIONS

Our results revealed a significant effect of host genetics on composition of the ileal microbiota and confirmed that host genetics and composition of the ileum microbiota have an impact on the host's quantitative traits. This offers the possibility to breed for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.