scholarly journals Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

2021 ◽  
Author(s):  
Yuliaxis Ramayo-Caldas ◽  
Laura M. Zingaretti ◽  
David Perez-Pascual ◽  
Pamela A Alexandre ◽  
Antonio Reverter-Gomez ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Mixed Model ◽  
Association Studies ◽  
Host Genome ◽  
Phenotypic Variance ◽  
Microbial Composition ◽  
Phagocytic Capacity ◽  
Host Genetics ◽  
Microbial Biomarkers ◽  
Variance Explained

The aim of the present work was to identify microbial biomarkers linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs. To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h2; proportion of phenotypic variance explained by the host genetics) and microbiability (m2; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that were dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r=0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r=-0.34) and between Megasphaera and serum concentration of haptoglobin (r=0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively. Overall, our findings reveal an important connection between immunity traits and gut microbiota in pigs and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

scholarly journals Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Yuliaxis Ramayo-Caldas ◽  
Laura M. Zingaretti ◽  
David Pérez-Pascual ◽  
Pamela A. Alexandre ◽  
Antonio Reverter ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Association Studies ◽  
Host Genome ◽  
Phenotypic Variance ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Phagocytic Capacity ◽  
Host Genetics ◽  
Gut Microbiota Composition ◽  
Variance Explained

Abstract Background The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs. Results To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h2; proportion of phenotypic variance explained by the host genetics) and microbiability (m2; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively. Conclusions Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

scholarly journals 297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 32-32
Author(s):  
Juan P Steibel ◽  
Ignacio Aguilar
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Mixed Model ◽  
Prediction Models ◽  
Association Studies ◽  
Least Square ◽  
Type I ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Formal Hypothesis Testing

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

scholarly journals Honeybee genetics shape the strain-level structure of gut microbiota in social transmission

2020 ◽  
Author(s):  
Jiaqiang Wu ◽  
Haoyu Lang ◽  
Xiaohuan Mu ◽  
Zijing Zhang ◽  
Qinzhi Su ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Receptor Gene ◽  
Level Structure ◽  
Social Transmission ◽  
Strain Level ◽  
Microbial Composition ◽  
Shotgun Metagenomics ◽  
Host Genetics ◽  
A Genome ◽  
Microbiota Structure

AbstractHoneybee gut microbiota transmitted via social interactions are beneficial to the host health. Although the microbial community is relatively stable, individual variations and high strain-level diversity have been detected across honeybees. Although the bee gut microbiota structure is influenced by environmental factors, the heritability of the gut members and the contribution of the host genetics remains elusive. Considering bees within a colony are not readily genetically identical due to the polyandry of queen, we hypothesize that the microbiota structure can be shaped by host genetics. We used shotgun metagenomics to simultaneously profile the microbiota and host genotypes of individuals from hives of four different subspecies. Gut composition is more distant between genetically different bees at both phylotype- and “sequence-discrete population”-level. We then performed a successive passaging experiment within colonies of hybrid bees generated by artificial insemination, which revealed that the microbial composition dramatically shifts across batches of bees during the social transmission. Specifically, different strains from the phylotype of Snodgrassella alvi are preferentially selected by genetically varied hosts, and strains from different hosts show a remarkably biased distribution of single-nucleotide polymorphism in the Type IV pili loci. A genome-wide association analysis identified that the relative abundance of a cluster of Bifidobacterium strains is associated with the host glutamate receptor gene that is specifically expressed in the bee brain. Finally, mono-colonization of Bifidobacterium with a specific polysaccharide utilization locus impacts the expression and alternative splicing of the gluR-B gene, which is associated with an altered circulating metabolomic profile. Our results indicated that host genetics influence the bee gut composition, and suggest a gut-brain connection implicated in the gut bacterial strain preference. Honeybees have been used extensively as a model organism for social behaviors, genetics, and gut microbiome. Further identification of host genetic function as shaping force of microbial structure will advance our understanding of the host-microbe interactions.

scholarly journals Differences in Anxiety Levels of Various Murine Models in Relation to the Gut Microbiota Composition

Biomedicines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 113 ◽  
Author(s):  
Eunchong Huang ◽  
Shinwon Kang ◽  
Haryung Park ◽  
Soyoung Park ◽  
Yosep Ji ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Psychological Symptoms ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Anxiety Level ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Host Genetics ◽  
Probiotic Strains ◽  
Anxiety Levels

Psychobiotics are probiotic strains that confer mental health benefits to the host through the modulation of the gut microbial population. Mounting evidence shows that the gut microbiota play an important role in communication within the gut–brain axis. However, the relationship between the host genetics and the gut microbiota and their influence on anxiety are still not fully understood. Hence, in our research, we attempted to draw a connection between host genetics, microbiota composition, and anxiety by performing an elevated plus maze (EPM) test on four genetically different mice. Four different breeds of 5-week-old mice were used in this experiment: Balb/c, Orient C57BL/6N, Taconic C57BL/6N, and Taconic C57BL/6J. After 1 week of adaptation, their initial anxiety level was monitored using the EPM test via an EthoVision XT, a standardized software used for behavorial testing. Significant differences in the initial anxiety level and microbial composition were detected. Subsequently, the microbiota of each group was modulated by the administration of either a probiotic, fecal microbiota transplantation, or antibiotics. Changes were observed in host anxiety levels in correlation to the shift of the gut microbiota. Our results suggest that the microbiota, host genetics, and psychological symptoms are strongly related, yet the deeper mechanistic links need further exploration.

scholarly journals Host genetic control of gut microbiome composition

2021 ◽  
Author(s):  
Jason A. Bubier ◽  
Elissa J. Chesler ◽  
George M. Weinstock
Keyword(s):  
Genetic Control ◽  
Gut Microbiome ◽  
Genetic Variant ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Disease Phenotype ◽  
Microbial Composition ◽  
Host Genetics ◽  
Microbiome Composition ◽  
Host Genetic

AbstractThe gut microbiome plays a significant role in health and disease, and there is mounting evidence indicating that the microbial composition is regulated in part by host genetics. Heritability estimates for microbial abundance in mice and humans range from (0.05–0.45), indicating that 5–45% of inter-individual variation can be explained by genetics. Through twin studies, genetic association studies, systems genetics, and genome-wide association studies (GWAS), hundreds of specific host genetic loci have been shown to associate with the abundance of discrete gut microbes. Using genetically engineered knock-out mice, at least 30 specific genes have now been validated as having specific effects on the microbiome. The relationships among of host genetics, microbiome composition, and abundance, and disease is now beginning to be unraveled through experiments designed to test causality. The genetic control of disease and its relationship to the microbiome can manifest in multiple ways. First, a genetic variant may directly cause the disease phenotype, resulting in an altered microbiome as a consequence of the disease phenotype. Second, a genetic variant may alter gene expression in the host, which in turn alters the microbiome, producing the disease phenotype. Finally, the genetic variant may alter the microbiome directly, which can result in the disease phenotype. In order to understand the processes that underlie the onset and progression of certain diseases, future research must take into account the relationship among host genetics, microbiome, and disease phenotype, and the resources needed to study these relationships.

scholarly journals On the use of GBLUP and its extension for GWAS with additive and epistatic effects

2021 ◽  
Author(s):  
Jie Zhang ◽  
Fang Liu ◽  
Jochen C Reif ◽  
Yong Jiang
Keyword(s):  
Mixed Model ◽  
Mathematical Proof ◽  
Association Studies ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Test Statistic ◽  
New Approach ◽  
Epistatic Effects ◽  
Relative Contribution ◽  
Genome Wide

Abstract Genomic best linear unbiased prediction (GBLUP) is the most widely used model for genome-wide predictions. Interestingly, it is also possible to perform genome-wide association studies (GWAS) based on GBLUP. Although the estimated marker effects in GBLUP are shrunken and the conventional test based on such effects has low power, it was observed that a modified test statistic can be produced and the result of test was identical to a standard GWAS model. Later, a mathematical proof was given for the special case that there is no fixed covariate in GBLUP. Since then, the new approach has been called “GWAS by GBLUP”. Nevertheless, covariates such as environmental and subpopulation effects are very common in GBLUP. Thus, it is necessary to confirm the equivalence in the general case. Recently, the concept was generalized to GWAS for epistatic effects and the new approach was termed rapid epistatic mixed-model association analysis (REMMA) because it greatly improved the computational efficiency. However, the relationship between REMMA and the standard GWAS model has not been investigated. In this study, we first provided a general mathematical proof of the equivalence between” GWAS by GBLUP” and the standard GWAS model for additive effects. Then, we compared REMMA with the standard GWAS model for epistatic effects by a theoretical investigation and by empirical data analyses. We hypothesized that the similarity of the two models is influenced by the relative contribution of additive and epistatic effects to the phenotypic variance, which was verified by empirical and simulation studies.

scholarly journals Hematopoietic Npc1 mutation shifts gut microbiota composition in Ldlr−/− mice on a high-fat, high-cholesterol diet

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tom Houben ◽  
John Penders ◽  
Yvonne Oligschlaeger ◽  
Inês A. Magro dos Reis ◽  
Marc-Jan Bonder ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Gut Microbiota ◽  
High Cholesterol Diet ◽  
Microbiota Composition ◽  
High Cholesterol ◽  
High Fat ◽  
Microbial Composition ◽  
Host Genetics ◽  
Gut Microbiota Composition

Abstract While the link between diet-induced changes in gut microbiota and lipid metabolism in metabolic syndrome (MetS) has been established, the contribution of host genetics is rather unexplored. As several findings suggested a role for the lysosomal lipid transporter Niemann-Pick type C1 (NPC1) in macrophages during MetS, we here explored whether a hematopoietic Npc1 mutation, induced via bone marrow transplantation, influences gut microbiota composition in low-density lipoprotein receptor knockout (Ldlr−/−) mice fed a high-fat, high-cholesterol (HFC) diet for 12 weeks. Ldlr−/− mice fed a HFC diet mimic a human plasma lipoprotein profile and show features of MetS, providing a model to explore the role of host genetics on gut microbiota under MetS conditions. Fecal samples were used to profile the microbial composition by 16 s ribosomal RNA gene sequencing. The hematopoietic Npc1 mutation shifted the gut microbiota composition and increased microbial richness and diversity. Variations in plasma lipid levels correlated with microbial diversity and richness as well as with several bacterial genera. This study suggests that host genetic influences on lipid metabolism affect the gut microbiome under MetS conditions. Future research investigating the role of host genetics on gut microbiota might therefore lead to identification of diagnostic and therapeutic targets for MetS.

scholarly journals A targeted genotyping approach to enhance the identification of variants for lactation persistency in dairy cows

2019 ◽  
Vol 97 (10) ◽  
pp. 4066-4075
Author(s):  
Duy Ngoc Do ◽  
Nathalie Bissonnette ◽  
Pierre Lacasse ◽  
Filippo Miglior ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Mixed Model ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genetic Selection ◽  
Phenotypic Variance ◽  
Potential Candidate ◽  
Nucleotide Polymorphisms ◽  
Functional Variants ◽  
Selection For

Abstract Lactation persistency (LP), defined as the ability of a cow to maintain milk production at a high level after milk peak, is an important phenotype for the dairy industry. In this study, we used a targeted genotyping approach to scan for potentially functional single nucleotide polymorphisms (SNPs) within 57 potential candidate genes derived from our previous genome wide association study on LP and from the literature. A total of 175,490 SNPs were annotated within 10-kb flanking regions of the selected candidate genes. After applying several filtering steps, a total of 105 SNPs were retained for genotyping using target genotyping arrays. SNP association analyses were performed in 1,231 Holstein cows with 69 polymorphic SNPs using the univariate liner mixed model with polygenic effects using DMU package. Six SNPs including rs43770847, rs208794152, and rs208332214 in ADRM1; rs209443540 in C5orf34; rs378943586 in DDX11; and rs385640152 in GHR were suggestively significantly associated with LP based on additive effects and associations with 4 of them (rs43770847, rs208794152, rs208332214, and rs209443540) were based on dominance effects at P < 0.05. However, none of the associations remained significant at false discovery rate adjusted P (FDR) < 0.05. The additive variances explained by each suggestively significantly associated SNP ranged from 0.15% (rs43770847 in ADRM1) to 5.69% (rs209443540 in C5orf34), suggesting that these SNPs might be used in genetic selection for enhanced LP. The percentage of phenotypic variance explained by dominance effect ranged from 0.24% to 1.35% which suggests that genetic selection for enhanced LP might be more efficient by inclusion of dominance effects. Overall, this study identified several potentially functional variants that might be useful for selection programs for higher LP. Finally, a combination of identification of potentially functional variants followed by targeted genotyping and association analysis is a cost-effective approach for increasing the power of genetic association studies.

scholarly journals Murine Genetic Background Has a Stronger Impact on the Composition of the Gut Microbiota than Maternal Inoculation or Exposure to Unlike Exogenous Microbiota

2019 ◽  
Vol 85 (18) ◽  
Author(s):  
Hila Korach-Rechtman ◽  
Shay Freilich ◽  
Shiran Gerassy-Vainberg ◽  
Keren Buhnik-Rosenblau ◽  
Yael Danin-Poleg ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Inbred Lines ◽  
Mouse Genetics ◽  
Rrna Gene ◽  
Continuous Exposure ◽  
Microbiota Composition ◽  
Microbial Composition ◽  
Cross Model ◽  
Host Genetics ◽  
Reciprocal Hybrids

ABSTRACT The gut microbiota is a complex ecosystem, affected by both environmental factors and host genetics. Here, we aim at uncovering the bacterial taxa whose gut persistence is controlled by host genetic variation. We used a murine model based on inbred lines BALB/c and C57BL/6J and their F1 reciprocal hybrids (♀C57BL/6J × ♂BALB/c; ♀BALB/c × ♂C57BL/6J). To guarantee genetic similarity of F1 offspring, including the sex chromosomes, we used only female mice. Based on 16S rRNA gene sequencing, we found that the genetically different inbred lines present different microbiota, whereas their genetically identical F1 reciprocal hybrids presented similar microbiota. Moreover, the F1 microbial composition differed from that of both parental lines. Twelve taxa were shown to have genetically controlled gut persistence, while none were found to show maternal effects. Nine of these taxa were dominantly inherited by the C57BL/6J line. Cohousing of the parental inbred lines resulted in a temporary and minor shift in microbiota composition, which returned back to the former microbial composition following separation, indicating that each line tends to maintain a unique bacterial signature reflecting the line. Taken together, our findings indicate that mouse genetics has an effect on the microbial composition in the gut, which is greater than maternal effect and continuous exposure to different microbiota of the alternative line. Uncovering the bacterial taxa associated with host genetics and understanding their role in the gut ecosystem could lead to the development of genetically oriented probiotic products, as part of the personalized medicine approach. IMPORTANCE The gut microbiota play important roles for their host. The link between host genetics and their microbial composition has received increasing interest. Using a unique reciprocal cross model, generating genetically similar F1 hybrids with different maternal inoculation, we demonstrate the inheritance of gut persistence of 12 bacterial taxa. No taxa identified as maternally transmitted. Moreover, cohabitation of two genetically different inbred lines did not dramatically affect the microbiota composition. Taken together, our results demonstrate the importance of the genetic effect over maternal inoculation or effect of exposure to unlike exogenous microbiota. These findings may lead to the development of personalized probiotic products, specifically designed according to the genetic makeup.

scholarly journals VarExp: Estimating variance explained by Genome-Wide GxE summary statistics

2017 ◽  
Author(s):  
Vincent Laville ◽  
Amy R. Bentley ◽  
Florian Privé ◽  
Xiafoeng Zhu ◽  
Jim Gauderman ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Genetics ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Ethical Challenges ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Variance Explained

AbstractMany genomic analyses, such as genome-wide association studies (GWAS) or genome-wide screening for Gene-Environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. In practice, this is commonly estimated using individual genotype data. However, using individual-level data faces practical and ethical challenges when the GWAS results are derived in large consortia through meta-analysis of results from multiple cohorts. In this work, we present a R package, “VarExp”, that allows for the estimation of the percentage of phenotypic variance explained by variants of interest using summary statistics only. Our package allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects, and main genetic and interaction effects jointly. Its implementation integrates all recent methodological developments on the topic and does not need external data to be uploaded by users.The R source code, tutorial and associated example are available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git.

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