| Literature DB >> 33510859 |
Francesco Tiezzi1, Justin Fix2, Clint Schwab2,3, Caleb Shull3, Christian Maltecca1.
Abstract
A large number of studies have highlighted the importance of gut microbiome composition in shaping fat deposition in mammals. Several studies have also highlighted how host genome controls the abundance of certain species that make up the gut microbiota. We propose a systematic approach to infer how the host genome can control the gut microbiome, which in turn contributes to the host phenotype determination. We implemented a mediation test that can be applied to measured and latent dependent variables to describe fat deposition in swine (Sus scrofa). In this study, we identify several host genomic features having a microbiome-mediated effects on fat deposition. This demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. Host genomic variants identified through our analysis are different than the ones detected in a traditional genome-wide association study. In addition, the use of latent dependent variables allows for the discovery of additional host genomic features that do not show a significant effect on the measured variables. Microbiome-mediated host genomic effects can help understand the genetic determination of fat deposition. Since their contribution to the overall genetic variance is usually not included in association studies, they can contribute to filling the missing heritability gap and provide further insights into the host genome - gut microbiome interplay. Further studies should focus on the portability of these effects to other populations as well as their preservation when pro-/pre-/anti-biotics are used (i.e. remediation).Entities:
Keywords: BEL, Weight of the belly cut; BF1, Backfat depth measured in vivo at the age of 118.1±1.16 d; BF2, Backfat depth measured in vivo at the age of 145.9±1.53 d; BF3, Backfat depth measured in vivo at the age of 174.3±1.43 d; BF4, Backfat depth measured in vivo at the age of 196.6±8.03 d; BFt, Backfat measured post mortem (after slaughter at 196.6±8.03 d); Causal effect; FATg, Latent variable built on BF1, BF2, and BF3; FATt, Latent variable built on BF4, BFt, and BEL; Fat deposition; G, host genomic features, represented in this study by SNP; Gut microbiome; Latent variables; M, gut microbiome features, represented in this study by OUT; Mod1, Model 1, used to estimate the total effect of G on P. Reported in Fig. 1a; Mod1L, Model 1L, used to estimate the total effect of G on; Mod2, Model 2, used to estimate the effect of M on P. Reported in Fig. 1b; Mod2L, Model 2L, used to estimate the effect of M on; Mod3, Model 3, used to estimate the effect of G on M. Reported in Fig. S1; Mod4, Model 4, used to estimate the direct and mediated effects of G on P. Reported in Fig. 1c; Mod4L, Model 4, used to estimate the direct and mediated effects of G on. Reported in Fig. 1d; OUT, Operational Taxonomic Units; P, Phenotype recorded on the host; S2a, S2b, S3a, S3b, S3c, Gut microbiome OUT selected used as mediator variables. See Table 2; SEM, Structural equation model; SNP, Single Nucleotide Polymorphism marker; Π, Latent variable built on the P variables
Year: 2020 PMID: 33510859 PMCID: PMC7809165 DOI: 10.1016/j.csbj.2020.12.038
Source DB: PubMed Journal: Comput Struct Biotechnol J ISSN: 2001-0370 Impact factor: 7.271