Inferring causal structures of gut microbiota diversity and feed efficiency traits in poultry using Bayesian learning and genomic structural equation models

Abstract

AbstractFeed and phosphorus (P) efficiency are of increasing importance in poultry breeding. It has been shown recently that these efficiency traits are influenced by the gut microbiota composition of the birds. The efficiency traits and the gut microbiota composition are partly under control of the host genome. Thus, the gut microbiota composition can be seen as a mediator trait between the host genome and the efficiency traits. The present study used data from 749 individuals of a Japanese quail F2 cross. The birds were genotyped for 4k single-nucleotide polymorphism (SNP) and trait recorded for P utilization (PU) and P retention (PR), body weight gain (BWG), and feed per gain ratio (F:G). The gut microbiota composition was characterized by targeted amplicon sequencing. The alpha diversity was calculated as the Pielou’s evenness index (Jʹ). A stable Bayesian network was established using a Hill-Climbing learning algorithm. Pielou’s evenness index was placed as the most upstream trait and BWG as the most downstream trait, with direct and indirect links via PR, PU, and F:G. The direct and indirect effects between Jʹ, PU, and PR were quantified with structural equation models (SEM), which revealed a causal link from Jʹ to PU and from PU to PR. Quantitative trait loci (QTL) linkage mapping revealed three genome-wide significant QTL regions for these traits with in total 49 trait-associated SNP within the QTL regions. SEM association mapping separated the total SNP effect for a trait into a direct effect and indirect effects mediated by upstream traits. Although the indirect effects were in general small, they contributed to the total SNP effect in some cases. This enabled us to detect some shared genetic effects. The method applied allows for the detection of shared genetic architecture of quantitative traits and microbiota compositions.