Exploring causal networks underlying fat deposition and muscularity in pigs through the integration of phenotypic, genotypic and transcriptomic data.

Francisco Peñagaricano,Juan P Steibel,Ronald O Bates,Catherine W Ernst,Bruno D Valente,Guilherme Jm Rosa,Hasan Khatib

doi:10.1186/s12918-015-0207-6

Francisco Peñagaricano, Juan P Steibel + Show 5 more

Open Access

https://doi.org/10.1186/s12918-015-0207-6

Copy DOI

Abstract

BackgroundJoint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e. undirected connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F2 Duroc x Pietrain resource pig population.ResultsWe initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region on SSC6 showed significant associations with three relevant phenotypes, off-midline10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of seven genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the three phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight.ConclusionsOur findings shed light on the antagonist relationship between carcass fat deposition and lean meat content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes.

Highlights

Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables
Namely loin muscle weight, off-midline 10th-rib backfat thickness (BF10), and average intramuscular fat percentage, showed significant phenotypic QTL (pQTL) at 5 % genome-wise significant level. These three significant pQTL mapped to the same genomic region on chromosome 6 (SSC6) of the pig genome (Fig. 2)
These findings provide some evidence of the existence of a genomic region on SCC6 with additive pleiotropic effects affecting both fat deposition and muscularity

Summary

Introduction

Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Many quantitative trait loci (QTL) influencing different phenotypes have been reported in the last two decades [1]. One way to unravel the molecular mechanisms underlying a phenotype of interest is to expand the type of traits under genetic analysis. One of such traits may be the abundance of messenger RNA transcripts, i.e., gene expression measurements. The co-localization of expression QTL (eQTL) with phenotypic QTL (pQTL)

Objectives

Methods

Results

Conclusion