Combined genetic, genomic and transcriptomic methods in the analysis of animal traits.

Abstract

Abstract This paper reviews advances and current status in genetic, genomic and transcriptomic investigations of quantitative traits in livestock species and proposes possible strategies for integrating the vast amounts of information (outputs/datasets) arising from all three investigations into a systems genetics approach. This systems genetics approach is characterized by investigations of the genetic basis of -omic variations and gene regulatory/co-regulatory networks. This review is structured as follows. Genetics/genomics, transcriptomics, combined transcriptomics-genomics and, finally, systems genetics/biology. In the genetics or genomics part, we discuss current status and advances in quantitative trait loci (QTLs) mapping to fine-map multiple causative/candidate genes. We discuss the potential of single nucleotide polymorphism (SNP) genotyping arrays (e.g. Affymetrix GeneChip arrays) to identify SNP-trait associations and fine-map QTLs to less than 0.1 cM. We provide references and resources for SNP analytical methods and tools. In the transcriptomics part, we formulate common biological questions relevant to livestock microarray gene expression profiling (MGEP) experiments to detect differentially expressed (DE) genes. In the combined transcriptomics-genomics (known as genetical genomics), we discuss detection and mapping of expression QTL (eQTL) that harbour regulatory genes for normal trait QTLs or candidate genes detected under 'genetics' and 'genomics' approaches. Illustrations from our own recent genetical genomic investigations in mouse and pigs are provided. One of the current challenges in genetical genomics is to have a coherent approach to design an experiment with due considerations for sample size and controlling false discovery rates (FDRs), while maintaining statistical power to detect eQTLs. Here we provide some concepts for the design of genetical genomics experiments with factors affecting FDR and power. This is followed by a systems biology/genetics section where we focus on combining research outputs from four different sources, namely: genetics or genomics (e.g. SNP, QTL and eQTLs), transcriptomics (e.g. DE genes, gene networks, and heritability of gene transcripts) and phenomics (e.g. phenotypes and pedigree). As a proof of principle, we provide results from systems biology/genetics approach anchored to a gene network for myogenin (MYOG), a muscle-specific transcription factor essential for the development of skeletal muscle. Using bovine expression datasets and in silico approaches, we built a gene network for MYOG that represents 35 significant connections between genes in this network. In conclusion, this paper provides strategies and tools to integrate data from genetics and -omics experiments in animals and highlighted the value of such 'systems genetics' strategy in understanding the genetics of complex traits.