A Functional Network in the Horse: Discovering Causal Variants for Complex Disease

The recent availability of high-throughput technologies in agricultural animals provides an opportunity to advance our understanding of complex agriculturally important traits. Genome wide association studies have identified thousands of loci linked to these traits; however in most cases the causal genes remain unknown.

Linking phenotype to causal genotype is hindered by: 1) poor resolution of QTLs resulting in tens to hundreds of genes significantly associated with a trait; 2) limited knowledge regarding the biological function of many genes; and 3) incomplete understanding of the physiology or cellular mechanisms underlying the trait of interest. Particularly in agricultural animals, these issues hamper the interpretation and narrowing of large candidate gene lists. Analysis of a single data type is typically unsatisfactory in explaining complex traits that exhibit variation across multiple levels of biological regulation. Integrating GWAS with robust genomic information such as gene expression is necessary for efficient ranking of interesting candidate regions discovered by association mapping.

Here, we expand open-source software called Camoco (CoAnalysis of Molecular Components) to integrate common sources of genomic data in the horse. Camoco combines GWAS and gene expression to build a functional network for prioritization of QTL candidate genes. In the context of the horse, we use Camoco to build co-expression networks from 11 different tissues (6 different adipose depots, liver, skeletal muscle, synovium, bone, and cartilage) and present a SNP-to-gene mapping extension which considers breed-specific haplotype. Using the horse, we establish an integrated, functional network based approach from which agronomically important traits can quickly be analyzed.