Unraveling gene function using gene co-expression networks in the domestic horse
Creators: name: Schaefer, Robert
name: Norton, Elaine
name: Mickelson, James
name: McCue, Molly E
Publisher: name: figshare
Year: 2017
Subjects: subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Computational Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 60408 Genomics
subject: 60411 Population, Ecological and Evolutionary Genetics
subject: 60114 Systems Biology

description: Genome-wide studies in the horse have successfully been used to describe domestication and selection, clinical disease, athletic performance, and population structure and dynamics. Despite their success, the causal genes and functional mechanism underlying many of these genome-wide studies remain unknown. In particular, genome wide association studies (GWAS) for complex traits in the horse have been limited by poor resolution in SNP boundaries as well as potentially dozens of different effective loci being associated with a trait of interest. <br>One such example is our recent GWAS using 11 different monomorphic, biochemical, and hormonal measurements that define Equine Metabolic Syndrome (EMS) which identified &gt;2,000 SNPs representing 183 regions of the genome that were associated with these traits. Our next challenge is to identify those genes and alleles within the ROIs that contribute to EMS pathophysiology and metabolic variation. This is an arduous task as: 1) these ROIs span ~98.5 million base pairs (bp) and contain &gt; 3,000 genes; 2) little is known about EMS cellular and molecular pathophysiology, thus prioritization is biased towards genes involved with metabolic derangements in humans/ model organisms; and 3) many genes have unknown or unanticipated functions. To mitigate these issues, here we systematically integrate whole genome SNP data, tissue specific RNAseq, and serum metabolomic data in order to better describe and inter-relate putative genomic loci associated with Equine Metabolic Syndrome. Independently, tissue specific gene co-expression networks were built from skeletal muscle and tail-head adipose depot in 28 horses from four breeds that displayed varying signs of EMS. Differential expression analysis between extreme phenotype scores quantifying EMS in these 28 horses identifies 529 genes among muscle and adipose tissues indicating these tissues are biologically active for our trait. In muscle/adipose networks, there were 6.5X and 8.3X enrichment for co-expression among genes within AgriGO terms indicating networks capture biologically relevant information. Functional analysis for EMS was performed by directly extracting co-expression interactions among genes that were within breed specific haplotype windows containing associated GWAS SNPs. From our starting set of 2,375 associated GWAS SNPs we discover 259 genes with co-expression evidence related to EMS. Future work will focus on building disease specific, differential co-expression networks targeting specific changes in transcription between EMS and healthy individuals as well as corroborating metabolite abundances from Welsh Ponies affected by EMS.