Meeting Abstract

S7-9  Monday, Jan. 6 13:30 - 14:00  Using Camoco to integrate genome-wide association studies with context specific co-expression networks in corn and horses SCHAEFER, RJ*; BAXTER, I; MCCUE, ME; University of Minnesota, St Paul, MN; Donald Danforth Plant Science Center, St Louis, MO; University of Minnesota, St Paul, MN

High throughput technologies are currently a major driver for genetic improvement in many domestic and ecological plant and animal species. In the past decade, genome wide studies (GWAS) have associated changes in DNA to variation in phenotypes of interest. Hundreds of links between genetic markers (SNPs) and important traits have been identified by GWAS. Yet, the causal gene/allele often remains unknown due to many genes being in linkage disequilibrium (LD) with each of potentially dozens of genetic markers. Co-expression networks identify genes that share similar response patterns of gene expression, making them a powerful tool for inferring the biological function of under-characterized genes. In the right biological context, sets of causal genes related to a GWAS trait will exhibit strong co-expression while inconsequential genes in LD with the marker exhibit random patterns of co-expression. Here, we showcase the functionality of Camoco, a computational framework developed to integrate GWA studies with gene co-expression networks. Camoco was used to build gene co-expression networks in many species, however this talk will focus on demonstrative use-cases in maize and the domestic horse. Using Camoco, we built gene co-expression networks in several different biological contexts. Networks were benchmarked for biological signal using curated ontologies (e.g. GO) as well as unsupervised network clustering. Once vetted, networks are used to interpret and prioritize GWAS data using an integrative “overlap” algorithm. Genes are prioritized based on the strength of co-expression among other GWAS tagged genes. Camoco is open source software and available at