Breeding and Genetics Symposium: building single nucleotide polymorphism-derived gene regulatory networks: Towards functional genomewide association studies.

The advent of economically viable high-throughput genetic and genomic techniques has equipped animal geneticists with an unprecedented ability to generate massive amounts of molecular data. As a result, large lists of genes differentially expressed in many experimental conditions of interests have been reported and, likewise, the association of an ever growing number of DNA variants with phenotypes of importance is now a routine endeavor. Although these studies have greatly improved our understanding of the genetic basis of complex phenotypes, they have also revealed the difficulty in explaining more than a fraction of the genetic variance. Inspired by this data-rich and knowledge-poor dichotomy, systems biology aims at the formal integration of seemingly disparate datasets allowing for a holistic view where key properties of the systems emerge as an intuitive feature and enable the generation of testable hypotheses. Herein, we present 2 examples of integrating molecular data anchored in the power of gene network inference. The first example is concerned with the onset of puberty in Bos indicus-influenced cows bred in Australia. Using the results from genomewide association studies across a range of phenotypes, we developed what we termed an association weight matrix to generate a gene network underlying phenotypes of puberty in cattle. The network was mined for the minimal set of transcription factor genes whose predicted target spanned the majority of the topology of the entire network. The second example deals with piebald, a pigmentation phenotype in Merino sheep. Two networks were developed: a regulatory network and an epistatic network. The former is inferred based on promoter sequence analysis of differentially expressed genes. The epistatic network is built from 2-locus models among all pairwise associated polymorphisms. At the intersection between these 2 networks, we revealed a set of genes and gene-gene interactions of validated and de novo predicted relevance to the piebald phenotype. We argue that these new approaches are holistic and therefore more appropriate than traditional approaches for investigating genetic mechanisms underlying complex phenotypes of importance in livestock species.