Gene prioritization for livestock diseases by data integration

Li Jiang, Peter Sørensen, Bo Thomsen, Stefan McKinnon Høj-Edwards, Axel Skarman, Christine M Røntved, Mogens S Lund, Christopher Workman

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches towards identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking of genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information of disease or trait phenotypes, gene-associated phenotypes and protein-protein interactions. It was used for ranking all known genes present in cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to E. coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions and biomedical text data were integrated systematically for ranking candidate genes in any livestock species.
Original languageEnglish
JournalPhysiological Genomics
Pages (from-to)305-317
Number of pages13
Publication statusPublished - Mar 2012


  • protein complexes
  • phenotype similarity
  • gene expression


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