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Background: Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases. Results: In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn’s disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations. Conclusions: Our network propagation method applied to a genome-wide association study increases association findings over other approaches. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world accounting for 4 percent of deaths from cancer in women1. We performed a three-phase genome-wide association study of EOC survival in 8,951 EOC cases with available survival time data, and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.11, rs8170 and rs2363956, showed evidence of association with survival (overall P=5×10−4 and 6×10−4), but did not replicate in phase 3. However, the same two SNPs demonstrated genome-wide significance for risk of serous EOC (P=3×10−9 and 4×10−11 respectively). Expression analysis of candidate genes at this locus in ovarian tumors supported a role for the BRCA1 interacting gene C19orf62, also known as MERIT40, which contains rs8170, in EOC development.

In this chapter, we give a short introduction to the genetics of complex diseases emphasizing evolutionary models for disease genes and the effect of different models on the genetic architecture, and we give a survey of the state-of-the-art of genome-wide association studies (GWASs).

In this chapter, we give a short introduction to the genetics of complex disease with special emphasis on evolutionary models for disease genes and the effect of different models on the genetic architecture, and finally give a survey of the state-of-the-art of genome-wide association studies.