The efforts of the human genome project are beginning to provide important findings for human health. Technological advances in the laboratory, particularly in characterizing human genomic variation, have created new approaches for studying the human genome. However, current statistical and computational strategies are taking only partial advantage of this wealth of information. In the quest for disease susceptibility genes for common, complex disease, we are faced with many challenges. Selecting genetic, clinical, and environmental factors important for the trait of interest is increasingly more difficult as high throughput data generation technologies are developed. We know that genes do not act in isolation, thus numerous other factors are likely important in complex disease phenotypes. Ultimately, we want to know what factors are important to provide superior prevention, diagnosis, and treatment of human disease. Unfortunately, interpretation of statistical models in a meaningful way for biomedical research has been lacking due to the inherent difficulty in making such connections.
One of the emerging areas for exploring the architecture of complex traits includes phenome-wide association studies, or PheWAS. It is conceivable that be exploiting the interrelationships between traits, we can uncover novel mechanistic insights and identify evidence for pleiotropy. A number of strategies are being explored to capitalize on the network of phenotypes to then test for association with genotypes. In this presentation, a number of PheWAS examples will be provided as well as next steps in the quest for understanding the architecture of complex traits.
Citation Format: Marylyn D. Ritchie, Pendergrass A. Sarah. The phenome and pleiotropy: Dissecting the architecture of complex traits. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr IA07.