Complex diseases are believed to be caused by a number of factors of both genetic and environmental nature, as well as lifestyle. Examples include cardiovascular (e.g., heart disease, hypertension), metabolic (e.g., diabetes), and neurological (e.g., Alzheimer's, Parkinson's, and autism) diseases, and cancer. The financial costs of these diseases on families and society are staggering, while their physical and emotional toll on individuals and their loved ones is incalculable. While early on it was expected that decoding the human genome would be an immediate precursor to gaining significant insight into the causes of complex diseases, it is clear now that progress in this area must come from unraveling the interplay of genes with environment, as well as with each other, through the system of biological pathways and related networks. Our goal in this proposal is the development of a novel network-guided statistical methodology to facilitate the discovery of gene-environment (GxE) and gene-gene (GxG) interactions associated with complex quantitative traits associated with human disease. Specifically, we will (1) develop a class of sparse, network-guided regression models for detection of GxE and GxG interactions, (2) extend the applicability of this regression framework to multiple cohorts through the development of a two-stage meta-analysis strategy, and (3) assess the overall methodology both in simulation and using data from two specific disease areas: diabetes-related quantitative traits and pulmonary quantitative traits and diseases. The data analyses will be done in conjunction with colleagues at the Framingham Heart Study and two consortia: MAGIC and CHARGE. Successful completion of the proposed research will yield a highly novel and coherent set of tools (including software implementation) for a principled and biologically- informed two-stage approach to detecting GxE and GxG interactions associated with human disease in current large-scale, multi-cohort association analyses. Ultimately, our work should help to significantly accelerate the development of targeted therapies and personalized medicine strategies, through its fundamental impact on the early stages of the overall process.
Our work will advance significantly the ability of scientists to unravel the interplay of genes with environment, as well as with each other, as they relate to complex human diseases, such as cardiovascular disease, diabetes, Alzheimer's disease, and cancer. Ultimately, our work should help to significantly accelerate the development of targeted therapies and personalized medicine strategies, through its fundamental impact on the early stages of the overall process.
| Lu, Chen; O'Connor, George T; Dupuis, Josée et al. (2016) Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies. Hum Hered 81:142-149 |
| Li, Yun; O'Connor, George T; Dupuis, Josée et al. (2015) Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies. Stat Appl Genet Mol Biol 14:265-77 |
| Chen, Han; Meigs, James B; Dupuis, Josée (2014) Incorporating gene-environment interaction in testing for association with rare genetic variants. Hum Hered 78:81-90 |
| Lu, Chen; Latourelle, Jeanne; O'Connor, George T et al. (2013) Network-guided sparse regression modeling for detection of gene-by-gene interactions. Bioinformatics 29:1241-9 |
