Statistical Methods for Data Integration and Applications to Genome-wide Association Studies

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Abstract Large-scale epidemiologic studies, including biobanks and genome-wide association studies (GWAS), are now rapidly leading to the identification of novel risk factors for complex diseases. There is now increasing opportunity to develop comprehensive models for disease risk incorporating genetic markers, other biomarkers, life-style factors and sociodemographic indicators. There are, however, major challenges as information on all of the potential risk factors are often not available in a single adequately large study. Instead, information may be available from different studies, each of which may include some subsets of the desired variables. Further, because of logistical and privacy concerns with individual level data, only summary-level information, i.e., estimates of model parameters, may be available from some studies. We propose to develop a series of novel statistical methods that will allow data integration across disparate datasets to tackle modern problems faced in genetics and more broadly, observational epidemiologic studies. In Aim 1, we will develop a general framework for building logistic regression models using detail covariate data from a main study, while incorporating summary-statistics information from an external study. We will develop a series of applications of this framework to GWAS where we will use covariate data, including high- throughput biomarkers, from biobanks and perform combined analysis with external summary- statistics data for powerful exploration of effect modification and mediation of genetic associations by covariates. In Aim 2, we will extend the proposed framework of Aim 1 for developing models with high-dimensional covariates with regularized parameter estimates. We will develop application of the proposed method for fine-mapping and polygenic risk score analysis conditional on covariates. In Aim 3, we will further develop multiple novel applications of the data integration framework to account for different accuracy/depth of disease outcome data across different studies. We will illustrate application of the proposed methods for risk modeling of multiple cancers (breast, melanoma and lung), two cardiometabolic traits (type-2 diabetes and coronary artery disease) and a psychiatric disorder (major depression disorder) using individual level data from the UK Biobank study and Breast Cancer Association Consortium, and external GWAS summary-statistics. We will distribute develop and freely distribute user friendly software.