An Integrated, 'Big-Data' Approach to Accelerate Gene Discovery in Diabetic Kidney Disease
Marcus Pezzolesi   (Salt Lake City, UT)
Diabetic kidney disease (DKD) is a complex, heterogeneous complication of diabetes. Genetic factors are known to contribute to DKD susceptibility, however, despite intense effort, the identification of variants that underlie its risk has been challenging. Taking advantage of insights from previous studies, as well as epidemiologic studies on the natural history of DKD, we propose a novel, highly innovative ‘big-data’ approach to accelerate gene discovery in DKD that integrates data from the Utah Population Database (UPDB), a unique population-based genealogy resource containing family histories and demographic data for 14 million individuals, electronic health records for 2.1 million individuals in the UPDB, and high throughput next-generation sequencing technology. The goal of this Pilot and Feasibility project is to apply this approach to i) identify high-risk DKD families that are enriched for rapid progression of renal function decline, the predominant clinical feature of DKD (Specific Aim 1), and ii) to initiate whole-genome sequencing (WGS) in these families to begin determining the contribution of variation across the entire genome on renal function decline in DKD (Specific Aim 2). Using more than 200,000 diabetic patients in the UPDB, we will establish estimated glomerular filtration rate (eGFR) trajectories using longitudinal measures of eGFR obtained from electronic health records from the University of Utah Health Sciences Center Hospital and Clinics (UUHSC) and Intermountain Healthcare (IHC) Enterprise Data Warehouses, our electronic health record repositories, determine the familiality of rapid renal function decline among these diabetic patients, and identify high-risk families enriched for rapid renal function decline. After identifying and prioritizing these families, we will identify select individuals to optimize WGS-based gene discovery power using innovative tools developed at the University of Utah and perform WGS-based gene discovery in these individuals to identify genes for rapid renal function decline. After completing this Pilot and Feasibility project, we anticipate that we will have identified as many as 50 to 100 (or more) large, multi-generational pedigrees that are enriched for progression of rapid renal function decline. As part of this project, we will perform WGS-based gene discovery in a subset of these families as proof-of-concept of our integrative ‘bigdata’ approach. The data generated through these efforts will be a springboard for further WGS-based gene discovery studies in the remaining families and future studies on the mechanisms of progressive function renal decline in diabetes.