Grant: Operating Grant

To harness the full potential of new technological advances in genetics coupled with large-scale genomic information, we develop and apply novel analytical methods with enhanced power to optimize association information between genetic data and complex human traits. We propose an interdisciplinary research strategy, forging a collaboration of methodology development and application to the genetics of Type 1 diabetes (T1D) complications and the genetic modification of Cystic Fibrosis (CF). Our previous use of this research model has provided important insights into the mechanisms of T1D and CF disease severities. Current ability to comprehensively survey the whole-genome for disease susceptibility has increased immensely as sequence and other genome data are being generated at an unprecedented rate. However, as the true complexity of many genetic diseases is being revealed, there is a pressing need for more advanced analytical strategies. For example, in our recent study of meconium ileus in individuals with CF, genes discovered by standard genome-wide association study (GWAS) approaches explain only 5% of the variation in the disease phenotype, while loci uncovered by our novel hypothesis-driven GWAS (GWAS-HD) method explain 17%. We now propose to develop novel association methods for newer, potentially more robust types of data including whole genome sequencing, and for X-chromosome data often omitted in many 'whole-genome' studies. Genes do not act alone and often interact with other genes or environmental factors. We propose a novel analytical method to identify variants with either main or interaction effects, or both. We also develop fine-mapping methods to accurately prioritize variants that are potentially causal within the genes identified in our T1D and CF studies, and studies of other complex human traits. As with previous work, we will disseminate our novel methodology so that others can employ our analytical approaches for their studies.