Quantitative Methods for Genetic Epidemiology

Mayo Clinic Rochester, Rochester MN

Investigators

Linked publications, trials & patents

Abstract

DESCRIPTION (provided by applicant): Modern genomic epidemiology has rapidly evolved beyond initial expectations, primarily because of cutting- edge genetic assays and next-generation sequencing technologies combined with large well-characterized studies. Yet, novel statistical analysis methods that combine genomic annotation with measured genotypes and phenotypes have lagged behind, with most published genome wide association studies (GWAS) focused on single-marker (single nucleotide polymorphisms, SNPs) analyses. Recognizing that the majority of common genetic variants have small effects on traits, and that there are many associated variants, the time is ripe to re-harvest the many existing GWAS data sets, and many expected in the near future, by joining genomic annotation with GWAS results. Hence, we propose to develop new statistical and computational methods in order to scan all possible gene-sets using GWAS SNP data and public gene annotation. We also plan to develop penalized regression models to simultaneously model the effects of individual SNPs on a trait, the effects of genes on a trait, and the effects o gene-sets on a trait. This will allow incorporation of annotation when available, but not lose SNPs or genes when annotation is incomplete. Rare variants are likely to have a prominent role in the etiology of complex traits, and next-generation sequencing technologies will soon be affordable for large studies. We propose new strategies to screen for the association of rare variants with traits based on both the first- and second-moments of generalized regression models (as well as censored survival models). Finally, including annotation information into statistical models is particularly important for analyzing rare variants because they are sparse, and has potential to improve analyses for common SNPs, or even combining both rare and common variants into models. For this, we propose novel statistical methods based on kernel matrices that provide information on how regression coefficients should be fused according to similarities of variants based on genomic annotation.