|
Abstract:
|
Important sources of unexplained disease heritability arise from a wide spectrum of genetic variants along with gene-environment interactions. While the next generation sequencing technologies and imputing current GWAS data to larger reference panels (e.g. the 1000 genomes project) provide two efficient ways to discover additional variants, statistical approaches for linking the high-dimensional genetic variants to disease phenotypes are not well-developed. We propose utilizing novel statistical approaches to investigate the joint contributions of imputed/sequenced genetic variant profiles to disease susceptibility and prognosis, along with how the variant profile effects are modified by other environmental factors. Specifically, we develop a unified functional data analysis framework and procedure to reduce data dimension and to model the integrative effects of wide spectrum of genetic variant profiles and environmental factors. We demonstrate the performance of our methods by extensive simulations and application to large genetic epidemiological studies of melanoma with well-characterized data: the Genes, Environment and Melanoma study, and the MD Anderson Cancer Center Study.
|
