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Abstract:
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Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphisms (SNP) at a time. We aim to select over half a million SNPs and time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. We develop and apply a novel penalized quantile generalized estimating equations (GEE) approach, incorporating several key aspects including ultra-high dimensional genetic SNPs, the longitudinal nature of blood pressure measurements, time-varying covariates, and conditional high quantiles of blood pressure. Importantly, we identify interesting new SNPs and some plausible SNP pathways for high blood pressure. Besides, we find blood pressure levels are likely heterogeneous, where the important risk factors identified differ among quantiles. We provide an efficient computational algorithm and establish theoretical properties that are challenging due to the non-smooth objective function, non-convex penalty function and ultra high dimensions
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