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Abstract:
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Motivated by a genome-wide association study (GWAS) to discover risk variants for the progression of Age-related Macular degeneration (AMD), we develop a computationally efficient copula-based score test, of which the association between bivariate progression times is explicitly modeled. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards assumption are considered. We further extend our work to gene-based tests through the functional linear smoothing approach. Extensive simulation studies were conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method on a large randomized trial data, Age-related Eye Disease Study (AREDS), to identify susceptible risk variants and gene regions for AMD progression. The top variants identified in Chromosome 10 (ARMS2 gene) show differential progression profiles for different genetic groups, which are useful in characterizing and predicting the risk of progression for patients with moderate AMD.
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