Title: Minimax powerful functional analysis of covariance tests for longitudinal genome-wide association studies
Authors: Sheng Xu - The Hong Kong Polytechnic University (China) [presenting]
Yehua Li - University of California at Riverside (United States)
Catherine Liu - The Hong Kong Polytechnic University (Hong Kong)
Abstract: The Alzheimers Disease (AD) related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies (GWAS) are modeled as sparse functional data and propose nonparametric test procedures to detect functional genotype effects while controlling the confounding effects of environmental covariates. Existing nonparametric tests do not take into account within-subject correlations, suffer from low statistical power and fail to reach the GWAS significance level. We propose a new class of functional analysis of covariance (fANCOVA) tests based on a seemingly unrelated kernel smoother, that can incorporate the correlations. We show that the proposed test combined with a uniformly consistent nonparametric covariance function estimator enjoys the Wilks property and is minimax most powerful. In an application to the Alzheimers Disease Neuroimaging Initiative data, the proposed test leads to the discovery of new genes that may be related to AD.