  The development of next generation sequencing (NGS) technology and genotype
imputation methods enabled researchers to measure both common and rare variants
in genome-wide association studies (GWAS). Statistical methods have been
proposed to test a set of genomic variants together to detect if any of them is
associated with the phenotype or disease. In practice, within the set of
variants, there is an unknown proportion of variants truly causal or associated
with the disease. Because most developed methods are sensitive to either the
dense scenario, where a large proportion of the variants are associated, or the
sparse scenario, where only a small proportion of the variants are associated,
there is a demand of statistical methods with high power in both scenarios. In
this paper, we propose a new association test (weighted Adaptive Fisher, wAF)
that can adapt to both the dense and sparse scenario by adding weights to the
Adaptive Fisher (AF) method we developed before. Using both simulation and the
Genetic Analysis Workshop 16 (GAW16) data, we have shown that the new method
enjoys comparable or better power to popular methods such as sequence kernel
association test (SKAT and SKAT-O) and adaptive SPU (aSPU) test.
