Meta-analysis is a statistical method to combine results from multiple clinical or genomic studies with the same or similar research problems. It has been widely use to increase statistical power in finding clinical or genomic differences among different groups. One major category of meta-analysis is combining p-values from independent studies and the Fisher's method is one of the most commonly used statistical methods. However, due to heterogeneity of studies, particularly in the field of genomic research, with thousands of features such as genes to assess, researches often desire to discover this heterogeneous information when identify differentially expressed genomic features. To address this problem, \citet{Li2011adaptively} proposed very interesting statistical method, adaptively weighted (AW) Fisher's method, where binary weights, $0$ or $1$, are assigned to the studies for each feature to distinguish potential zero or none-zero effect sizes. \citet{Li2011adaptively} has shown some good properties of AW fisher's method such as the admissibility. In this paper, we further explore some asymptotic properties of AW-Fisher's method including consistency of the adaptive weights and the asymptotic Bahadur optimality of the test.