With the explosion of high-throughput data, an effective integrative analysis is needed to decipher the knowledge accumulated in multiple studies. However, batch effects, patient heterogeneity, and disease complexity all complicate the integration of data from different sources. Here we introduce TOMAS, a novel meta-analysis framework that transforms the challenging meta-analysis problem into a set of standard analysis problems that can be solved efficiently. This framework utilizes techniques based on both p-values and effect sizes to identify differentially expressed genes and their expression change on a genome-scale. The computed statistics allow for topology-aware pathway analysis of the given phenotypes, where topological information of genes is taken into consideration. We compare TOMAS with four meta-analysis approaches, as well as with three dedicated pathway analysis approaches that employ multiple datasets (MetaPath). The eight approaches have been tested on 609 samples from 9 Alzheimer’s studies conducted in independent labs for different sets of patients and tissues. We demonstrate that the topology based meta-analysis framework overcomes noise and bias to identify pathways that are known to be implicated in Alzheimer’s disease. While presented here in a genomic data analysis application, the proposed framework is sufficiently general to be applied in other research areas.