Genome-wide association (GWA) studies have been successfully applied in detection of susceptibility loci for complex diseases, but most of the identified variants have a large to moderate effect, and explain only a limited proportion of the heritability of the diseases. It is believed that the majority of the latent risk alleles have very small risk effects that are difficult to be identified and GWA study may have inadequate power in dealing with those small effect variants. Researchers will often collect other phenotypic information in addition to disease status to maximize the output from the study. Some of the phenotypes can be on the pathway to the disease, i.e., intermediate phenotype. Statistical methods based on both the disease status and intermediate phenotype should be more powerful than a case-control study as it incorporates more information. Meta-analysis has been used in genetic association analysis for many years to combine information from multiple populations, but never been used in a single population GWA study. In this study, simulations were conducted and the results show that when an intermediate phenotype is available, the meta-analysis incorporating the disease status and intermediate phenotype information from a single population has more power than a case-control study only in GWA study of complex diseases, especially for identification of those loci that have a very small effect. And compared with Fisher's method, the modified inverse variance weighted meta-analysis method is more robust as it is more powerful and has a lower type I error rate at the same time, which provides a potent approach in detecting the susceptibility loci associated with complex diseases, especially for those latent loci whose effect are very small. In the meta-analysis of lung cancer with smoking data, the results replicate the signal in \emph{CHRNA3} and \emph{CHRNA5} genes on chromosome 15q25. Some new signals in \emph{CYP2F1} on chromosome 19, \emph{SUMF1} on chromosome 3, and \emph{ARHGAP10} on chromosome 4 are also detected. And the \emph{CYP2F1} gene, close to the already known cigarette-induced lung cancer gene \emph{CYP2A6}, is highly possible another cytochrome P450 (CYP) gene that is related to the smoking-involved lung cancer. The meta-analysis of rheumatoid arthritis with anti-cyclic citrullinated peptide (anti-CCP) data identified new signals on 9q24 and 16q12. There are evidences these two regions are involved in other autoimmune diseases and different autoimmune/inflammatory diseases may share same genetic susceptibility loci. Both the theoretical and empirical studies show that the modified variance weighted meta-analysis method is a robust method and is a potent approach in detecting the susceptibility loci associated with complex diseases when an intermediate phenotype is available.