PURPOSE: We present an approach to prioritize single nucleotide polymorphisms 
for further follow-up in genome-wide association studies of type 2 diabetes.
METHOD: The proposed method combines both the use of open data access from two 
type 2 diabetes-genome-wide association studies (granted by the Diabetes 
Genetics Initiative and the Welcome Trust Case Control Consortium) and the 
comprehensive analysis of candidate regions generated by the freely accessible 
ENDEAVOUR software.
RESULTS: The algorithm prioritized all genes of the whole genome in relation to 
type 2 diabetes. There were six of 1096 single nucleotide polymorphisms in five 
genes potentially associated with type 2 diabetes: tachykinin receptor 3 
(rs1384401), anaplastic lymphoma receptor tyrosine kinase (rs4319896), calcium 
channel, voltage-dependent, L type, alpha 1D subunit (rs12487452), FOXO1A 
(rs10507486 and rs7323267), and v-akt murine thymoma viral oncogene homolog 3 
(rs897959). We estimated the fixed effect and P values of each single nucleotide 
polymorphism in the combined dataset by Mantel-Haenszel meta-analysis and we 
observed significant P values for all single nucleotide polymorphisms except for 
rs897959 at v-akt murine thymoma viral oncogene homolog 3.
CONCLUSION: The proposed strategy may be used as an alternative tool for 
optimizing the information of the nearly 500,000 gene variants in which markers 
with modest significant P value for disease association are currently 
disregarded. Additionally, the said single nucleotide polymorphisms may be 
incorporated into the replication of the multistage design involved in the 
genome-wide association studies.