Background: A key goal of mining single nucleotide polymorphism data of complex
diseases (CD) is to build models that provide fundamental insight into genetic variations of CD.
Therefore, we can predict disease risk and clinical outcomes and ultimately understand the development
and progress mechanism of CD. As the technologies of omics data generation and computer science, the
reductionist paradigm of genome wide association study becomes less prevalent.
Conclusion: In this review, we summarize the different strategies for boosting the power of association
study, which include data quality improvement, high-performance computing platform and advanced
computational method. Using these complementary approaches, the fundamental mechanism of
genomic variations affecting occurrence and development of CD may be uncovered.