Xiong Li* Pages 367 - 372 ( 6 )
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.
Complex diseases, epistasis, systems biology, cloud computing, data integration.
School of Software, East China Jiaotong University, Nanchang 330013