Methods for Mining Single Nucleotide Polymorphism Data of Complex Diseases

Title:Methods for Mining Single Nucleotide Polymorphism Data of Complex Diseases

Volume: 13 Issue: 4

Author(s): Xiong Li*

Affiliation:

• School of Software, East China Jiaotong University, Nanchang 330013,China

Keywords: Complex diseases, epistasis, systems biology, cloud computing, data integration.

Abstract: 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.

Cite this article as:

Li Xiong *, Methods for Mining Single Nucleotide Polymorphism Data of Complex Diseases, Current Bioinformatics 2018; 13 (4) . https://dx.doi.org/10.2174/1574893612666170619083537