Two-Stage Procedures for the Identification of Gene × Environment and Gene × Gene Interactions in Genome-Wide Association Studies

Author(s):  
Charles Kooperberg ◽  
James Y. Dai ◽  
Li Hsu

Genome-wide association studies and next generation sequencing studies offer us an unprecedented opportunity to study the genetic etiology of diseases and other traits. Over the last few years, many replicated associations between SNPs and traits have been published. It is of particular interest to identify how genes may interact with environmental factors and other genes. In this chapter, we show that a two-stage approach, where in the first stage SNPs are screened for their potential to be involved in interactions, and interactions are then tested only among SNPs that pass the screening can greatly enhance power for detecting gene-environment and gene-gene interaction in large genetic studies compared to the tests without screening.

2020 ◽  
Vol 10 (7) ◽  
pp. 1776-1784
Author(s):  
Shudong Wang ◽  
Jixiao Wang ◽  
Xinzeng Wang ◽  
Yuanyuan Zhang ◽  
Tao Yi

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.


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