A Novel Powerful Joint Analysis with Data Fusion in Two-stage Case–Control Genome-wide Association Studies

Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work has been done on continuous (quantitative) traits, since the statistical significance of these robust tests is difficult to calculate. In this paper, we develop a powerfulF-statistic-based robust joint analysis method for quantitative traits using the combined raw data from both stages in the framework of two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and power. We show using simulations that the proposed method is substantially more robust than theF-test based on the additive model when the underlying genetic model is unknown. An example for rheumatic arthritis (RA) is used for illustration.

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Faculty Opinions recommendation of Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1030645.365484 ◽

2006 ◽

Author(s):

Pui-Yan Kwok

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Two Stage ◽

Genome Wide

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Faculty Opinions recommendation of Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1030645.360432 ◽

2006 ◽

Author(s):

Jurg Ott

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Two Stage ◽

Genome Wide

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A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

Journal of Probability and Statistics ◽

10.1155/2012/642403 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Jingyuan Zhao ◽

Zehua Chen

Keyword(s):

Logistic Regression ◽

Association Studies ◽

Case Control ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Two Stage ◽

Genome Wide ◽

Regression Approach ◽

Penalized Logistic Regression ◽

Selection Stage

We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by usingL1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and Jeffrey’s Prior penalty (Firth, 1993), a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008). The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS) project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005) and the LASSO-patternsearch algorithm (Shi et al. 2007).

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