title: Design and analysis of multiple diseases genome-wide association studies without controls.
abstract: In genome-wide association studies (GWAS), multiple diseases with shared controls is one of the case-control study designs. If data obtained from these studies are appropriately analyzed, this design can have several advantages such as improving statistical power in detecting associations and reducing the time and cost in the data collection process. In this paper, we propose a study design for GWAS which involves multiple diseases but without controls. We also propose corresponding statistical data analysis strategy for GWAS with multiple diseases but no controls. Through a simulation study, we show that the statistical association test with the proposed study design is more powerful than the test with single disease sharing common controls, and it has comparable power to the overall test based on the whole dataset including the controls. We also apply the proposed method to a real GWAS dataset to illustrate the methodologies and the advantages of the proposed design. Some possible limitations of this study design and testing method and their solutions are also discussed. Our findings indicate that the proposed study design and statistical analysis strategy could be more efficient than the usual case-control GWAS as well as those with shared controls.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN 47405-7109, USA. zc3@indiana.edu
authors.forename: Zhongxue
authors.initials: Z
authors.lastname: Chen
authors.forename: Hanwen
authors.initials: H
authors.lastname: Huang
authors.forename: Hon Keung Tony
authors.initials: HK
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D003198:Computer Simulation; D035061:Control Groups; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide; D012107:Research Design