Comparing apples and oranges: Equating the power of case-control and quantitative trait association studies

Yang, Jian, Wray, Naomi R. and Visscher, Peter M. (2010) Comparing apples and oranges: Equating the power of case-control and quantitative trait association studies. Genetic Epidemiology, 34 3: 254-257. doi:10.1002/gepi.20456


Author Yang, Jian
Wray, Naomi R.
Visscher, Peter M.
Title Comparing apples and oranges: Equating the power of case-control and quantitative trait association studies
Journal name Genetic Epidemiology   Check publisher's open access policy
ISSN 0741-0395
1098-2272
Publication date 2010-04-01
Sub-type Article (original research)
DOI 10.1002/gepi.20456
Volume 34
Issue 3
Start page 254
End page 257
Total pages 4
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Formatted abstract
Genome-wide association studies have achieved unprecedented success in the identification of novel genes and pathways implicated in complex traits. Typically, studies for disease use a case-control (CC) design and studies for quantitative traits (QT) are population based. The question that we address is what is the equivalence between CC and QT association studies in terms of detection power and sample size? We compare the binary and continuous traits by assuming a threshold model for disease and assuming that the effect size on disease liability has similar feature as on QT. We derive the approximate ratio of the non-centrality parameter (NCP) between CC and QT association studies, which is determined by sample size, disease prevalence (K) and the proportion of cases (v) in the CC study. For disease with prevalence <0.1, CC association study with equal numbers of cases and controls (v=0.5) needs smaller sample size than QT association study to achieve equivalent power, e.g. a CC association study of schizophrenia (K=0.01) needs only ~55% sample size required for association study of height. So a planned meta-analysis for height on ~120,000 individuals has power equivalent to a CC study on 33,100 schizophrenia cases and 33,100 controls, a size not yet achievable for this disease. With equal sample size, when v=K, the power of CC association study is much less than that of QT association study because of the information lost by transforming a quantitative continuous trait to a binary trait.
Keyword Association
Case-control study
Quantitative trait
Genome-wide association
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: ERA 2012 Admin Only
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Created: Tue, 14 Feb 2012, 02:39:19 EST by Mr Mathew Carter on behalf of School of Medicine