Robust joint analysis allowing for model uncertainty in two-stage genetic association studies |
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| Authors: | Dongdong Pan Qizhai Li Ningning Jiang Aiyi Liu Kai Yu |
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| Affiliation: | (1) Department of Statistics, Yunnan University, 650091 Kunming, PR China;(2) Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190 Beijing, PR China;(3) Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 20892 Bethesda, MD, USA;(4) Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 20892 Bethesda, MD, USA |
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| Abstract: | Background The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate. |
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