A simulation study investigating power estimates in phenome-wide association studies
Abstract Background Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls fo...
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BMC
2018-04-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2135-0 |
| _version_ | 1817997799311015936 |
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| author | Anurag Verma Yuki Bradford Scott Dudek Anastasia M. Lucas Shefali S. Verma Sarah A. Pendergrass Marylyn D. Ritchie |
| author_facet | Anurag Verma Yuki Bradford Scott Dudek Anastasia M. Lucas Shefali S. Verma Sarah A. Pendergrass Marylyn D. Ritchie |
| author_sort | Anurag Verma |
| collection | DOAJ |
| description | Abstract Background Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance. Results We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants. Conclusions This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses. |
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| id | doaj.art-9504dcdb14c74f35a8705d8ec0ff5273 |
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| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-14T02:43:37Z |
| publishDate | 2018-04-01 |
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| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-9504dcdb14c74f35a8705d8ec0ff52732022-12-22T02:16:39ZengBMCBMC Bioinformatics1471-21052018-04-011911810.1186/s12859-018-2135-0A simulation study investigating power estimates in phenome-wide association studiesAnurag Verma0Yuki Bradford1Scott Dudek2Anastasia M. Lucas3Shefali S. Verma4Sarah A. Pendergrass5Marylyn D. Ritchie6Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineDepartment of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineDepartment of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineDepartment of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineDepartment of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineBiomedical and Translational InformaticsDepartment of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of MedicineAbstract Background Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance. Results We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants. Conclusions This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.http://link.springer.com/article/10.1186/s12859-018-2135-0PheWASEHRICD-9 codesPower analysisSimulation study |
| spellingShingle | Anurag Verma Yuki Bradford Scott Dudek Anastasia M. Lucas Shefali S. Verma Sarah A. Pendergrass Marylyn D. Ritchie A simulation study investigating power estimates in phenome-wide association studies BMC Bioinformatics PheWAS EHR ICD-9 codes Power analysis Simulation study |
| title | A simulation study investigating power estimates in phenome-wide association studies |
| title_full | A simulation study investigating power estimates in phenome-wide association studies |
| title_fullStr | A simulation study investigating power estimates in phenome-wide association studies |
| title_full_unstemmed | A simulation study investigating power estimates in phenome-wide association studies |
| title_short | A simulation study investigating power estimates in phenome-wide association studies |
| title_sort | simulation study investigating power estimates in phenome wide association studies |
| topic | PheWAS EHR ICD-9 codes Power analysis Simulation study |
| url | http://link.springer.com/article/10.1186/s12859-018-2135-0 |
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