Sample Size and Statistical Power Calculation in Genetic Association Studies
A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power wi...
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BioMed Central
2012-06-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-10-117.pdf |
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| author | Eun Pyo Hong Ji Wan Park |
| author_facet | Eun Pyo Hong Ji Wan Park |
| author_sort | Eun Pyo Hong |
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| description | A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study. |
| format | Article |
| id | doaj-art-efd9c5ef2eba476d878e48e191d75b09 |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2012-06-01 |
| publisher | BioMed Central |
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| series | Genomics & Informatics |
| spelling | doaj-art-efd9c5ef2eba476d878e48e191d75b092025-02-02T07:36:56ZengBioMed CentralGenomics & Informatics1598-866X2234-07422012-06-0110211712210.5808/GI.2012.10.2.1175Sample Size and Statistical Power Calculation in Genetic Association StudiesEun Pyo Hong0Ji Wan Park1Department of Medical Genetics, Hallym University College of Medicine, Chuncheon 200-702, Korea.Department of Medical Genetics, Hallym University College of Medicine, Chuncheon 200-702, Korea.A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.http://genominfo.org/upload/pdf/gni-10-117.pdfcase-control studiescase-parent studygenetic association studiessample sizestatistical power |
| spellingShingle | Eun Pyo Hong Ji Wan Park Sample Size and Statistical Power Calculation in Genetic Association Studies Genomics & Informatics case-control studies case-parent study genetic association studies sample size statistical power |
| title | Sample Size and Statistical Power Calculation in Genetic Association Studies |
| title_full | Sample Size and Statistical Power Calculation in Genetic Association Studies |
| title_fullStr | Sample Size and Statistical Power Calculation in Genetic Association Studies |
| title_full_unstemmed | Sample Size and Statistical Power Calculation in Genetic Association Studies |
| title_short | Sample Size and Statistical Power Calculation in Genetic Association Studies |
| title_sort | sample size and statistical power calculation in genetic association studies |
| topic | case-control studies case-parent study genetic association studies sample size statistical power |
| url | http://genominfo.org/upload/pdf/gni-10-117.pdf |
| work_keys_str_mv | AT eunpyohong samplesizeandstatisticalpowercalculationingeneticassociationstudies AT jiwanpark samplesizeandstatisticalpowercalculationingeneticassociationstudies |