ArticleGenomics & informatics2012
Sample size and statistical power calculation in genetic association studies.
Article in Genomics & informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 277 papers, 11 of them syntheses that pooled it.
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Who cites it
277 citing papers in PubMed, 11 syntheses or guidelines pooled it.
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- A systematic review of GWAS identified SNPs associated with outcomes of medications for opioid use disorder.Addiction science & clinical practice · 2021Pooled it
- A systematic review of gene-by-intervention studies of alcohol and other substance use.Development and psychopathology · 2021Pooled it
- The complex interaction of genetics and delirium: a systematic review and meta-analysis.European archives of psychiatry and clinical neuroscience · 2021Pooled it
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- A k-mer-based genome-wide association study approach empowering gene mining in polyploids.Nature genetics · 2026Article
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- Genome-Wide Association Study of Genetic Variants Associated with Lower Extremity Amputation Risk in Peripheral Artery Disease.International journal of molecular sciences · 2026Article
- Relevance of polymorphisms in TLR2/4 genes and their association with plasma cytokines for schizophrenia.Genes and immunity · 2026Article
- Population-Specific Pharmacogenomic Profiling ofJournal of personalized medicine · 2026Article
217 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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