ArticleFrontiers in genetics2022
Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Targeted population genomics uncovers demographic history and genetic divergence in north American wild cranberry.Horticulture research · 2026Article
- The relationship between genotype- and phenotype-based estimates of genetic liability to psychiatric disorders, in practice and in theory.American journal of human genetics · 2026Article
- Binge drinking trajectories across adolescence and early adulthood: Associations with genetic influences for dual-systems impulsive personality traits, alcohol consumption, and alcohol use disorder.Journal of psychopathology and clinical science · 2025Article
- The genetic architecture of cervical length is shared with spontaneous preterm birth risk.Communications medicine · 2025Article
- Polygenic Risk for Substance Use Disorders as Predictors of Substance Use Initiation Among African American Youth.Journal of studies on alcohol and drugs · 2025Article
- Bayesian inference of genetic pleiotropy identifies drug targets and repurposable medicines for human complex diseases.medRxiv : the preprint server for health sciences · 2025Article
- Identification of QTLs associated with grain yield-related traits of spring barley.BMC plant biology · 2025Article
- Combining genome-wide association study and transcriptome analysis to identify molecular markers and genetic basis of population-asynchronous ovarian development inZoological research · 2024Article
- Biobank-scale methods and projections for sparse polygenic prediction from machine learning.Scientific reports · 2023Article
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Authors and funding
4 authors.
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Abstract
Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. While several computer programs have been developed to perform power calculation for single SNP association testing, it might be more appropriate for GWAS power calculation to address the probability of detecting any number of associated SNPs. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. We also provide a fast, flexible and interactive power calculation tool which generates predictions for key GWAS outcomes including the number of independent significant SNPs, the phenotypic variance explained by these SNPs, and the predictive accuracy of resulting polygenic scores. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results.
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