Meta-GWAS Accuracy and Power (MetaGAP) ( de Vlaming et al., 2017) performs GWAS power calculations and introduces genetic correlation parameters to account for effect size heterogeneity between studies. However, these tools perform power calculation for single SNPs, ignoring the polygenic nature of complex diseases, and the simultaneous testing of millions of SNPs that is now standard in GWAS ( Sham and Purcell, 2014). Genetic Association Study Power Calculator (GAS) ( Johnson and Abecasis, 2017) performs power calculation for genetic association studies under case-control design. For example, Genetic Power Calculator (GPC) ( Purcell et al., 2003) used closed-form analytic results ( Sham and Purcell, 2014) to perform power calculations for linkage and association studies. Several computer programs have been developed to perform power calculation for single SNP association testing. The recent increase in the sample size of GWAS and meta-GWAS has resulted in more of these SNPs to be identified, leading not only to more comprehensive understanding of disease etiology ( Cano-Gamez and Trynka, 2020), but also greater accuracy in the calculation of polygenic scores to predict individual genetic liability to develop disease ( Vilhjalmsson et al., 2015 Mak et al., 2017 Torkamani et al., 2018).Īdequate statistical power is necessary to both detect enough SNPs to inform etiology and to obtain accurate effect size estimate for polygenic score calculations ( Dudbridge, 2013). GWAS on a wide range of phenotypes have confirmed the polygenic nature of most common traits, with thousands of SNPs each making a small contribution to individual differences in the population ( Visscher et al., 2017). Though not necessarily causal, associated SNPs are good starting points for elucidating biological mechanisms of diseases and related phenotypes. Genome-wide association studies (GWAS) aim to systematically identify single-nucleotide polymorphisms (SNPs) associated with complex phenotypes. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. 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. ![]() We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. 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. ![]() Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. 4Fano Labs, Hong Kong, Hong Kong SAR, China. ![]() 3Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.2State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.1Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.Tian Wu 1 Zipeng Liu 1,2,3 Timothy Shin Heng Mak 3,4 Pak Chung Sham 1,2,3*
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