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Power_analysis

Summary Table

NAME CITATION YEAR
CaTS power calculator Skol AD, Scott LJ, Abecasis GR, Boehnke M. (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies Nat. Genet., 38 (2) 209-213. doi:10.1038/ng1706. PMID 16415888 2006
GAS Power Calculator Johnson, J. L., & Abecasis, G. R. (2017). GAS Power Calculator: web-based power calculator for genetic association studies. BioRxiv, 164343. NA
mRnd Brion MJ, Shakhbazov K, Visscher PM. (2013) Calculating statistical power in Mendelian randomization studies Int. J. Epidemiol., 42 (5) 1497-1501. doi:10.1093/ije/dyt179. PMID 24159078 2013

CaTS power calculator

  • NAME : CaTS power calculator
  • SHORT NAME : CaTS power calculator
  • FULL NAME : CaTS power calculator
  • DESCRIPTION : CaTS is a simple, multi-platform interface for carrying out power calculations for large genetic association studies, including two stage genome wide association studies.
  • URL : https://csg.sph.umich.edu/abecasis/cats/
  • TITLE : Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
  • DOI : 10.1038/ng1706
  • ABSTRACT : Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).
  • CITATION : Skol AD, Scott LJ, Abecasis GR, Boehnke M. (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies Nat. Genet., 38 (2) 209-213. doi:10.1038/ng1706. PMID 16415888
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2006 ; 38 ; 2 ; 209-213
  • PUBMED_LINK : 16415888

GAS Power Calculator

  • NAME : GAS Power Calculator
  • SHORT NAME : GAS Power Calculator
  • FULL NAME : Genetic Association Study Power Calculator
  • DESCRIPTION : This Genetic Association Study (GAS) Power Calculator is a simple interface that can be used to compute statistical power for large one-stage genetic association studies. The underlying method is derived from the CaTS power calculator for two-stage association studies (2006).
  • URL : https://csg.sph.umich.edu/abecasis/gas_power_calculator/
  • PREPRINT_DOI : 10.1101/164343
  • SERVER : biorxiv
  • CITATION : Johnson, J. L., & Abecasis, G. R. (2017). GAS Power Calculator: web-based power calculator for genetic association studies. BioRxiv, 164343.

mRnd

  • NAME : mRnd
  • SHORT NAME : mRnd
  • FULL NAME : Power calculations for Mendelian Randomization
  • URL : https://shiny.cnsgenomics.com/mRnd/
  • TITLE : Calculating statistical power in Mendelian randomization studies
  • DOI : 10.1093/ije/dyt179
  • ABSTRACT : In Mendelian randomization (MR) studies, where genetic variants are used as proxy measures for an exposure trait of interest, obtaining adequate statistical power is frequently a concern due to the small amount of variation in a phenotypic trait that is typically explained by genetic variants. A range of power estimates based on simulations and specific parameters for two-stage least squares (2SLS) MR analyses based on continuous variables has previously been published. However there are presently no specific equations or software tools one can implement for calculating power of a given MR study. Using asymptotic theory, we show that in the case of continuous variables and a single instrument, for example a single-nucleotide polymorphism (SNP) or multiple SNP predictor, statistical power for a fixed sample size is a function of two parameters: the proportion of variation in the exposure variable explained by the genetic predictor and the true causal association between the exposure and outcome variable. We demonstrate that power for 2SLS MR can be derived using the non-centrality parameter (NCP) of the statistical test that is employed to test whether the 2SLS regression coefficient is zero. We show that the previously published power estimates from simulations can be represented theoretically using this NCP-based approach, with similar estimates observed when the simulation-based estimates are compared with our NCP-based approach. General equations for calculating statistical power for 2SLS MR using the NCP are provided in this note, and we implement the calculations in a web-based application.
  • CITATION : Brion MJ, Shakhbazov K, Visscher PM. (2013) Calculating statistical power in Mendelian randomization studies Int. J. Epidemiol., 42 (5) 1497-1501. doi:10.1093/ije/dyt179. PMID 24159078
  • JOURNAL_INFO : International journal of epidemiology ; Int. J. Epidemiol. ; 2013 ; 42 ; 5 ; 1497-1501
  • PUBMED_LINK : 24159078