Literature DB >> 18080738

Power calculations using exact data simulation: a useful tool for genetic study designs.

Sophie van der Sluis1, Conor V Dolan, Michael C Neale, Danielle Posthuma.   

Abstract

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes.

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Year:  2007        PMID: 18080738      PMCID: PMC2257998          DOI: 10.1007/s10519-007-9184-x

Source DB:  PubMed          Journal:  Behav Genet        ISSN: 0001-8244            Impact factor:   2.805


  21 in total

1.  Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data.

Authors:  P C Sham; S S Cherny; S Purcell; J K Hewitt
Journal:  Am J Hum Genet       Date:  2000-04-12       Impact factor: 11.025

2.  A general test of association for quantitative traits in nuclear families.

Authors:  G R Abecasis; L R Cardon; W O Cookson
Journal:  Am J Hum Genet       Date:  2000-01       Impact factor: 11.025

3.  A note on the power provided by sibships of sizes 2, 3, and 4 in genetic covariance modeling of a codominant QTL.

Authors:  C V Dolan; D I Boomsma; M C Neale
Journal:  Behav Genet       Date:  1999-05       Impact factor: 2.805

4.  A note on the statistical power in extended twin designs.

Authors:  D Posthuma; D I Boomsma
Journal:  Behav Genet       Date:  2000-03       Impact factor: 2.805

5.  Powerful regression-based quantitative-trait linkage analysis of general pedigrees.

Authors:  Pak C Sham; Shaun Purcell; Stacey S Cherny; Gonçalo R Abecasis
Journal:  Am J Hum Genet       Date:  2002-07-05       Impact factor: 11.025

6.  Missing data: our view of the state of the art.

Authors:  Joseph L Schafer; John W Graham
Journal:  Psychol Methods       Date:  2002-06

7.  Variance components models for gene-environment interaction in quantitative trait locus linkage analysis.

Authors:  Shaun Purcell; Pak Sham
Journal:  Twin Res       Date:  2002-12

8.  Variance components models for gene-environment interaction in twin analysis.

Authors:  Shaun Purcell
Journal:  Twin Res       Date:  2002-12

9.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits.

Authors:  S Purcell; S S Cherny; P C Sham
Journal:  Bioinformatics       Date:  2003-01       Impact factor: 6.937

10.  Pedigree tests of transmission disequilibrium.

Authors:  G R Abecasis; W O Cookson; L R Cardon
Journal:  Eur J Hum Genet       Date:  2000-07       Impact factor: 4.246

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  8 in total

1.  Extending Causality Tests with Genetic Instruments: An Integration of Mendelian Randomization with the Classical Twin Design.

Authors:  Camelia C Minică; Conor V Dolan; Dorret I Boomsma; Eco de Geus; Michael C Neale
Journal:  Behav Genet       Date:  2018-06-07       Impact factor: 2.805

2.  Notes on Three Decades of Methodology Workshops.

Authors:  Hermine H Maes
Journal:  Behav Genet       Date:  2021-02-14       Impact factor: 2.805

3.  Comparison of Twin and Extended Pedigree Designs for Obtaining Heritability Estimates.

Authors:  Anna R Docherty; William S Kremen; Matthew S Panizzon; Elizabeth C Prom-Wormley; Carol E Franz; Michael J Lyons; Lindon J Eaves; Michael C Neale
Journal:  Behav Genet       Date:  2015-04-18       Impact factor: 2.805

4.  A general test for gene-environment interaction in sib pair-based association analysis of quantitative traits.

Authors:  Sophie van der Sluis; Conor V Dolan; Michael C Neale; Danielle Posthuma
Journal:  Behav Genet       Date:  2008-04-04       Impact factor: 2.805

5.  Prediction of a time-to-event trait using genome wide SNP data.

Authors:  Jinseog Kim; Insuk Sohn; Dae-Soon Son; Dong Hwan Kim; Taejin Ahn; Sin-Ho Jung
Journal:  BMC Bioinformatics       Date:  2013-02-19       Impact factor: 3.169

6.  Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies.

Authors:  Sophie van der Sluis; Matthijs Verhage; Danielle Posthuma; Conor V Dolan
Journal:  PLoS One       Date:  2010-11-10       Impact factor: 3.240

7.  The Analytic Identification of Variance Component Models Common to Behavior Genetics.

Authors:  Michael D Hunter; S Mason Garrison; S Alexandra Burt; Joseph L Rodgers
Journal:  Behav Genet       Date:  2021-06-04       Impact factor: 2.965

8.  GE covariance through phenotype to environment transmission: an assessment in longitudinal twin data and application to childhood anxiety.

Authors:  Conor V Dolan; Johanna M de Kort; Toos C E M van Beijsterveldt; Meike Bartels; Dorret I Boomsma
Journal:  Behav Genet       Date:  2014-05-01       Impact factor: 2.805

  8 in total

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