Package: paweh 0.99.0

Akil Anthony

paweh: Power and Sample Size Analysis for Genetic Studies with Heterogeneity and Misclassification

Provides prospective power and minimum sample size necessary (MSSN) calculations for genetic and genomic association study design. Supported designs include case-control studies, transmission disequilibrium tests with affected-child trios, and single- and multivariate quantitative-trait studies. The package emphasizes selected departures from idealized assumptions, including locus heterogeneity and supported forms of genotype and phenotype misclassification. These factors can materially affect power and sample-size requirements but can be difficult to incorporate analytically. Published statistical-genetics methods are implemented in a reproducible, primarily parameter- and model-based framework with static visualizations and an optional Shiny dashboard for interactive exploration. The software supports prospective study planning; it does not perform downstream association testing.

Authors:Akil Anthony [aut, cre], Derek Gordon [aut]

paweh_0.99.0.tar.gz
paweh_0.99.0.zip(r-4.7-any)paweh_0.99.0.zip(r-4.6-any)paweh_0.99.0.zip(r-4.5-any)
paweh_0.99.0.tgz(r-4.6-any)paweh_0.99.0.tgz(r-4.5-any)
paweh_0.99.0.tar.gz(r-4.7-any)paweh_0.99.0.tar.gz(r-4.6-any)
paweh_0.99.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
paweh/json (API)

# Install 'paweh' in R:
install.packages('paweh', repos = c('https://biocstaging.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/akilanthony/paweh/issues

Pkgdown/docs site:https://akilanthony.github.io

On CRAN:

Conda:

experimentaldesigngeneticsgenomewideassociationshinyapps

4.69 score 58 exports 42 dependencies

Last updated from:7a5ed7bdfc. Checks:1 WARNING, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING209
linux-develOK251
source / vignettesOK237
linux-releaseOK249
macos-releaseOK192
macos-oldrelOK179
windows-develOK228
windows-releaseOK228
windows-oldrelOK228
wasm-releaseOK124

Exports:cc_chisq_mssn_genotype_misclassification_1pcc_chisq_mssn_genotype_misclassification_2pcc_chisq_mssn_genotype_misclassification_3pcc_chisq_mssn_genotype_misclassification_differential_3pcc_chisq_mssn_locus_heterogeneitycc_chisq_mssn_phenotype_misclassificationcc_chisq_power_genotype_misclassification_1pcc_chisq_power_genotype_misclassification_2pcc_chisq_power_genotype_misclassification_3pcc_chisq_power_genotype_misclassification_differential_3pcc_chisq_power_locus_heterogeneitycc_chisq_power_phenotype_misclassificationcc_mssncc_ngs_mssncc_ngs_powercc_powercc_trend_mssn_locus_heterogeneitycc_trend_power_locus_heterogeneitylrtae_mssnlrtae_ncplrtae_powerngs_genotype_error_matrixpaweh_appplot_cc_mssnplot_cc_powerplot_ngs_mssnplot_ngs_powerplot_qtl_anova_mssnplot_qtl_anova_powerplot_qtl_genotype_distributionplot_qtl_threshold_chisq_mssnplot_qtl_threshold_chisq_powerplot_tdt_mssnplot_tdt_mssn_locus_heterogeneityplot_tdt_mssn_phenotype_misclassificationplot_tdt_powerplot_tdt_power_locus_heterogeneityplot_tdt_power_phenotype_misclassificationplot_tdt_surface3dqtl_anova_mssnqtl_anova_powerqtl_falconer_parametersqtl_falconer_threshold_parametersqtl_multivariate_mssn_fullqtl_multivariate_power_fullqtl_threshold_chisq_mssnqtl_threshold_chisq_powertdt_expected_nontransmission_probabilitytdt_expected_transmission_countstdt_expected_transmission_probabilitytdt_mssntdt_mssn_from_modeltdt_mssn_phenotype_misclassification_from_ncptdt_ngs_mssntdt_ngs_powertdt_powertdt_power_from_expected_countstdt_power_from_model

Dependencies:base64encbslibcachemclicommonmarkcpp11digestfarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlifecyclemagrittrmemoisemimemvtnormotelpromisesR6rappdirsRColorBrewerRcpprlangS7sassscalesshinysourcetoolsvctrsviridisLitewithrxtable

Getting started with paweh
What problem does paweh solve? | Power and MSSN answer inverse questions | Five study-design families | Case-control association | Affected-child trios | Quantitative traits | Sequencing-based designs | LRTae validation/error-aware designs | Model-based and model-free inputs | Where should I start? | Why Bioconductor? | Published validation philosophy | Installation | Session information
Rendered frompaweh-01-getting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-16
Started: 2026-08-24

Quantitative-trait genetic association study design
Quantitative-trait study design in PAWEH | Genotype-specific trait distributions | Continuous-trait ANOVA design | Extreme-phenotype sampling | Multiple quantitative traits | Published-example reproduction: Gordon et al. (2017) | Joint multivariate threshold selection | Scope and limitations | References | Session information
Rendered frompaweh-04-quantitative-trait-study-design.Rmdusingknitr::rmarkdown

Last update: 2026-09-16
Started: 2026-08-24

LRTae power and sample-size design
What is LRTae? | Four validation tiers | A reproducible prospective design | Computing the NCP | Computing power | Computing minimum sample size | Why validation allocation matters | Reading the misclassification matrices | When to use these functions | References | Session information
Rendered frompaweh-07-lrtae-study-design.Rmdusingknitr::rmarkdown

Last update: 2026-09-15
Started: 2026-09-14

Case-control genetic association study design
From a scientific contrast to a design | Basic power and inverse design | Published-example reproduction: Gordon et al. (2002) | Extending the same design: how MSSN grows with DSB error | Published-example reproduction: Edwards et al. (2005) | Extending Edwards: prevalence and false-positive classification | Locus heterogeneity and the trend test | Differential genotype error: a Type I error warning | Independent sensitivity scenarios | Choosing an interface and recognizing limits | References | Session information
Rendered frompaweh-02-case-control-study-design.Rmdusingknitr::rmarkdown

Last update: 2026-09-15
Started: 2026-08-24

Transmission disequilibrium test study design
Why use an affected-child trio design? | Power and MSSN for trios | Model-based and model-free TDT inputs | Chapter 5 genotype misclassification | Published-example reproduction: Buyske et al. (2009) | Extending Buyske: a misclassification curve | Extending Buyske: prevalence by misclassification | Published-example reproduction: Chen et al. (2009) | Extending Chen: dilution by heterogeneous trios | Assumptions and limitations | References | Session information
Rendered frompaweh-03-tdt-study-design.Rmdusingknitr::rmarkdown

Last update: 2026-09-15
Started: 2026-08-24

Interactive Study Design with the paweh Dashboard
Introduction | Launching the dashboard | Case-Control workflow | TDT / Family workflow | Quantitative Trait workflow | Full continuous trait | Extreme phenotype sampling | Multiple quantitative traits | Detailed panels in Case-Control and TDT | Reproduce calculations in R | Frequently asked questions | Dashboard and programmatic workflows | Further reading | Session information
Rendered frompaweh-05-interactive-dashboard.Rmdusingknitr::rmarkdown

Last update: 2026-09-15
Started: 2026-08-24

Sequencing-Based Genetic Study Design with PAWEH
Overview | Two sequencing workflows in paweh | Exact published TDT1-NGS replication: the Kim AIS example | TDT1-NGS power and MSSN | TDT sequencing sensitivity | Published case-control framework: the Ahn example | The PAWEH CC-NGS sequencing bridge | CC-NGS power and MSSN | Separate case/control sequencing | Why zero per-read error is not perfect at finite depth | CC sequencing sensitivity | CC locus heterogeneity | The exact pi = 0 boundary | Fixed-depth assumptions and limitations | A practical design workflow | References | Session information
Rendered frompaweh-06-sequencing-study-design.Rmdusingknitr::rmarkdown

Last update: 2026-09-07
Started: 2026-09-01

Readme and manuals

Help Manual

Help pageTopics
Specialized Case-Control Genotype Misclassification Functionscase_control_genotype_misclassification cc_chisq_mssn_genotype_misclassification_1p cc_chisq_mssn_genotype_misclassification_2p cc_chisq_mssn_genotype_misclassification_3p cc_chisq_mssn_genotype_misclassification_differential_3p cc_chisq_power_genotype_misclassification_1p cc_chisq_power_genotype_misclassification_2p cc_chisq_power_genotype_misclassification_3p cc_chisq_power_genotype_misclassification_differential_3p
Specialized Case-Control Locus-Heterogeneity Functionscase_control_locus_heterogeneity cc_chisq_mssn_locus_heterogeneity cc_chisq_power_locus_heterogeneity cc_trend_mssn_locus_heterogeneity cc_trend_power_locus_heterogeneity
Specialized Case-Control Phenotype Misclassification Functionscase_control_phenotype_misclassification cc_chisq_mssn_phenotype_misclassification cc_chisq_power_phenotype_misclassification
Case-Control Minimum Sample Size for Conditional Genotype Frequenciescc_mssn
Analytic MSSN for a Case-Control Sequencing Studycc_ngs_mssn
Analytic Power for a Case-Control Sequencing Studycc_ngs_power
Case-Control Power for Conditional Genotype Frequenciescc_power
Minimum Total Sample Size for an LRTae Designlrtae_mssn
Noncentrality Parameter for Prospective LRTae Designslrtae_ncp
Prospective Power for an LRTae Designlrtae_power
Sequencing-Derived Genotype Error Matrixngs_genotype_error_matrix
Construct the paweh Shiny Dashboardpaweh_app
Plot Case-Control Minimum Sample Size Necessaryplot_cc_mssn
Plot Case-Control Powerplot_cc_power
Plot MSSN Sensitivity for Sequencing Designsplot_ngs_mssn
Plot Power Sensitivity for Sequencing Designsplot_ngs_power
Plot One-Way ANOVA Minimum Sample Size Under the Falconer Modelplot_qtl_anova_mssn
Plot One-Way ANOVA Power Under the Falconer Modelplot_qtl_anova_power
Plot Falconer Genotype-Specific Quantitative-Trait Distributionsplot_qtl_genotype_distribution
Plot Threshold-Selected Genotype Chi-Square Minimum Sample Sizeplot_qtl_threshold_chisq_mssn
Plot Threshold-Selected Genotype Chi-Square Powerplot_qtl_threshold_chisq_power
Plot TDT Minimum Sample Size Necessaryplot_tdt_mssn
Plot TDT MSSN Sensitivity to Locus Heterogeneityplot_tdt_mssn_locus_heterogeneity
Plot TDT MSSN Sensitivity to Phenotype Misclassificationplot_tdt_mssn_phenotype_misclassification
Plot TDT Powerplot_tdt_power
Plot TDT Power Sensitivity to Locus Heterogeneityplot_tdt_power_locus_heterogeneity
Plot TDT Power Sensitivity to Phenotype Misclassificationplot_tdt_power_phenotype_misclassification
Plot a Generalized 3D TDT Sensitivity Surfaceplot_tdt_surface3d
Minimum Sample Size for One-Way ANOVA Under the Falconer Modelqtl_anova_mssn
Power for One-Way ANOVA Under the Falconer Modelqtl_anova_power
Falconer Parameters for a Single Quantitative Traitqtl_falconer_parameters
Threshold-Selected Parameters Under the Falconer Modelqtl_falconer_threshold_parameters
Multivariate Falconer Minimum Sample Sizeqtl_multivariate_mssn_full
Multivariate Falconer Powerqtl_multivariate_power_full
Minimum Sample Size for a Threshold-Selected Genotype Chi-Square Testqtl_threshold_chisq_mssn
Power for a Threshold-Selected Genotype Chi-Square Testqtl_threshold_chisq_power
Expected Non-Transmission Probability (gNT*) in the Transmission Disequilibrium Test (TDT)tdt_expected_nontransmission_probability
Expected Transmissions (ET*) and Non-Transmissions (ENT*) in the Transmission Disequilibrium Test (TDT)tdt_expected_transmission_counts
Expected Transmission Probability (gT*) in the Transmission Disequilibrium Test (TDT)tdt_expected_transmission_probability
Family-Based (TDT) Minimum Sample Size Necessarytdt_mssn
TDT Minimum Sample Size Necessary from Genetic Model Parameterstdt_mssn_from_model
TDT Minimum Sample Size Necessary from an NCP under Phenotype Misclassificationtdt_mssn_phenotype_misclassification_from_ncp
Analytic MSSN for a TDT1-NGS Sequencing Studytdt_ngs_mssn
Analytic Power for a TDT1-NGS Sequencing Studytdt_ngs_power
Family-Based (TDT) Powertdt_power
Transmission Disequilibrium Test (TDT) Power from Expected Transmissions and Non-Transmissionstdt_power_from_expected_counts
Transmission Disequilibrium Test (TDT) Power from Genetic Model Parameterstdt_power_from_model