https://bioinformatics.org/spower/ 2026-04-17T00:58:55-04:00 https://bioinformatics.org/spower/ https://bioinformatics.org/spower/lib/tpl/codowik/images/favicon.ico text/html 2016-04-09T20:51:52-04:00 https://bioinformatics.org/spower/404?rev=1460249512&do=diff Cool stuff coming soon ... text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/allargs?rev=1460584978&do=diff Table of Command Options Common options Option Default Description --moi A mode of inheritance: 'A', additive --resampling False directly draw sample genotypes from given haplotype pools (sample genotypes will be simulated on the fly if haplotype pools are not avaliable) text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/analytic-tutorial?rev=1460584979&do=diff Analytic Power Analysis Analytic Power and Sample Size Calculation for Case Control Studies LOGIT model Basic example To calculate power at given sample size assuming equal case control samples (1000 cases, 1000 controls), at an effect size of odds ratio equals 2 for rare variants, 1 for common variants, evaluated at text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/artifact?rev=1460584979&do=diff Emulating Genotyping Artifact For empirical power calculations it is possible to model sequencing / genotyping artifact such as missing variant sites, missing genotypes and genotype call errors. We apply a simple probabilistic model to each type of artifact by specifying a probability it occurs, at variant or genotype call levels. text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/benchmark?rev=1460584979&do=diff SEQPower Benchmark spower execute command executes a given configuration file with parameters set to allow for power analysis under multiple scenarios within a single command. The feature also provides us a shortcut to benchmark performance of different rare variant association methods under various parameter settings. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/cc?rev=1460584978&do=diff Power for Case Control Studies Cumulative Minor Allele Frequency in Cases and Controls To compare the difference between cases/ctrls for cumulative MAF of all variants across a gene analytically, the case/ctrl group specific MAF have to be calculated given the MAF in population and effect size of variant sites. Consider a multi-site genotype having text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/changelog?rev=1460584978&do=diff Version History 1.1.0 Major new features * Reimplemented gdata infrastructure. All users should update to this version because it is no longer compatible with previous gdata format. The data release have also been updated. * More association tests included: a partial list of text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/dataset?rev=1460584979&do=diff EVS Data Derived Haplotype Pool This tutorial explains how the EVS dataset for SEQPower was generated from resource on the Exome Variant Server. wget http://evs.gs.washington.edu/evs_bulk_data/ESP6500SI-V2.snps_indels.vcf.tar.gz # Copyright (C) 2011 Bo Peng (bpeng@mdanderson.org) # Distributed under GPL. see <http://www.gnu.org/licenses/> # # Please refer to http://varianttools.sourceforge.net/Format/New for # a description of the format of this file. [format description] description=I… text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/docker-guide?rev=1460584978&do=diff A Jump-start Guide to Docker Setup Docker setup for Mac users <https://docs.docker.com/mac/> Docker setup for Linux users Debian Jessie sudo apt-key adv --keyserver hkp://pgp.mit.edu:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D echo "deb [arch=amd64] https://apt.dockerproject.org/repo debian-jessie main" | sudo tee /etc/apt/sources.list.d/docker.list sudo apt-get update sudo apt-get install docker-engine text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/docs?rev=1460584978&do=diff Documentation Data Input and Output * SEQPower input data * SEQPower simulation of association data * SEQPower power analysis results * Graphic representation of power calculation results Complete List of Command Options Argument list Guide to Command Usage Common options * Genotype simulation options text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/empirical-tutorial?rev=1460584979&do=diff Empirical Power Analysis Logit Model Power calculation Option --method specifies statistical tests to be used for empirical power calculation. To read the complete list of available statistical tests, spower show tests and options under specific test text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/empirical?rev=1460584978&do=diff Empirical Power Analysis Power Calculations For many association methods for rare variants, it is not possible to perform theoretical power analysis, since the statistical properties for these methods are mathematically intractable. A variety of such tests have been developed over the past few years. Compare with simple tests with close form solution for power and sample size, these methods are usually more powerful. Performance of these tests, particularly statistical power, have to be assess… text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/input?rev=1460584978&do=diff Adam R. Boyko, Scott H. Williamson, Amit R. Indap, Jeremiah D. Degenhardt, Ryan D. Hernandez, Kirk E. Lohmueller, Mark D. Adams, Steffen Schmidt, John J. Sninsky, Shamil R. Sunyaev, Thomas J. White, Rasmus Nielsen, Andrew G. Clark and Carlos D. Bustamante (2008). text/html 2020-12-02T18:50:42-04:00 https://bioinformatics.org/spower/installation?rev=1606953042&do=diff Installation SEQPower Docker Image Starting from version 1.1.0, binary release for Linux and MacOS are no longer supported. Instead we release Docker image for SEQPower which can be executed on both Linux and MacOS. Although overall execution time of a command text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/mblnr?rev=1460584978&do=diff Linear Model Binary Outcome Under this model, case control status are simulated based on QT values. Quantitative traits are generated; Cases are defined as samples having QT values larger than upper percentile, and controls are samples having QT values smaller than text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/melnr?rev=1460584978&do=diff Extreme Quantitative Traits Model Under this model, extreme quantitative traits are simulated based on given percentile upper () and lower () bounds. There are two approaches to generate extreme QT values: * Finite sample pool method: QT values are generated from simulation in text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/mlnr?rev=1460584978&do=diff Linear Model Quantitative Traits Simulation of quantitative trait (QT) values follows from a simple linear model of the additive effect of mutations. A mutation contributes to the change in mean of QT value by a specific amount. For protective variants, mutations decrease QT; for detrimental variants, mutations increase QT. Under this model both analytic and empirical power and sample size calculations are available for quantitative traits analysis methods. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/mlogit?rev=1460584978&do=diff Logit Model Binary Outcome Under this model, case control status are simulated based on logit model of odds ratio and disease prevalence. Power and sample size calculations can be performed using a number of theoretical and empirical methods. Command Interface text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/mpar?rev=1460584978&do=diff Population Attributable Risk Model Under this model, case control status are simulated based on difference in population attributable risk between case control groups. Power and sample size calculations can be performed using a number of theoretical and empirical methods. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/options?rev=1460584978&do=diff Command Interface This page documents command options shared by all simulation settings and association methods with explanations. For a complete table of program options please check here. For model or association test specific option please refer to their respective documentation pages. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/output?rev=1460584978&do=diff SEQPower Output Output Contents Two types of output are generated in power calculation process: association group statistics as well as summary site specific attributes. * Association group statistics: summary statistic for the group of variants on which power analysis was carried out, including power or required sample size, cumulative MAF (by case control group, by functionality categories), data properties (missing / error genotype call rates), etc. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/qt?rev=1460584978&do=diff Power for Quantitative Traits Model The key to power and sample size calculation for quantitative traits is on the comparison of quantitative trait values between groups. Consider a two-sample test framework with unequal sample size, where one group of sample consists of text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/quickstart?rev=1460584979&do=diff A Quick Start Tutorial Purpose Evidences suggest that rare variants in the human genome might have strong impact on the risk of complex diseases. An essential first step in designing genetic association mapping studies is to assess the sample size needed to achieve sufficient statistical power and to choose appropriate statistical methods for association testing. SEQPower employs sophisticated modeling of human genome sequences and complex diseases, and rapidly conducts customized power analys… text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/sidebar?rev=1460584979&do=diff * About * Installation * Support * Version History * Documentation * Command Interface * Empirical Power Analysis * Extreme Quantitative Traits Model * Forward-time Simulation with SRV * Linear Model Binary Outcome * Linear Model Quantitative Traits * Logit Model Binary Outcome * Population Attributable Risk Model * Power for Case Control Studies * Power for Quantitative Traits * Rare Variant Association Methods * SEQPower Input … text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/simseq?rev=1460584978&do=diff Simulation of DNA Sequence Data Model Based Simulation Due to the cost and availability of ample real world genetic sequence data, there is currently a lack of knowledge of the number of mutation sites and distribution of minor allele frequency. To evaluate the contribution of very rare variants to observed phenotypes, simulated DNA sequence data are often generated, under various genetic model assumptions. Below is a summary of most commonly used simulation models in literature: text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/simtraits?rev=1460584978&do=diff Simulating Genetic Associations For type I evaluations simulation of genotypes is independent to phenotypes. Phenotype values or disease status can be randomly assigned to sample genotypes. To evaluate power, genotype-phenotype associations have to be simulated, by either generating genotypes conditional on phenotypes, or assign phenotypes based on given genotype sequence. This document briefly discusses general considerations for simulations of genetic associations as well as models for quanti… text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/srvbatch-tutorial?rev=1460584979&do=diff Simulate Rare Variants Data This tutorial covers simulation for DNA sequence data under * Demographic models based on Scott H. Williamson 2005 NIEHS SNPs data and Adam Eyre-Walker 2006 Environmental Genome Project (EGP) data * num. haplotypes = 51,340 x 2 = 102,680 text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/srvbatch?rev=1460584978&do=diff Forward-time Simulation with SRV The original srv simulator was authored by Bo Peng, 2011 (download). SEQPower expands the srv simulator to allow for additional demographic models and specification of selection coefficients. It also saves generated data in a compressed format compatible with SEQPower's power calculation input. text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/start?rev=1460584978&do=diff About SEQPower provides statistical power analysis and sample size estimation for sequence-based association studies Download <http://www.bioinformatics.org/spower/ccount/click.php?id=3> Developers * Gao Wang, Baylor College of Medicine * Biao Li, Baylor College of Medicine * Regie Lyn Santos-Cortez, Baylor College of Medicine text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/support?rev=1460584978&do=diff Support SEQPower was written by Gao Wang when he was graduate student at Baylor College of Medicine. Though no longer actively developed, the package is still under maintenance and the author is open to feature requests and collaborations. Please file your bug reports, questions and comments on text/html 2016-04-13T18:02:59-04:00 https://bioinformatics.org/spower/tutorial?rev=1460584979&do=diff Tutorial Quick Start * A short tutorial demonstrating major features of SEQPower Data Simulation * Forward time simulation of DNA sequences with rare variants * Sequencing / genotyping artifact Power and Sample Size Calculation for Population Based Study Designs * Analytic power analysis for case control and quantitative phenotype rare variant burden test text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/vat-methods?rev=1460584978&do=diff Rare Variant Association Methods Implementation of association analysis methods in SEQPower is taken from the VAT ensemble module, which is part of ''variant tools / VAT''. A number of published methods and their variations are available in VAT for rare variants association analysis of quantitative and qualitative traits. SEQPower incorporates these methods for power and sample size analysis purposes. The documentation below is also borrowed from VAT, which briefly describes the implementation … text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/vat-r?rev=1460584978&do=diff SEQPower R Interface In addition to making sample size and power calculations, SEQPower can be used to validate and evaluate novel association methods proposed and implemented by researchers. We incorporate the mechanism to load R script from variant association tools (VAT) text/html 2016-04-13T18:02:58-04:00 https://bioinformatics.org/spower/write?rev=1460584978&do=diff Write Simulation Data Set to Files The GroupWrite method can be used to save simulation data sets to separate files for purposes beyond the scope of SEQPower. See below for more details: spower show test GroupWrite It will create 3 files for each group: