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samuelbfernandes

simplePHENOTYPES:Simulation of Pleiotropic, Linked and Epistatic Phenotypes

The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1186/s12859-020-03804-y>.

Maintained by Samuel Fernandes. Last updated 3 years ago.

pleiotropysimulation

15.3 match 12 stars 4.95 score 15 scripts

sinnweja

pleio:Pleiotropy Test for Multiple Traits on a Genetic Marker

Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.

Maintained by Jason Sinnwell. Last updated 1 years ago.

22.7 match 3.00 score 7 scripts

bioc

GPA:GPA (Genetic analysis incorporating Pleiotropy and Annotation)

This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.

Maintained by Dongjun Chung. Last updated 5 months ago.

softwarestatisticalmethodclassificationgenomewideassociationsnpgeneticsclusteringmultiplecomparisonpreprocessinggeneexpressiondifferentialexpressioncpp

7.2 match 14 stars 6.15 score 7 scripts

mrcieu

TwoSampleMR:Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database

A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis.

Maintained by Gibran Hemani. Last updated 11 days ago.

3.3 match 467 stars 11.23 score 1.7k scripts 1 dependents

fboehm

qtl2pleio:Testing Pleiotropy in Multiparental Populations

We implement an adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing the null hypothesis of pleiotropy against the alternative hypothesis, two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in that our test accommodates multiparental populations.

Maintained by Frederick J Boehm. Last updated 4 years ago.

multiparental-populationsquantitative-geneticsquantitative-traitcpp

5.2 match 5 stars 4.41 score 26 scripts

annaltyler

cape:Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice

Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>. Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Maintained by Anna Tyler. Last updated 1 years ago.

6.3 match 3.27 score 37 scripts

feraguate

pleiotest:Fast Sequential Pleiotropy Test

It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.

Maintained by Fernando Aguate. Last updated 3 years ago.

openblascpp

6.7 match 1 stars 2.70 score 2 scripts

bioc

viper:Virtual Inference of Protein-activity by Enriched Regulon analysis

Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms

Maintained by Mariano J Alvarez. Last updated 5 months ago.

systemsbiologynetworkenrichmentgeneexpressionfunctionalpredictiongeneregulation

2.3 match 7.00 score 342 scripts 5 dependents

kbroman

qtlcharts:Interactive Graphics for QTL Experiments

Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) <doi:10.1534/genetics.114.172742>.

Maintained by Karl W Broman. Last updated 2 years ago.

2.0 match 84 stars 6.61 score 322 scripts

mrcieu

epigraphdb:Interface Package for the 'EpiGraphDB' Platform

The interface package to access data from the 'EpiGraphDB' <https://epigraphdb.org> platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API <https://api.epigraphdb.org> and return the response data in the 'tibble' data frame format.

Maintained by Yi Liu. Last updated 3 years ago.

api-clientbioinformaticsepidemiologygraph-databasemendelian-randomizationphenotypes

1.8 match 27 stars 6.02 score 13 scripts

mrcieu

CAMeRa:CAMeRa (Cross Ancestral Mendelian Randomisation)

CAMERA estimates joint causal effect in multiple ancestries and detects pleiotropy via the zero relevance model.

Maintained by Gibran Hemani. Last updated 12 months ago.

causal-inferencegwas-summary-statisticsmendelian-randomisationmulti-ancestry

1.7 match 2 stars 5.32 score 175 scripts

mrcieu

MVMR:MVMR

An R package for performing multivariable Mendelian randomization analyses.

Maintained by Wes Spiller. Last updated 2 months ago.

1.1 match 45 stars 6.65 score 166 scripts 1 dependents

arunabhacodes

CPBayes:Bayesian Meta Analysis for Studying Cross-Phenotype Genetic Associations

A Bayesian meta-analysis method for studying cross-phenotype genetic associations. It uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. CPBayes is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) <doi:10.1371/journal.pgen.1007139>.

Maintained by Arunabha Majumdar. Last updated 4 years ago.

1.7 match 3 stars 4.26 score 12 scripts

wlu04

ALLSPICER:ALLelic Spectrum of Pleiotropy Informed Correlated Effects

Provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) <doi:10.1101/2024.10.01.614806>.

Maintained by Wenhan Lu. Last updated 5 months ago.

2.9 match 1.70 score 1 scripts

cran

CUMP:Analyze Multivariate Phenotypes by Combining Univariate Results

Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.

Maintained by Shuo Li. Last updated 9 years ago.

2.2 match 1.70 score

bioc

GGPA:graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

Maintained by Dongjun Chung. Last updated 5 months ago.

softwarestatisticalmethodclassificationgenomewideassociationsnpgeneticsclusteringmultiplecomparisonpreprocessinggeneexpressiondifferentialexpressionopenblascpp

0.8 match 1 stars 4.00 score 2 scripts

umich-biostatistics

PPMR:Probabilistic Two Sample Mendelian Randomization

Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data.

Maintained by Michael Kleinsasser. Last updated 6 years ago.

openblascppopenmp

0.8 match 2 stars 3.28 score 19 scripts

ajbass

sffdr:Surrogate Functional False Discovery Rates for Genome-Wide Association Studies

Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization).

Maintained by Andrew Bass. Last updated 1 months ago.

cpp

0.5 match 4 stars 5.00 score 3 scripts

ryansunwork

csmGmm:Conditionally Symmetric Multidimensional Gaussian Mixture Model

Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (2024, <doi:10.1080/01621459.2024.2422124>). The paper is accepted and published online (but not yet in print) in the Journal of the American Statistical Association as of Dec 1 2024.

Maintained by Ryan Sun. Last updated 3 months ago.

0.5 match 3.73 score 108 scripts

tbaghfalaki

GCPBayes:Bayesian Meta-Analysis of Pleiotropic Effects Using Group Structure

Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.

Maintained by Yazdan Asgari. Last updated 1 years ago.

openblascpp

0.5 match 2.88 score 15 scripts

siqixu

mr.pivw:Penalized Inverse-Variance Weighted Estimator for Mendelian Randomization

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.

Maintained by Siqi Xu. Last updated 11 months ago.

0.5 match 2.70 score 1 scripts

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