Showing 27 of total 27 results (show query)
biometris
statgenGxE:Genotype by Environment (GxE) Analysis
Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Maintained by Bart-Jan van Rossum. Last updated 6 months ago.
geneticsgxegxe-modellingmulti-trial-analysis
24.5 match 10 stars 5.53 score 17 scripts
tbates
umx:Structural Equation Modeling and Twin Modeling in R
Quickly create, run, and report structural equation models, and twin models. See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Maintained by Timothy C. Bates. Last updated 2 days ago.
behavior-geneticsgeneticsopenmxpsychologysemstatisticsstructural-equation-modelingtutorialstwin-modelsumx
12.6 match 44 stars 9.45 score 472 scripts
crwerner
FieldSimR:Simulation of Plot Errors and Phenotypes in Plant Breeding Field Trials
Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.
Maintained by Christian Werner. Last updated 2 days ago.
15.8 match 9 stars 7.13 score 62 scripts
gaynorr
AlphaSimR:Breeding Program Simulations
The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Maintained by Chris Gaynor. Last updated 5 months ago.
breedinggenomicssimulationopenblascppopenmp
7.5 match 47 stars 10.22 score 534 scripts 2 dependents
vincentgarin
mppR:Multi-Parent Population QTL Analysis
Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi: 10.1093/genetics/iyae003>.
Maintained by Vincent Garin. Last updated 1 years ago.
9.1 match 2 stars 5.35 score 28 scripts
bioc
CGEN:An R package for analysis of case-control studies in genetic epidemiology
This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population.
Maintained by Justin Lee. Last updated 5 months ago.
snpmultiplecomparisonclusteringfortran
9.8 match 3.90 score 10 scripts
arunabhacodes
MPGE:A Two-Step Approach to Testing Overall Effect of Gene-Environment Interaction for Multiple Phenotypes
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
Maintained by Arunabha Majumdar. Last updated 4 years ago.
6.2 match 1 stars 3.70 score 1 scriptsbioc
epistasisGA:An R package to identify multi-snp effects in nuclear family studies using the GADGETS method
This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results.
Maintained by Michael Nodzenski. Last updated 5 months ago.
geneticssnpgeneticvariabilityopenblascpp
4.3 match 1 stars 4.48 score 5 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
3.5 match 2 stars 5.32 score 175 scripts
covaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
1.3 match 43 stars 12.70 score 300 scripts 9 dependents
cran
gesso:Hierarchical GxE Interactions in a Regularized Regression Model
The method focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure for the joint selection of interaction terms in a regularized regression model. For details see Zemlianskaia et al. (2021) <arxiv:2103.13510>.
Maintained by Natalia Zemlianskaia. Last updated 3 years ago.
4.7 match 2.70 score
michlau
logicDT:Identifying Interactions Between Binary Predictors
A statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. Several search algorithms and ensembling techniques are implemented allowing for finetuning the method to the specific problem. Interactions with quantitative covariables can be properly taken into account by fitting local regression models. Moreover, a variable importance measure for assessing marginal and interaction effects is provided. Implements the procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).
Maintained by Michael Lau. Last updated 6 months ago.
5.9 match 2 stars 2.00 score 2 scripts
biometris
statgenSTA:Single Trial Analysis (STA) of Field Trials
Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Maintained by Bart-Jan van Rossum. Last updated 5 months ago.
1.3 match 4 stars 6.30 score 14 scripts 3 dependentsbioc
trio:Testing of SNPs and SNP Interactions in Case-Parent Trio Studies
Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data.
Maintained by Holger Schwender. Last updated 3 months ago.
snpgeneticvariabilitymicroarraygenetics
1.8 match 4.36 score 19 scriptsmichlau
GRSxE:Testing Gene-Environment Interactions Through Genetic Risk Scores
Statistical testing procedures for detecting GxE (gene-environment) interactions. The main focus lies on GRSxE interaction tests that aim at detecting GxE interactions through GRS (genetic risk scores). Moreover, a novel testing procedure based on bagging and OOB (out-of-bag) predictions is implemented for incorporating all available observations at both GRS construction and GxE testing (Lau et al., 2023, <doi:10.1038/s41598-023-28172-4>).
Maintained by Michael Lau. Last updated 1 years ago.
4.2 match 1 stars 1.70 score 1 scriptscran
GEVACO:Joint Test of Gene and GxE Interactions via Varying Coefficients
A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.
Maintained by Sydney Manning. Last updated 3 years ago.
3.6 match 2.00 score 2 scripts
alenxav
NAM:Nested Association Mapping
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
Maintained by Alencar Xavier. Last updated 5 years ago.
1.2 match 2 stars 5.72 score 44 scripts 1 dependentscran
powerGWASinteraction:Power Calculations for GxE and GxG Interactions for GWAS
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures. It can also calculate power for GxE and GxG without any screening.
Maintained by Charles Kooperberg. Last updated 10 years ago.
5.5 match 1.00 score 4 scripts
ingaschwabe
BayesTwin:Bayesian Analysis of Item-Level Twin Data
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>. Caution! The subroutines of this package rely on the program JAGS, which can be freely obtained from http://mcmc-jags.sourceforge.net.
Maintained by Inga Schwabe. Last updated 6 years ago.
bayesiangeneticsheritabilityitem-response-theorymcmc-samplerpsychometricsjagscpp
1.8 match 3.04 score 11 scriptsbioc
GEM:GEM: fast association study for the interplay of Gene, Environment and Methylation
Tools for analyzing EWAS, methQTL and GxE genome widely.
Maintained by Hong Pan. Last updated 5 months ago.
methylseqmethylationarraygenomewideassociationregressiondnamethylationsnpgeneexpressiongui
0.6 match 5.43 score 27 scriptscran
metaGE:Meta-Analysis for Detecting Genotype x Environment Associations
Provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi{10.1371/journal.pgen.1011553}).
Maintained by Annaïg De Walsche. Last updated 22 days ago.
1.3 match 2.30 score 1 scripts
randel
ofGEM:A Meta-Analysis Approach with Filtering for Identifying Gene-Level Gene-Environment Interactions with Genetic Association Data
Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.
Maintained by Jiebiao Wang. Last updated 7 years ago.
0.8 match 3 stars 3.18 score 3 scripts
dovinij
GxEprs:Genotype-by-Environment Interaction in Polygenic Score Models
A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).
Maintained by Dovini Jayasinghe. Last updated 10 months ago.
0.5 match 2 stars 3.30 score