Software & Tutorial

This page list the softwares  and tutorial developed by our group


This R package contains several utilities and modules that complement and enhance the data analysis and visualization of results from existing GWAS analysis software. It also provides several tools for advanced visualization of genomic data and utilizes the power of the R language to aid in preparation of publication-quality figures.

Reference: Kierczak M, Jabłońska J, Forsberg S K G, et al. cgmisc: enhanced genome-wide association analyses and visualization. Bioinformatics, 2015: btv426.


This is an R-package for estimating stochastic identity-by-descent (IBD) matrices for large F2 intercrosses. Arbitrary allelic-segregation of founder alleles is allowed. Genotypic and gametic IBD matrices can be estimated for a single-locus or two-loci with epistatic effects. The computational performance is enhanced by utilizing parallel-computing using the snowfall package. Additional functions allow calculating locus-specific IBD matrices along entire chromosomes or epistatic IBD matrices for pairs of chromosomes. The output matrix has several format options, where we propose a format using a principle-component incidence matrix that improves the estimation efficiency of variance component models in quantitative trait loci (QTL) analyses. This package is useful for providing input to our previously developed flexible intercross analysis (FIA) method, which models within-line segregation in analyses of outbred line-cross QTL mapping experiments.

Reference:Shen X, Nettelblad C, Rönnegård L, Carlborg Ö. Flexible Monte Carlo Identity-By-Descent Matrix Estimation with Given Base Generation Structures in F2 Intercross Designs.*checkout*/pkg/inst/doc/MCIBD.pdf?root=mcibd


qtl.outbred is an umbrella R-package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. This provides users interested in analyzing data from experimental crosses between outbred populations with the functionality of the extensive R/qtl package.

Reference: Nelson R M, Shen X, Carlborg Ö. qtl. outbred: Interfacing outbred line cross data with the R/qtl mapping software. BMC research notes, 2011, 4(1): 154.


The vGWAS R-package provides functions for genome-wide association analysis for identifying loci displaying a genetic variance-heterogeneity using nonparametric variance test. In addition, it also provide some additional functionalities for visualization and analyses of variance-heterogeneity loci.

Reference:Shen X, Pettersson M, Rönnegård L, et al. Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana. PLoS Genet, 2012, 8(8): e1002839.

5.PASE  click here to download

This software implements a novel method to predict the effects of non-synonymous Amino Acid Substitutions based on the physicochemical property changes of the respective amino acids.

Reference: Li X, Kierczak M, Shen X, Ahsan M, Carlborg Ö, Marklund S. PASE: a novel method for functional prediction of amino acid substitutions based on physicochemical properties. Front. Genet, 2013, 4: 21.

6.MAPfastR Click here to download the tested version on windows7 or MAC os (R version 2.15.3) and all dependent packages.

MAPfastR is an R-package for mapping quantitative trait loci (QTL) in outbred line crosses. It contains all the functions required to perform a QTL analysis in outbred lines. Some of the functions implement different algorithms that can be used interchangeably and it is up to the user to decide which will be applied. This gives the user a certain degree of flexibility when constructing the analysis pipeline and helps creating custom-tailored solutions depending on the type of analyzed data and the aims of the research.

Reference: Nelson R M, Nettelblad C, Pettersson M E, et al. MAPfastR: quantitative trait loci mapping in outbred line crosses[J]. G3: Genes| Genomes| Genetics, 2013, 3(12): 2147-2149.

7. Materials from LUPA workshop on Genome-wide association study.  Talk1 Talk2 Talk3 Tutorial Data

8.R and GenABEL-based tutorial on statistical analysis in genome-wide association studies. Tutorial Data