ASRgwas is a comprehensive R package designed to perform Genome-Wide Association Studies (GWAS) using the modelling flexibility available in ASReml-R in an efficient and reliable way. This library assists with preparing data and matrices, and verifies that they are adequate to perform GWAS. In addition, it has a set of complementary functions to be used for post-GWAS analyses to help with interpretation, use of the output information, and obtaining graphical outputs.
The main tasks considered within ASRgwas are:
- Preparing and auditing phenotypic and genomic data.
- Fitting GWAS models and identifying significant markers.
- Evaluating results and generating tables and graphical output.
Watch the video below to learn more or download the package from ASRgwas.
ASRtriala is a free to use R library. The package is aimed at plant breeders with the purpose of improving their experience, providing robust and in-depth analyses of their single and multi-environment trials. The main capabilities of the package include:
- Auditing/preparing single-trial data and multi-environment trial data.
- Fitting/selecting a single-trial model (spatial and non-spatial) using ASReml-R.
- Fitting/selecting a multi-environment trial model using ASReml-R.
- Enhancing output from multi-environment trial models.
Watch the video below to learn more or download the package from ASRtriala.
ASRgenomics is a package that presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of ASReml-R (or another library) to perform analyses such as Genomic Selection (GS) and/or Genome-Wide Association Studies (GWAS). The main tasks considered are:
- Preparing/exploring pedigree/phenotypic data
- Preparing/exploring genomic data
- Complementing genomic breeding values.
Watch the video below to learn more or download the package from ASRgenomics.
ASExtras4 contains utility functions for the meta-analysis of a series of spatially defined field experiments. It includes functions for:
- diagnostic plots for multi-environment experiments
- summary methods for factor analytic models
- variogram envelopes using parametric bootstrap.
You can read more and download the package from https://mmade.org/asextras4/.
asremlPlus augments the use of ASReml-R in fitting mixed models and packages generally in exploring prediction differences. You can read more and download the package from http://chris.brien.name/rpackages.