ASRgwas | Accurate and flexible Genome Wide Association analysis with ASReml-R
Used in conjunction with ASReml-R, ASRgwas is a free add-on R software package that gives researchers a rich yet easy-to-use tool for Genome Wide Association Studies. Using the modelling capabilities of ASReml-R, ASRgwas leads to more accurate and realistic GWAS analyses.
ASRgwas performs genome wide association analysis to connect a trait with genomic variants. It is used by plant and animal breeders to identify genes for selection, and used bymedical scientists for the identification of genes which control diseases and behaviour. ASRgwas can also be applied to the the fields of psychology, epidemiology, pharmacology, forestry, aquaculture and many more.
ASRgwas analysis features
Our ASRgwas R package for genome wide association analysis provides a rich yet easy-to-use tool for your phenotypic and genomic data. Here are the key features:
Discovery of associations
ASRgwas uses the modelling flexibility of ASReml-R to fit GWAS models and drive the discovery of associations in study populations.
Processes raw replicated phenotypic data
ASRgwas is designed to work with replicated data without the prior adjustment of phenotypes that usually leads to information loss.
Complete data to decision pipeline
ASRgwas’ workflow for genetic association studies takes you from preparing/exploring the raw phenotypic data to the identification of gene-to-trait associations using flexible and computationally efficient R functions.
Developed by leading statisticians
The models and methods used in ASRgwas are based on state-of-the-art statistical and computational algorithms using current and proven analytical procedures as well as practical experience.
Imputation of missing genotypes is NOT required
The refined algorithms used in ASRgwas enable fitting GWAS with marker matrices that contain missing values. The traditional marker imputation step, which increases the uncertainty of the analysis, is not mandatory.
Parallel processing and C++ implementation
Parallel processing and C++ implementation offered with ASRgwas reduces analysis processing time and helps manage computational resources better. This is particularly important for complex models.
An ASReml-R tool for accurate GWAS analyses
Robust integration with ASReml-R improves performance and reduces the risk of error. ASRgwas helps users to run more realistic and flexible mixed models with GWAS.
Enhanced post-GWAS output
Take your GWAS analyses further with post-GWAS capabilities that include Q-Q and Manhattan plots, genetic map and marker plots as well as a backward selection function to refine the final set of markers.