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To leverage the full benefits of ASRgwas, the package has been designed to integrate with ASReml-R to improve performance, reduce the risk of error and lead to more accurate and realistic GWAS analyses. When ASRgwas is combined with an ASReml-R license, ASRgwas uses the efficient and reliable modelling flexibility of ASReml-R to fit GWAS models to drive the discovery of associations in study populations.

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Together with ASReml-R statistical analysis software in R

ASRgwas was created by VSNi, a world leader in the advancement of statistical analysis software and statistical solutions for biosciences. 

Used in conjunction with ASReml-R, ASRgwas is free add-on software for preparing and auditing phenotypic and genomic data for use in genome-wide association studies. Its complete data-to-decision pipeline takes you through preparing and exploring the raw phenotypic data to identifying gene-to-trait associations. 

Developed in-house, our software creators know science, know statistics and understand both the current and emerging needs of users. ASRgwas is the third free R package designed, created and released by VSNi. Our products are used by over 10,000 professionals. Accurate and trustworthy data analysis is imperative to achieve their goals, and of course yours.

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Why use ASRgwas for genomic association studies

ASRgwas is free to use with your ASReml-R licence. It is only available to download via the [ASReml Knowledge Base](https://asreml.kb.vsni.co.uk/download-asrgwas/).

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How to download

ASRgwas uses the modelling flexibility of ASReml-R to fit GWAS models and drive the discovery of associations in study populations.

Discovery of associations

ASRgwas is designed to work with replicated data without the prior adjustment of phenotypes that usually leads to information loss.

Processes raw replicated phenotypic data

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.

Complete data to decision pipeline

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.

Developed by leading statisticians

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.

Imputation of missing genotypes is NOT required

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.

Parallel processing and C++ implementation

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.

An ASReml-R tool for accurate GWAS analyses

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.

Enhanced post-GWAS output

ASRgwas fits GWAS models with both Normally or Binomially distributed responses. For binomial data, ASRgwas offers more complex and realistic models using the latest statistical models (GLMM).

Normally or Binomially distributed responses

ASRgwas lets you prioritise what’s most important to you: higher accuracy with slower processing speed or a faster algorithm with less approximate results. Users can choose the method they want to use.

Select approximated or exact algorithms

ASRgwas allows for any number of fixed or random structures, the inclusion of covariates and the specification of heterogeneous error variances. ASRgwas allows the incorporation of many relationship matrices, such as additive and dominant genomic relationship matrices.

Enhanced modelling flexibility

ASRgwas is intended to keep any loss of data to a minimum when moving from stage 1 (phenotypic analysis) to phase 2 (genomic analysis) of phenotypic data analysis by allowing the use of the weights from stage 1. This provides considerably more robust analyses and accurate results.

Perform two-stage analysis with weights

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:

ASRgwas analysis features

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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 | Accurate and flexible Genome Wide Association analysis with ASReml-R

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ASRgwas is a free R package offering researchers a rich yet easy-to-use tool for Genome Wide Association Studies. Using the modelling available in ASReml-R efficiently and reliably, ASRgwas leads to more accurate and realistic GWAS analyses.

ASRgwas

ASRgwas provides scientists and researchers with a state-of-the-art R package for preparing and auditing phenotypic and genomic data. 

This free software was created with robust statistical and biological methodologies to provide scientists with an advanced tool for the discovery of putative associations in their study populations. 

ASRgwas genome wide association analysis helps users to run more realistic and flexible mixed models with GWAS, leveraged by the strengths of ASReml. ASRgwas brings together in one tool, post-GWAS functions that help you explore and understand your results.

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GWAS analysis software in an R environment

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.