gwastic-desktop 0.4.1

GWAS software that combines traditional statistical methods with the power of Artificial Intelligence

About GWAStic

GWAStic is a software for Genome-Wide Association Study (GWAS) that combines traditional statistical methods with the power of Artificial Intelligence (AI) for comprehensive genetic analysis.

Table of Contents

• Installation
• Documentation
• References
• Acknowledgment

Key Features:

• Cross Platform

• Comprehensive Genetic Analysis: GWAStic offers a wide range of methods to analyze your genomic data, allowing you to explore the associations between genetic variants and traits of interest comprehensively.

• AI-Enhanced Data Analysis: Harness the capabilities of machine learning and AI to uncover subtle patterns, interactions, and associations that may be missed by conventional statistical methods.

• Genomic Prediction: Take your research to the next level by using GWAStic's advanced AI models for genomic prediction. Predict future health outcomes, disease risks, or phenotypic traits based on your genetic data and environmental factors.

• User-Friendly Interface: GWAStic's intuitive interface makes it accessible to both novice and experienced researchers. Seamlessly navigate through your data, perform analyses, and visualize results with ease.

• Customizable Workflows: Tailor your analysis to your specific research goals with customizable workflows. Define your parameters, select the appropriate statistical models, and integrate AI components as needed for a personalized analysis experience.

• Collaborative Research: Collaborate seamlessly with colleagues and share your findings securely within the platform.

• Frequent Updates: Stay at the forefront of genetic research with regular software updates. GWAStic incorporates the latest advancements in GWAS and AI methodologies to keep your analyses up-to-date.

1. Installation

GWAStic software was build and successfully tested on Windows operating system (Windows 7 and 10).

Windows:

We recommend to install Anaconda and create an environment:

1. Install Anaconda (https://www.anaconda.com/distribution/)

2. conda create --name gwastic_env python=3.9

3. conda install pip

4. Open the Anaconda prompt and activate your GWAStic environment.
   conda activate gwastic_env
   

Then install GWAStic via pip:

5. pip install gwastic_desktop

6. Type gwastic in the command line to start the software.

Linux:

We recommend to install Anaconda and create an environment:

1. wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
2. chmod +x Miniconda3-latest-Linux-x86_64.sh
3. ./Miniconda3-latest-Linux-x86_64.sh
4. cd /home/username/miniconda3
5. source ~/miniconda3/bin/activate

Then install GWAStic via pip:

6. pip install gwastic_desktop

7. apt-get install libgl1

8. Type gwastic in the command line to start the software.

Supported input file formats

• VCF file format (including vcf.gz) and Plink BED (binary) format are supported for all GWAS methods. In case of vcf, you first must convert the genotype data to bed file format.

VCF example file

• Phenotypic data must be three columns (Family ID; Within-family ID; Value) text or CSV file delimited by space.

Phenotype example file

2. References

2.1 - Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, de Bakker PIW: Daly MJ & Sham PC (in press) PLINK: a toolset for whole-genome association and population-based linkage analysis. American Journal of Human Genetics.

2.2 - Lippert, C., Listgarten, J., Liu, Y. et al. FaST linear mixed models for genome-wide association studies. Nat Methods 8, 833–835 (2011). https://doi.org/10.1038/nmeth.1681

3. Acknowledgment

Gwastic has incorporated the FaST-LMM library (fastlmm.github.io), to enhance its Linear Mixed Models (LMM) feature. We thank Carl Kadie and David Heckerman for not only creating this exceptional tool but also providing outstanding support and discussions.