isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease

This is the repository for the isGWAS algorithm introduced in the preprint linked here.

isGWAS is a optimisation algorithm that ingests sample-level data to rapidly compute accurate estimates of genetic-disease associations.

Key impact areas include:

• Rapid GWAS enabling WGS and NGS-level analysis across millions of individuals in minutes;
• Avoids the need to acquire/access expensive individual-level data and can operate efficiently with sufficient statistics;
• Enables privacy-preserved data sharing and collaboration opportunities;
• Enables biobank design by forecasting disease association for arbitrary sample sizes.

This code repository is currently under development but you can use the algorithm and test your hypothesis at the following website: https://www.optima-isgwas.com/.

Keep checking this page for code updates. In the meantime, feel free to email us at isgwas@optimapartners.co.uk with questions, feedback or ideas.