Genome-Associated Interaction Networks (GAINs)

We have developed a new and unique capability based upon our network biology expertise and technology, which enables us to make sense of complex omics data. We can use our proprietary platform to analyse, amongst other inputs, genome-wide association study ("GWAS") data, which identifies disease variants at the genome level, to identify potential intervention strategies, therapies and diagnostics.

Our GAINs analysis of GWAS data builds a comprehensive understanding of relevant pathways in disease that we can leverage in a network-aware manner to identify therapeutic targets and/or active compounds for further investigation.

What is GWAS?

GWAS is a population genetics study methodology that identifies DNA variants (termed Single Nucleotide Polymorphisms, SNPs)  associated with disease. It has been in use for over 15 years and more than 40,000 genetic links have been made to human traits and disease from the analyses of in excess of 50,000,000 individual human genomes.

GWAS is often the only functional "omics" data available for some disease tissues and it is therefore a very valuable data source. However, the path from GWAS to disease biology is not straightforward and the huge volume and complexity of the data generated means that, despite the huge financial investment, very little has been delivered in terms of actionable insights and understanding to inform drug development

GWAS

1. Pinpoint Genes from GWAS

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Select GWAS SNP data

Map SNPs to genes / proteins

GAINs

2. Place proteins in Network Context

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Network construction identifies the wider molecular context of the disease associated proteins

3. Introduce Interacting Proteins

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Include other proteins into the network that are likely to interact with those identified from GWAS

4. Apply Network-Aware Functional Enrichment

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Identify modules / pathways that are critical to the structure of the network

Rigorous network-aware statistical network controls for annotation bias, biological noise and error

5. Extract Common Mechanisms

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Collate mechanisms reflecting a common theme in a network-aware manner

Insight

6. Actionable Insight Bought into Relief

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Determine new intervention strategies, disease endotypes & potential biomarkers

How GAINs addresses the failings of current GWAS analysis

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