What is GATE analysis?
- A novel approach to identifying drug targets through genetics, developed by Professor Paul McKeigue, together with the GATEGene team, from the University of Edinburgh.
- It draws on the “omnigenic” sparse effector hypothesis first proposed in 2017; this postulates that effects of common SNPs on disease are mostly mediated through trans-effects that coalesce on the expression of a sparse set of core genes.
- This hypothesis helps to explain why the results of genome-wide association studies (GWAS) have mostly been disappointing for drug target discovery: studying the genes near GWAS-associated SNPs, even when resolved by co-localisation analysis with cis-effects, mostly yields upstream non-specific genetic determinants less proximal to disease.
- The GATE method uses publicly available genetic association data on gene expression and proteomics, along with genotype data in disease case–control studies, to identify the core genes on which the trans-effects of common variants coalesce to cause disease.
- We have implemented this in an analysis platform that combines GATE analysis with multiple other lines of evidence for causality.
Validation of GATE analysis
Our method is independently validated by several lines of evidence including, for some of the core genes that we have identified:
- Rare variants in the gene cause monogenic forms of the disease.
- Drugs targeting the encoded protein cause or alleviate the disease in humans.
- Perturbation of the gene causes or alleviates the disease in experimental models.
Examples are given below:
| Disease | Core genes/proteins identified | Drugs in development that target this pathway |
|---|---|---|
| Systemic lupus erythematosus | B cell activating receptors: TNFRSF17, TNFRSF13B | BAFF/APRIL inhibitors |
| Type I interferon signalling | IFNAR inhibitors | |
| Rheumatoid arthritis Type 1 diabetes Systemic lupus erythematosus Primary biliary cholangitis |
PDCD1: Programmed cell death protein 1 | PD-1 agonists |
| Eosinophilic asthma | CLC: Charcot-Leyden crystal protein, Galectin 10 | Galectin 10 inhibitors |
| IL5RA | IL-5 inhibitors | |
| Cardiometabolic disease | LPL: lipoprotein lipase | ANGPTL3/4 inhibitors |
What can we offer the pharmaceutical sector?
Solid genetic evidence for drug targets makes eventual licensing 3× more likely.
- Novel drug target identification: we can identify potential drug targets by identifying novel core genes and pathways. Diseases for which novel targets have been identified so far include autoimmune diseases, atopic dermatitis, coronary artery disease, and non-alcoholic fatty liver disease.
- For drugs already in pipeline: we can support decisions about which additional indications to trial in Phase 2, based on genetic evidence of causality of the target for other diseases.
- Prediction of on-target adverse effects: we can identify likely adverse effects based on phenome-wide genetic prediction of the effect of perturbing signalling through the target.
Key References / Published Analyses
- Cordell, H. et al (2026, in press). Genomewide aggregated trans-effects analysis identifies key roles for PD-1 and BAFF/APRIL signaling in primary biliary cholangitis. Liver International
- Braichenko, S. et al (2026). Genome-wide aggregated trans-effects analysis identifies key role in hypertension for proteins involved in clearance of triglyceride-rich lipoproteins. bioRxiv (preprint)
- McKeigue, PM. et al (2025). Genome-wide aggregated trans-effects analysis implicates deficient Type III interferon signalling as a key cause of inflammatory bowel disease. Inflammatory Bowel Diseases
- Iakovliev, A. et al (2025). Discovery of Core Genes for Systemic Lupus Erythematosus via Genome-Wide Aggregated Trans-Effects Analysis. Genes and Immunity
- Zhou, X. et al (2025). Genome-Wide Aggregated Trans Effects Analysis for Circulating Proteins Indicates a Key Role of Immune Checkpoints in Type 1 Diabetes. Diabetes
- Spiliopoulou, A. et al (2025). Genome-Wide Aggregated Trans Effects Analysis Identifies Genes Encoding Immune Checkpoints as Core Genes for Rheumatoid Arthritis. Arthritis & Rheumatology
- Iakovliev, A. et al (2023). Genome-Wide Aggregated Trans-Effects on Risk of Type 1 Diabetes: A Test of the “Omnigenic” Sparse Effector Hypothesis of Complex Trait Genetics. American Journal of Human Genetics
GATEGene Team
Prof. Paul McKeigue
Prof. Helen Colhoun
Dr Athina Spiliopoulou
Dr Andrii Iakovliev
The team is based at the Institute of Genetics & Cancer and Usher Institute at the University of Edinburgh, Scotland, and encompasses expertise in clinical medicine, statistical genetics & bioinformatics, clinical trials and epidemiology, AI/machine learning and computer science.