Alex Cornish

Epidemiology Research Fellow


I’m a Postdoc in the Molecular and Population Genetics Team at the Institute of Cancer Research. Before this I was a Ph.D. student in the Structural Bioinformatics Group at Imperial College London, supervised by Michael Sternberg.


alex [dot] cornish [at] icr [dot] ac [dot] uk

Genetics and Epidemiology
Institute of Cancer Research
15 Cotswold Road, London, UK, SM2 5NG

Google Scholar
Research Gate


Genome-wide association analysis identifies a meningioma risk locus at 11p15.5 2018
Claus, E.B., Cornish, A.J., Broderick, P., Schildkraut, J.M., Dobbins, S.E. et al.

Whole-genome sequencing of multiple myeloma reveals oncogenic pathways are targeted somatically through multiple mechanisms 2018
Hoang, P.H., Dobbins, S.E., Cornish, A.J., Chubb, D., Law, P.J. et al.

Impact of atopy on risk of glioma: a Mendelian randomisation study 2018
Disney-Hogg, L., Cornish, A.J., Sud, A., Law, P.J., Kinnersley, B. et al.
BMC Medicine

Influence of obesity-related risk factors in the aetiology of glioma 2018
Disney-Hogg, L., Sud, A., Law, P.J., Kinnersley, B., Cornish, A.J. et al.
British Journal of Cancer

Mendelian randomisation study of the relationship between vitamin D and risk of glioma 2018
Takahashi, H., Cornish, A.J., Sud, A., Law, P.J., Kinnersley, B. et al.
Scientific Reports

PhenoRank: reducing study bias in gene prioritisation through simulation 2018
Cornish, A.J., David, A. and Sternberg, M.J.E.

Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types 2015
Cornish, A.J., Filippis I., David, A. and Sternberg, M.J.E.
Genome Medicine

SANTA, quantifying the functional content of molecular networks 2014
Cornish, A.J. and Markowetz, F.
PLOS Computational Biology



PhenoRank uses PPI and phenotype data from multiple species to prioritize disease genes, whilst avoiding being biased by the number of data associated with each gene. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms. Using this simulation-based approach ensures PhenoRank is not biased towards genes with more available data and improves its performance.


An R-package for identifying disease-associated cell types. DiseaseCellTypes contains implementations of two alternative methods: Gene Set Compactness (GSC) and Gene Set Overexpression (GSO). Also provided are functions for building cell type-specific interactomes and cell type-based diseasomes.


An R-package for linking networks of molecular interactions to cellular functions and phenotypes. SANTA functionally annotates networks like standard enrichment methods annotate lists of genes.