Professor of Genetic Epidemiology and
Statistical Genetics, University of Edinburgh
also Honorary Consultant in Public Health, NHS Lothian
This is my personal web page. For the Usher Institute, information about research is on this page, information about taught postgraduate courses is on this page, and information about PhDs is on this page. Larger files, including software packages and public datasets, can be found on my research group’s home page on my server.
of Genetic Epidemiology
Lecturer then Reader then Professor of Metabolic and Genetic
Heart Foundation Research Fellow
Training Fellow in Clinical Epidemiology
Usher Institute of Population Health
Sciences and Informatics
University of Edinburgh Medical School,
Teviot Place, Edinburgh EH8 9AG
Phone +44 131 650 4556
If you need to send me confidential material by email, use my PGP
public key (obtained by searching for my email address on a PGP key
server such as https://pgp.mit.edu)
for which the fingerprint is
683E 7E3B E8B3 83BB 8F80 363A A034 3F3B B2D6 769A
For best practice, you should confirm the key fingerprint with me in person or by video link before using it.
If you need to transfer large data files securely, I can set up an SFTP account for you on my server. You will need to use SSH public key authentication. Instructions for setting this up on a Windows PC are here.
My research focuses on methods for molecular and genetic epidemiology, with applications in clinical prediction and personalized medicine. These methods make use of Bayesian and computationally-intensive statistical methods, and machine learning methods for constructing predictors. I work closely with Helen Colhoun’s research group at the Centre for Genomic and Experimental Medicine. This collaboration includes the development of an analysis platform based on deidentified electronic health records and the use of this platform to study drug safety and complications of diabetes.
My group’s current research includes
Construction of predictive models for drug response in rheumatoid arthritis in two collaborative studies: the MRC-funded MATURA consortium and the Scottish Early Rheumatoid Arthritis cohort
Development of biomarker-based predictions of diabetic complications in the SUMMIT European consortium and the Scottish Diabetes Research Network Type 1 Bioresource
Development of a platform (GENOSCORES) for constructing genotypic predictors from summary results of genome-wide association studies
Development of deep belief nets for learning to predict diabetic complications from retinal images
Bell S, Farran B, McGurnaghan S, McCrimmon RJ, Leese GP, Petrie JR, McKeigue P, Sattar N, Wild S, McKnight J, Lindsay R, Colhoun HM, Looker H. Risk of acute kidney injury and survival in patients treated with Metformin: an observational cohort study.BMC Nephrol. 2017 May 19;18(1):163. doi: 10.1186/s12882-017-0579-5. PubMed PMID: 28526011; PubMed Central PMCID: PMC5437411.
Spiliopoulou A, Colombo M, Orchard P, Agakov F, McKeigue P. GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing. Genetics. 2017 May;206(1):91-104. doi: 10.1534/genetics.117.200063. Epub 2017 Mar 27. PubMed PMID: 28348060; PubMed Central PMCID: PMC5419496.
Quell JD, Römisch-Margl W, Colombo M, Krumsiek J, Evans AM, Mohney R, Salomaa V, de Faire U, Groop LC, Agakov F, Looker HC, McKeigue P, Colhoun HM, Kastenmüller G. Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies. J Chromatogr B Analyt Technol Biomed Life Sci. 2017 Apr 4. pii: S1570-0232(17)30568-8. doi: 10.1016/j.jchromb.2017.04.002. [Epub ahead of print] PubMed PMID: 28479069.
Sandholm N, Van Zuydam N, Ahlqvist E, Juliusdottir T, Deshmukh HA, Rayner NW, Di Camillo B, Forsblom C, Fadista J, Ziemek D, Salem RM, Hiraki LT, Pezzolesi M, Trégouët D, Dahlström E, Valo E, Oskolkov N, Ladenvall C, Marcovecchio ML, Cooper J, Sambo F, Malovini A, Manfrini M, McKnight AJ, Lajer M, Harjutsalo V, Gordin D, Parkkonen M; FinnDiane Study Group, Jaakko Tuomilehto., Lyssenko V, McKeigue PM, Rich SS, Brosnan MJ, Fauman E, Bellazzi R, Rossing P, Hadjadj S, Krolewski A, Paterson AD; DCCT/EDIC Study Group, Jose C. Florez., Hirschhorn JN, Maxwell AP; GENIE Consortium, David Dunger., Cobelli C, Colhoun HM, Groop L, McCarthy MI, Groop PH; SUMMIT Consortium.. The Genetic Landscape of Renal Complications in Type 1 Diabetes.J Am Soc Nephrol. 2017 Feb;28(2):557-574. doi: 10.1681/ASN.2016020231. Epub 2016 Sep 19. PubMed PMID: 27647854; PubMed Central PMCID: PMC5280020.
Postmus I, Warren HR, Trompet S, Arsenault BJ, Avery CL, Bis JC, Chasman DI, de Keyser CE, Deshmukh HA, Evans DS, Feng Q, Li X, Smit RA, Smith AV, Sun F, Taylor KD, Arnold AM, Barnes MR, Barratt BJ, Betteridge J, Boekholdt SM, Boerwinkle E, Buckley BM, Chen YI, de Craen AJ, Cummings SR, Denny JC, Dubé MP, Durrington PN, Eiriksdottir G, Ford I, Guo X, Harris TB, Heckbert SR, Hofman A, Hovingh GK, Kastelein JJ, Launer LJ, Liu CT, Liu Y, Lumley T, McKeigue PM, Munroe PB, Neil A, Nickerson DA, Nyberg F, O'Brien E, O'Donnell CJ, Post W, Poulter N, Vasan RS, Rice K, Rich SS, Rivadeneira F, Sattar N, Sever P, Shaw-Hawkins S, Shields DC, Slagboom PE, Smith NL, Smith JD, Sotoodehnia N, Stanton A, Stott DJ, Stricker BH, Stürmer T, Uitterlinden AG, Wei WQ, Westendorp RG, Whitsel EA, Wiggins KL, Wilke RA, Ballantyne CM, Colhoun HM, Cupples LA, Franco OH, Gudnason V, Hitman G, Palmer CN, Psaty BM, Ridker PM, Stafford JM, Stein CM, Tardif JC, Caulfield MJ, Jukema JW, Rotter JI, Krauss RM. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins. J Med Genet. 2016 Dec;53(12):835-845. doi: 10.1136/jmedgenet-2016-103966. Epub 2016 Sep 1. PubMed PMID: 27587472; PubMed Central PMCID: PMC5309131.
Scotland G, McKeigue P, Philip S, Leese GP, Olson JA, Looker HC, Colhoun HM, Javanbakht M. Modelling the cost-effectiveness of adopting risk-stratified approaches to extended screening intervals in the national diabetic retinopathy screening programme in Scotland. Diabet Med. 2016 Jul;33(7):886-95. doi: 10.1111/dme.13129. Epub 2016 May 11. PubMed PMID: 27040994.
Sample size requirements for learning to classify with high-dimensional biomarker panels
This paper describes a simple method for calculating the sample size required to learn to classify with a high-dimensional biomarker panel, based on the asymptotic distribution of the log Bayes factor
This R script uses the method described in the paper to calculate and plot a learning curve for a classifier as a function of the ratio of cases to biomarkers. To use it, you have to specify the performance (as C-statistic or AUROC) of the optimal classifier that could be learned from a training sample of infinite size, and the proportion of biomarkers that have nonzero effect sizes.
Notes on sample size calculation, with extension to two-step Mendelian randomization
Note on evaluating predictive performance of generalized linear models fitted to survival data
Using summary GWAS data to construct genotypic scores for testing and prediction
Tutorial: molecular pathology, stratified medicine and prediction from biomarkers
Tutorial: genetic admixture and stratification
Tutorial: genotypic prediction and Mendelian randomization
Tutorial: life-course epidemiology
Tutorial: genetic association and prediction