Dr John Williams

Dr John Williams

Institute of Cancer and Genomic Sciences
Research Fellow

Contact details

Centre for Computational Biology
Haworth Building
University of Birmingham
B15 2TT

John is a bioinformatician who investigates the genetics of neurobehavioural and psychiatric disorders. He use machine learning methods to integrate large genomic and clinical datasets in predictive and causal models of gene-influenced behaviour. 


  • PhD in Bioinformatics, University of Birmingham, 2021

  • MSc in Bioinformatics, University of Leicester, 2015

  • ALB in Biological Sciences, Harvard University, 2014


John studied molecular biology in the US before doing postgraduate education in bioinformatics in the UK. He learned genetics at the MRC Harwell Institute's Mammalian Genetics Unit, working in the Biocomputing group. He's been at the University of Birmingham since 2016, working in the Gkoutos Group.


Research Interests

John is broadly interested in neuropsychiatric genetics, including those involved in chronobiology, autism, schizophrenia, and cognitive decline. His reseach involves genes and traits in big data. On the trait side, it includes characterizing neurobehavioral phenotypes with biomedical ontologies and working with derived imaging phenotypes from MRI. On the genomic side, it encompasses GWAS, genetic epidemiology, and combining biomarkers with genomic data in machine learning models. Ultimately, he would like to advance the understanding of neuropsychiatrid disease. 

Current Projects

View my research portal above.



Williams, John A., et al. “Inflammation and brain structure in schizophrenia and other neuropsychiatric disorders: an interrogation of causal pathways and transcriptomic profiles”. JAMA Psychiatry. In press.

Russ, D.,* Williams, John A*, et al. “Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models”. PLOS One. In press. *Joint first author.

Slater, Luke T., Williams, John A., et al. “Multi-Faceted Semantic Clustering with Text-Derived Phenotypes.” Computers in Biology and Medicine, vol. 138, Nov. 2021, p. 104904.

Karwath, Andreas, … Williams, John A., et al. “Redefining β-Blocker Response in Heart Failure Patients with Sinus Rhythm and Atrial Fibrillation: A Machine Learning Cluster Analysis.” The Lancet, Aug. 2021.

Williams, John A., et al. “A Causal Web between Chronotype and Metabolic Health Traits.” Genes, vol. 12, no. 7, July 2021, p. 1029.

Williams, John A., et al. “Genomic Mutation Identification in Mice Using Illumina Sequencing and Linux-Based Computational Methods.” Current Protocols in Mouse Biology, vol. 9, no. 3, 2019, p. e64.

Bravo-Merodio, Laura, Williams, John A.,  et al. “-Omics Biomarker Identification Pipeline for Translational Medicine.” Journal of Translational Medicine, vol. 17, no. 1, May 2019, p. 155.

Brown, Laurence A., Williams, John,  et al. “Meta-Analysis of Transcriptomic Datasets Identifies Genes Enriched in the Mammalian Circadian Pacemaker.” Nucleic Acids Research, vol. 45, no. 17, Sept. 2017, pp. 9860–73.

View all publications in research portal

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