During my PhD, my research focused on developing computational methods to improve the genetic diagnosis of individuals with rare monogenic diseases. I developed novel approaches to more accurately describe clinical phenotypes and identify causative genes. By transforming free-text clinical descriptions into machine-readable, weighted phenotypic profiles, I demonstrated how “bespoke virtual gene panels” could significantly improve the efficiency of genomic interpretation.
The rigorous bioinformatics and statistical training provided by my PhD set the foundation for my subsequent career achievements in the genomics of inflammatory skin disease. In my first post-doctoral role, I led an international genome-wide association (GWAS) meta-analysis that identified 29 regions of the genome associated with risk of developing severe acne, revealing shared biological pathways between common acne and rare inflammatory conditions. Following this, I joined the BIOMAP European consortium, where my work demonstrated how genetic liability (in the form of polygenic risk scores) can effectively predict severe clinical outcomes in psoriasis patients.
Building on the trajectory established during my doctoral and post-doctoral training, I have recently been appointed as a Research Fellow within the Skin Genetics Consortium, an international initiative dedicated to uncovering the genetic architecture of skin health and disease. I am co-leading the analysis of genome-wide association studies in over 5 million research participants worldwide for over 100 skin traits, using the computational and scientific expertise I built during my PhD.
The support of The Generation Trust and The Peter Stebbings Memorial Charity during my PhD enabled me to develop the technical expertise and clinical perspective necessary for me to become a leader in the field of human genetics.
Funding situation: 4 years funding
PhD thesis: https://kclpure.kcl.ac.uk/portal/files/116537802/2019_Saklatvala_Jake_1454769_ethesis.pdf
First-author paper published during thesis:
– Saklatvala, J. R., Dand, N., & Simpson, M. A. (2018). Text-mined phenotype annotation and vector-based similarity to improve identification of similar phenotypes and causative genes in monogenic disease patients. Human mutation, 39(5), 643–652. https://doi.org/10.1002/humu.23413
First-author papers published since PhD:
– Mitchell, B. L., Saklatvala, J. R., … (2022). Genome-wide association meta-analysis identifies 29 new acne susceptibility loci. Nature communications, 13(1), 702. https://doi.org/10.1038/s41467-022-28252-5
– Saklatvala, J. R., … (2023). Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies. The Journal of investigative dermatology, 143(8), 1598–1601.e10. https://doi.org/10.1016/j.jid.2023.02.010
– Saklatvala, J. R., … Simpson, M. A. (2025). Genetic liability to psoriasis predicts severe disease outcomes. Genome medicine, 18(1), 14. https://doi.org/10.1186/s13073-025-01561-2
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