Inga Prokopenko

Prof Inga Prokopenko is a Head of Statistical Multi-Omics at the School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, UK. The Section established in 2019 is targeting the University’s interest in omics technologies, crucial for utilising large high-dimensional datasets in human and animal health (One Health – One Medicine approach). She is leading the Faculty’s of Health and Medical Sciences interest in AI as the Co-Director within the Pan-University People-Centred AI institute. The Section of Statistical Multi-Omics focuses on method development and application for analyses of big data from novel omics technologies on a newly established high-performance computing (HPC) cluster. This HPC cluster is setting the University’s capacity and research in health and omics Big Data analytics and AI. The section is leading on several UK biobank projects using whole-exome/-genome sequencing and genome-wide imputed and imaging data, hundreds of health-related clinical and laboratory variables. She and her group tightly collaborate with the Estonian and Finnish (FinnGen) Biobanks, Northern Finland Birth Cohorts 1966 and 1986, datasets from ATLAS BIOMED, Ukrainian LUCAR and GIWU-CF, Italian Brisighella and German SORBS studies, to name some, using their respective (epi)genomic, metabolomic and epidemiological data. Prof Prokopenko has led and supervised development of innovative approaches, implementing AI among other, for the analyses of human multi-omics large-scale datasets.

Prof Prokopenko obtained PhD in Pharmacoepidemiology and Pharmacoeconomics at the University of Pavia, Italy. For several years, she worked in Psychiatry Translational Medicine & Genetics R&D of GlaxoSmithKline, Verona, Italy, and subsequently has undertaken her research at the Wellcome Trust Centre for Human Genetics, University of Oxford, UK. As Associate Professor in Human Genomics at Imperial College London, UK, she contributed to the activities of Diabetes Network of Excellence. As part of teaching activities, she developed modules, coordinated assessments, and lectured at multiple postgraduate courses, including MScs in Genomic Medicine, Human Molecular Genetics, Applied Omics, Molecular Epidemiology, Cancer Biology and Immunology. As a Co-director of AI institute, she contributes to training of postgraduates within MSc in AI and online MSc in People-Centred AI courses.

Prof Prokopenko’s scientific research spans over more than past two decades. Her major scientific contributions are to the genetics of type 2 diabetes, glycaemic traits and early growth. These brought discoveries of dozens of DNA variants affecting individual variability in/susceptibility to these phenotypes. Her recent work highlighted the causal effects of depression on susceptibility to type 2 diabetes as well as that of high glucose levels in type 2 diabetes on lung dysfunction, which should be considered as a complication of this disease. She led the efforts on sex-dimorphism of fasting glycaemic trait genetic effects and on random glucose with large-scale GWAS meta-analyses. Her research highlighted that variation in GLP1R gene might define diverse response to GLP-1R agonist drugs. This includes identification of genetic relationships between susceptibility to type 2 diabetes and circadian rhythms through genetic variability within the receptor of melatonin 1b gene or MTNR1B, which received wide media coverage upon publication. She is an active leader of major international efforts within DIAGRAM, MAGIC, ENGAGE and EGG genome-wide association studies consortia. She co-authored over 220 peer-reviewed publications, and is a leading/senior author on 40 of them, her H-index (Jan 2024) is 96 by Google Scholar (over 65k citations). She is a member of the European Human Exposome network funded through the EU H2020 programme and is leading the University of Surrey efforts within the LONGITOOLS project tackling exposome of cardiometabolic traits and disease.

Her current research focuses on method development for the high-dimensional multi-omics data analyses. The innovative approaches use machine learning approaches and AI as well as tackle dissection of the longitudinal multi-omics effects. From the applied perspective, Prof Prokopenko’s focus is on the improved profiling, prevention and progression tracking, evaluation of trajectories in pathogenesis of human diseases. Her major interest is in metabolic and early growth phenotypes, type 2 diabetes, its major comorbidities, perinatal maternal health and dissection of multimorbidity through the life span.