Contact information

Biography

I obtained a BSc in Molecular Biology from the University of St Andrews before completing my PhD at the University of Cambridge. There I undertook my research at the EMBL European Bioinformatics Institute (EMBL-EBI), focusing on the genetics of retinal morphology using image-derived phenotypes. I then received a Marie Skłodowska-Curie Actions Postdoctoral Fellowship to work at the University of Copenhagen on the genetics underlying morphology of neuroendocrine regions using MRI-derived phenotypes before moving to the Nuffield Department of Population Health at the University of Oxford. I am now establishing my independent research group at the Nuffield Department of Women’s and Reproductive Health as a Wellcome Early Career Fellow.

Colleges

Worcester College

Hannah Currant

Principal Investigator

Statistical Genetics
Electronic Health Care Records
High Dimensional Phenotypes

Wellcome Early Career Fellow

Research

I am a Principal Investigator based in the Big Data Institute and part of the Nuffield Department of Women's and Reproductive Health. I am interested in the use of statistical genetics and large-scale data analysis to further our understanding of reproductive health. I received the Wellcome Trust Early Career Award to deliver my research programme focused on identifying biomarkers of hormonal medication compatibility.

Hormonal medications are accessible and effective with multiple uses, including contraception and menopausal hormone therapy, however many individuals experience side-effects, which may go unrecorded. Such side-effects can significantly impact patients’ quality of life and individuals may change medication formulation multiple times to identify the type with minimal side-effects, a potentially lengthy and distressing process. The biology underlying this differential response is largely unknown, presenting a challenge for clinicians prescribing.

Utilising longitudinal electronic healthcare records, we will define new phenotypes representing hormonal medication compatibility in the case of contraceptive and menopausal hormone therapy usage. We’ll use statistical genetic techniques to identify biomarkers of hormonal medication compatibility and assessing their predictive capabilities towards efficient personalised prescription of hormonal medication.

Recent publications

Individuals whose phenotype deviates from genetic expectation defined by common variation are enriched for rare damaging variants in genes that cause rare disease.

Baya NA. et al, (2025), medRxiv

Genome-wide associations spanning 194 in-hospital drug dosage change phenotypes highlight diverse genetic backgrounds in concurrent drug therapy.

Henriksen AP. et al, (2025), Comput Struct Biotechnol J, 28, 239 - 248

Genome-wide analysis identifies 66 variants underlying anatomical variation in human neuroendocrine structures and reveals links to testosterone

Currant H. et al, (2024)

Sub-cellular level resolution of common genetic variation in the photoreceptor layer identifies continuum between rare disease and common variation.

Currant H. et al, (2023), PLoS Genet, 19

Visualising disease trajectories from population-wide data.

Hjaltelin JX. et al, (2023), Front Bioinform, 3

More publications

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