TOPS Research Fellow - Genomics Analyst
About us:
The Social, Genetic & Developmental Psychiatry (SGDP) Centre is uncovering the genetic and environmental influences on mental health and psychiatric disorders. Our research encompasses behavioural genetics, molecular genetics, experimental psychology and psychiatry, and neuroimaging. Scientists at the SGDP lead some of the UK’s foremost longitudinal cohort and twin studies including TEDS, eRISK, Dunedin, IMAGEN and GLAD.
The NIHR BioResource Centre Maudsley leads the NIHR BioResource for Mental Health. We focus on recruiting participants experiencing mental health conditions, as well as healthy people.
Alongside the GLAD and TEDs study, Prof. Breen and Eley's teams boast an accomplished portfolio of clinical and genetic studies which aim to improve treatment and outcomes for those affected by anxiety, depression, eating disorders and other mental health conditions.
About the role:
We are seeking a highly motivated postdoctoral Research Fellow in statistical genetics and genomic data science to join Professor Gerome Breen’s team at King’s College London and close collaboration with Prof Thalia Eley and her team. This post will play a central role in delivering the genomic analysis components of the Treatment Outcomes Prediction Study (TOPS), a major Wellcome-funded program investigating the biological and cognitive mechanisms underlying psychological treatment response in anxiety disorders.
TOPS is an ambitious, multi-cohort precision psychiatry initiative designed to identify genetic and cognitive predictors of treatment response across large, well-characterised samples. The programme integrates genomic data with cognitive phenotyping, digital assessments, and linked NHS Talking Therapies outcomes from multiple national cohorts including GLAD, TEDS and Genes & Health.
The successful candidate will lead and contribute to genomic and statistical genetic analyses across these datasets. The post will lead on our collaborations with international consortia such as the Psychiatric Genomics Consortium anxiety and depression groups. This includes genome-wide association analyses of disorders, symptoms, treatment response and cognitive mechanisms, estimation of SNP-heritability and genetic correlations, development and evaluation of polygenic scores, and integration of genomic predictors into multivariable and machine-learning prediction models for treatment outcome.
Working closely with Professors Breen, Eley and collaborators across psychiatric genetics, clinical psychology and data science, you will contribute to one of the largest efforts globally to understand how genomic variation influences psychological treatment response. The role offers opportunities to work with large-scale genomic and linked clinical datasets within secure Trusted Research Environments, and to collaborate with international consortia and national infrastructures.
This post will suit a candidate with strong quantitative and genomic analysis skills who is keen to apply statistical genetics and predictive modelling approaches to clinically meaningful questions in mental health. You will be embedded within the Social, Genetic & Developmental Psychiatry Centre at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), an internationally leading environment for psychiatric genomics and longitudinal cohort research.
The post is full-time (35 hours per week) and offered on a fixed-term contract for 2 years, with the possibility of extension.
About you:
Essential criteria
- PhD qualified in psychiatric genetics, biostatistics, statistics or related field
- Proficient in using R, Python or similar programming language
- Extensive experience of working with clinical or equivalent data.
- Extensive experience in developing analytic pipelines.
- Experience working with mental disorders in general, anxiety and/or depression in particular, and/or with treatment response data.
Desirable criteria
- Experience of working within Trusted Research Environments to analyse data.
- Good presentation and communication skills
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