We are seeking an innovative and motivated statistical geneticist to join our expanding team within the Human Genetics and Computational Biology department at GSK. The candidate should be experienced with applying state of the art analysis methods for the identification of genetically validated drug targets, utilising the largest datasets with integrated phenotype and genetic information and interpreting findings within the context of growing functional genomics evidence to generate biological insights that will translate into innovative new medicines for patients.
At the core of GSK's R&D strategy is to leverage major scientific and technological advances, including investment in biobanks linked to large-scale human health databases; cutting-edge informatics platforms; genetics, functional genomics, and AI/ML capabilities; and leading industry-academia partnerships ( e.g., 23andMe, UK Biobank, FinnGen), to identify the best targets with strong evidence of causal role in disease biology.
The successful candidate will work in a multidisciplinary, collaborative and scientifically driven environment, interacting with key academic collaborators and GSK scientists to advance drug discovery and clinical development in multiple disease areas. This field-leading research addresses important drug discovery and development challenges, directly impacts GSK's R&D pipeline, and publishes results in top scientific journals.
The selected Statistical Genetics Investigator(s) will:
- Develop, implement, and validate approaches to acquire, analyse, interpret, and apply human genetic evidence to inform target selection and validation decisions across a range of disease areas.
- Participate in project teams with GSK scientists and external collaborators.
- Find creative solutions and quickly prototype applications to address challenging scientific questions.
- Ph.D. or equivalent experience in statistical genetics, genetic epidemiology, statistical bioinformatics, human genetics, or a related field.
- Proficient in analyses of human genetic and phenotypic data, interpretation of genetic associations, and use of functional annotation to investigate potential causal mechanisms, with publications in peer reviewed journals.
- Experience with approaches used to understand complex trait genetics, e.g. , genome-wide association analysis, rare-variant analysis, Mendelian randomisation, LD Score regression, polygenic score modelling and cross-trait analysis.
- Expertise with command line interface and working in high performance computing environments, and at least one general purpose programming language ( e.g., R or Python). Familiarity with SQL is a plus.
- Familiarity with tools used for statistical genetics research, e.g., PLINK, SAIGE, LDSC and Hail.
- Ability to develop and evaluate innovative statistical genetics or data analytic methodology.
- Experience analysing data from both array genotyping and next generation sequencing platforms.
- Demonstrable teamwork skills to find creative solutions to scientific questions.
- Excellent communication and influencing skills as well as developing leadership skills for working with multidisciplinary teams and with international colleagues.
- Experience with one or more of the following:
- Maintaining and developing collaborative code, including applications and packages/libraries for the analysis and visualisation of data.
- Genomic data linked to varied sources of complex phenotypic data ( e.g. electronic health records, epidemiological cohorts, clinical trials, longitudinal or time series data).
- Incorporation of additional molecular phenotype data, such as from gene expression profiling, metabolomics, or proteomics into genetic analyses.
- Genetic or genomic data analysis in a distributed data and compute "big data" environment ( e.g. Apache Hadoop, Apache Spark).
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