Alzheimers disease and Parkinson’s disease (AD and PD) are the two most common neurodegenerative conditions with known genetic, medical and lifestyle factors that influence risk. There are few, if any, interventions that slow progression in these disorders. Biogen has been ranked as the #1 pharmaceutical institution based on innovation, being AD and PD within our main disease focus.
The statistical genetics group is looking for a highly-skilled candidate who is eager to learn and apply the most novel methods of statistical genetics and machine learning on very large scale genetic data.
The successful candidate will participate on the statistical design and analysis of human genetic studies with an emphasis on genome-wide association studies of phenotypes with > 500,000 previously unpublished samples. The candidate should have a foundation in statistics and genetics, a strong work ethic. The position requires excellent written and oral communication skills allowing the preparation of effective reports, presentations and manuscripts. The position offers the opportunity to interact with research biologists, clinicians and computational scientists on complex datasets incorporating genetic, genomic and clinical data.
The aims of the main research project which analysis will be lead by the successful candidate are:
1. Identify and characterize novel AD/PD loci using state of the art phenotyping and GWAS in very large studies
2. Identify novel endophenotypes from imaging/cognitive data
3. Use rich endophenotypes to infer mechanism of action of disease loci
4. Use mendelian randomization approaches to evaluate potential therapeutic effect of proposed targets.
In Biogen we consider these activities pre-competitive and therefore all the results will be able to be shown in scientific meetings and published in scientific journals. The candidate will be mentored by Dr. Karol Estrada who has a very strong publication record on high impact journals.
Experience in analysis and interpretation of complex disease genetics in large datasets (ideally both GWAS and Next Generation Sequencing studies).
Strong programming skills, as well as a firm grounding in relevant statistical methodologies, will be essential.
Experience processing big data using Distributed File Systems is a plus.
PhD in statistics, genomics, human genetics, computer science or related field.
Internship or Co-op