Postdoctoral Fellow – Human Genetics, Unit of Statistical Genetics

Context: The rising cost of drug development – a phenomenon known as Eroom’s Law – is a call for innovation. Over the past decade, the statistical genetics community discovered thousands of variant-trait associations in human populations. By exploring this wealth of data, investigators revived the initial hopes for the potential of genetics to inform drug development: drug targets with genetic evidence for efficacy (i.e. the target gene is linked to a variant significantly associated with the drug indication, cf Illustration) are twice as likely to succeed in a clinical trial.  However, challenges persist in this field. For example, most existing genetic studies focused only on European ancestry with documented effects: Polygenic Risk Scores fitted – scores calculating a person phenotype as the sum of the genetic effects of its variants across the genome – on European data have decreased performance in other populations. Additionaly, in existing studies linking genetic effects to drug response, we can distinguish two separate types of predictions: 1) efficacy of the drug target, or 2) the safety of the drug target. However, both safety and efficacy are required to obtain an approval.

The Project: In this project, the selected applicant and the hosting team will leverage genetic informationto guide the selection of drug targets. We will build upon the state-of-the-art by addressing key areas lacking investigations through the following steps: 

The MooreForAll project involves collecting and curating a large volume of data. The development of the predictive algorithm will require a good understanding of machine learning evaluation and interpretation techniques. Through this project, the applicant will be able to develop their knowledge in genetics, gene regulation, and epigenetics. The project will involve communicating and learning from clinician researchers who are collaborators of the hosting teams.

QUALIFICATIONS

The selected candidate should have a quantitative background with statistical and computational skills. they should be qualified with at least one programming language (e.g. Python, R). The candidate will typically hold a Ph.D in Statistics/Biostatistics, Epidemiology, Bioinformatics, Computer Science or other relevant disciplines with a quantitative research background. Practical experience working with genetic data sets, and developing statistical methods are desirable, but not required. We are a team committed to foster a fair, inclusive and diverse work environment. Diversity has been scientifically established as a key factor to improve scientific objectivity. Hence, all applicants will be evaluated solely based on qualification regardless of gender, gender identity, sexual orientation, race or disability.

THE INSTITUT PASTEUR

The Institut Pasteur is an internationally renowned center for biomedical research that stands out in many disciplines. The campus, founded in 1887 by Louis Pasteur and located in the center of Paris, hosts over 1,300 researchers and 300 doctoral students from over 60 nationalities. It is a creative and inspiring environment full of expertise and opportunities to connect and learn. The department of Computational Biology hosts multiple teams and a biostatistics/bioinformatics hub which includes over 50 PhD engineers specialized in computational biology.

PRACTICAL INFORMATION

Candidates should send their curriculum vitae, a cover letter detailing research experience, and contact information for two references or more to Dr. Hanna Julienne ([email protected] ) and Dr. Hugues Aschard ([email protected] ).

More information on the Institut Pasteur and the Statistical Genetics unit can be found here http://www.pasteur.fr/en and here https://research.pasteur.fr/en/team/statistical-genetics/ .

Applicants may start in March 2024 and applications will be considered through the end of June 2024. This postdoctoral position is funded for three years.