Professors Carolina Meloto and Audrey Grant are hiring one PhD student at the Faculty of Dental Medicine and Oral Health Sciencesat McGill University. This research opportunity is focused on applied approaches directed towards prevention of chronic low back pain (cLBP) development. Broadly, chronic pain is defined based on the persistence of pain experience for over three months and represents a substantial public health burden with a prevalence of 20 % in the general population, with cLBP as the most common chronic pain condition. Accurately predicting individuals who are at risk of cLBP is a vital step needed to enable cLBP prevention strategies. Despite cLBP having a sizable genetic heritability, models proposed to predict cLBP development are based on biopsychosocial measures and do not incorporate genetic variability. Here, we will capitalize on large scale biobanks available to our teams to derive and assess the performance (discrimination, calibration, and accuracy) of a polygenic risk score (PRS) that predicts cLBP development. We plan to use cutting edge methodology and new data resources to maximize predictive performance of the PRS. 

Responsibilities:

Design and execute research project in the area of cLBP PRS development and optimization.

Through supervision and advice given by Profs. Meloto and Grant, the student will develop this project using resources including extensive clinical and genomic data from the:

UK Biobank

Canadian Longitudinal Study on Aging

All of US

CARTaGENE

and others,

participate in discussions, planning, execution, and evaluation of projects together with other members of Prof Meloto and Grant’slaboratories, report research results and developments at laboratory meetings, scientific conferences, and in peer-reviewed scientific publications.

We seek a PhD student to start as of January, 2025 or May, 2025. The ideal candidate should display an interest in statistical and computational approaches applied to complex traits such as pain, have completed or be in the final stages of a relevant Bachelor’s,Master’s or DMD program, and have the following:

Strong quantitative sciences/statistics/mathematics background;

Passion for the potential of technological innovation applications in the health or life sciences;

Some familiarity with at least one programming language or environment such as: R, Python, perl;

Fluent spoken and written English or French.