Research Associate -- Statistical Pain Genomics

McGill University
Montreal, QC, Canada
Job Type:
  • Staff (Up to 12 months; renewable)
Degree Level Required:
Masters or PhD
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Research Associate -- Statistical Pain Genomics

Professors Luda Diatchenko and Audrey Grant are jointly hiring a Research Assistant or Research Associate (depending on the qualifications of the candidate) in the Faculty of Dentistry at McGill University. This research opportunity is focused on applying statistical genomics approaches directed towards chronic pain development, conditions featuring chronic pain, and pain sensitivity. Chronic pain is defined based on the persistence of pain experience for over three months of time and represents a substantial public health burden with a prevalence of 20 % in the general population. Although the genetic basis of various pain conditions (including fibromyalgia, migraine, lower back pain) is not well understood, the development of chronic pain implicates both the nervous system and immunity, and may be related to psychiatric outcomes.

The overall goal of our joint research interests is to identify the molecular basis for the transition from acute to chronic pain, body site specificity vs. widespreadness across pain conditions, and pain sensitivity. Methodology employed includes classical and emerging statistical genomics analysis techniques. The successful candidate will be in charge of analyses that span across these areas.


The candidate will conduct statistical analyses using existing and emerging statistical methods. The analytical tasks may involve:

● Processing UK Biobank and Canadian Longitudinal Study on Aging data and calculating the summary statistics of pain phenotypes (taking into account population admixture and various confounding covariates); ● Statistical enrichment analysis of gene sets, pathways, cell/tissue-specific transcriptomes and epigenomes (harnessing reference single-cell and bulk public data); ● Inferring mediating phenotypes of pain phenotypes within hierarchical polygenic models; ● Statistical fine-mapping of pleiotropic sub-threshold functional genetic variants.


We seek a Statistical Genomics Analyst trained in the statistical or computational sciences with a graduate degree. The ideal candidate should display a passion for statistical and computational approaches applied to complex traits such as pain, and have the following skills:

● Strong statistical genetics, computational biology or machine learning background; ● Solid experience in at least one of the following programming languages or environments: R, Python, SAS, STATA; ● Experience using PLINK or other specialized software for processing genomic data; ● Familiarity with Linux and shell scripts.

Additional Information

To find out more about the research environment, please visit the Diatchenko webpage at: