Wednesday 24th November 2021  14.00-15.30 CET

Celia Greenwood

Senior Investigator

Lady Davis Institute for Medical Research, and McGill University

GWAS, polygenic scores, and beyond


 This lecture will discuss general concepts of genetic architecture, and review the basics of genome-wide association studies and polygenic risk score (PRS) analysis.  I will discuss some of the challenges and opportunities when building and interpreting PRS. For example, I will show how PRS can perform differently in subgroups of individuals, and touch on the challenges associated with translating PRS across populations of different ancestry. Then, I will show how PRS can prioritize individuals for sequencing to find rare pathogenic variants. Finally, we can discuss other potential opportunities for using PRS.

Key references:

Timpson et al. (2018) Genetic architecture: the shape of the genetic contribution to human traits and disease. Nature Reviews Genetics 19(2): 110-124. doi: 10.1038/nrg.2017.101

Lu et al. (2021) Individuals with common diseases, but with a low polygenic risk score could be prioritized for rare variant screening. Genetics in Medicine 23(3):508-515. doi: 10.1038/s41436-020-01007-7


Dr. Celia Greenwood is Senior Investigator at the Lady Davis Institute for Medical Research in Montreal, QC, Canada, and James McGill Professor at McGill University. She is a statistician whose research domain is development and application of statistical methodology for analysis of genetic and genomic data.  Recent methodological work focuses on dimension reduction and prediction with high dimensional data, analysis of DNA methylation data, and data integration. Applications of the methods have been used to improve understanding of multiple phenotypes and diseases, including osteoporosis, rheumatic diseases, cognitive ability, and cancer. She obtained her Ph.D. from the University of Toronto in Biostatistics, and then held a postdoctoral fellowship at McGill University in Human Genetics. She was the 2019 President of the International Genetic Epidemiology Society. Google scholar ; Website ; Github ; Twitter

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