Current projects

Drug target genetics

The main goal of my thesis was to use genetic variants in the genes encoding drug targets to predict the effect of drugs. I am still actively working in this area and my focus is on targets of blood lipid drugs. For example, I am studying lipoprotein(a), a circulating lipoprotein that causes early heart attacks in individuals with elevated levels. Interestingly, the levels of lipoprotein(a) are mostly genetically determined which makes models based on naturally occurring genetic variants particularly appropriate.

To answer important questions about drug target genetics, I am developing machine learning models of drug target activity based on genetic variants and I use causal inference methods to derive clinically-relevant insights.

Past projects

Genetic models of CETP inhibitors

The CETP is an enzyme responsible for exchanges of cholesteryl esters between different lipoproteins. Its net effect is to increase the cholesterol carried by high density lipoproteins (with many other more subtle effects), and it plays a role in a mechanism involved in atherosclerotic plaque metabolism. This enzyme is also important because it is a drug target for a widely studied and somewhat controversial class of drugs called CETP inhibitors developed to treat coronary artery disease.

I worked on two projects aimed at better understanding the effect of a genetically predicted reduction in CETP levels:

Study of effect modifiers of genetically predicted CETP reduction. Legault, M.-A., Barhdadi, A., Gamache, I. Lemaçon, A., Lemieux Perreault L.-P., Grenier, J.-C., Sylvestre, M.-P., Hussin, G. J., Rhainds, D., Tardif, J.-C., Dubé, M.-P. (2021). Under review, https://doi.org/10.1101/2021.09.09.21263362
A sex-specific evolutionary interaction between ADCY9 and CETP. Gamache, I., Legault, M.-A., Grenier, J.-C., Sanchez, R., Rhéaume, E., Asgari, S., Barhdadi, A., Zada, Y. F., Trochet, H., Luo, Y., Lecca, L., Murray, M., Raychaudhuri, S., Tardif, J.-C., Dubé, M.-P., & Hussin, J. (2021). eLife, 10. https://doi.org/10.7554/eLife.69198

Human genetics for drug target validation, the case of ivabradine

Ivabradine is a drug that inhibits an ion channel at the sinoatrial node in the heart called the HCN4. This channel is responsible for the spontaneous depolarization of the heart that results in its autonomous contraction. Inhibiting this channel reduces the heart rate which was expected to be beneficial in various medical conditions where the heart's energy balance is disturbed. For example, ivabradine is beneficial for the treatment of heart failure in individuals with elevated heart rate and also alleviates anginal symptoms in patients with stable angina. However, it did not protect against coronary events in patients with stable coronary artery disease without heart failure.

To see if a genetic approach could have predicted ivabradine's efficacy and safety profile, we compared a genetic model of ivabradine treatment based on genetic variants in the HCN4 gene to the results from randomized controlled trials. After accounting for comorbidities using a competing risk survival analysis model, our results recapitulated the main observations from clinical trials. We also used a Mendelian Randomization approach to further assess the effect of a heart rate reduction on heart failure an coronary artery disease outcomes.

The paper for this project is now published:

A genetic model of ivabradine recapitulates results from randomized clinical trials. Legault, M.-A., Sandoval, J., Provost, S., Barhdadi, A., Lemieux Perreault, L.-P., Shah, S., Lumbers, R. T., de Denus, S., Tyl, B., Tardif, J.-C., & Dubé, M.-P. (2020). PloS One, 15(7), e0236193. https://doi.org/10.1371/journal.pone.0236193

The ExPheWas browser

The ExPheWas browser allows users to browse the results of a gene-based PheWAS analysis in the UK Biobank. More than 23 million statistical association tests between protein coding genes and various phenotypes are presented. Data visualization tools and enrichment analyses are also available allowing, for example, to find drug classes that seem to share a biological basis with the phenotype. Concretely, this data visualization displays results for an enrichment analysis between disease associated genes and drug target genes from the ChEMBL database. There are many more features and interesting results which are more deeply covered in the preprint:

ExPheWas: a browser for gene-based pheWAS associations. Legault, M.-A., Lemieux Perreault, L.-P., & Dubé, M.-P. (2021). medRxiv. https://doi.org/10.1101/2021.03.17.21253824