Portrait of Marc-André Legault

Marc-André Legault

Associate Academic Member
marc-andre.legault@mila.quebec
Assistant Professor, Université de Montréal, Pharmacogenetics
Research Topics
Causality
Computational Biology
Medical Machine Learning
Google Scholar

Biography

Marc-André Legault obtained his Ph.D. in bioinformatics from Université de Montréal and the Montreal Heart Institute where he developed and applied new computational methods for drug target validation. He subsequently completed his postdoctoral training at McGill University and Mila - Quebec Artificial Intelligence Institute, working on instrumental variable estimation and machine learning for genetic epidemiology more broadly.

He is now an Assistant Professor in pharmacogenetics at the Université de Montréal's Faculty of pharmacy and a researcher at the CHU Sainte-Justine Azrieli research centre. His research program aims to develop and use computational approaches for drug target validation to better understand treatment heterogeneity and improve our ability to anticipate the on-target effect of new drug classes. He is also an Associate Academic member at Mila - Quebec Institute of Artificial Intelligence.

Current Students

Isabela Morita
Master's Research - Université de Montréal
isabela.morita@mila.quebec
LinkedIn
Amine Obeid
PhD - Université de Montréal
amine.obeid@mila.quebec
Github
Shu Ai Song
Master's Research - Université de Montréal
shu.ai-song@mila.quebec
Julien St-Pierre
Postdoctorate - Université de Montréal
julien.st-pierre@mila.quebec
Github
Google Scholar

Publications

Revisiting the genetic support of leukotriene pathway drug targets in atherosclerotic cardiovascular disease
Tristan St-Laurent
Julien St.-Pierre
Julien Quang Le Van
Marc‐André Legault
Arachidonate 5-lipoxygenase (5-LO) and its activating protein, encoded by ALOX5 and ALOX5AP respectively, are two leukotriene pathway protei… (see more)ns suspected to play a role in atherosclerotic cardiovascular disease (ASCVD) through inflammatory mechanisms. Prior genetic association studies linking ALOX5 and ALOX5AP to ASCVD have motivated clinical trials of 5-LO inhibitors, such as atreleuton, which was associated with reduced atherosclerotic plaque progression in patients with a recent acute coronary syndrome in a phase II trial. However, the effect of 5-LO inhibition on ASCVD clinical outcomes remains unclear. Using data from the UK Biobank, we revisit previously reported genetic associations of ALOX5 and ALOX5AP with ASCVD using a multi-ancestry meta-analysis including data from 378,962 participants. We characterize ancestral differences in previously studied ALOX5 and ALOX5AP polymorphisms which could have led to population stratification. When adequately accounting for ancestry, these previous associations were not replicated. In addition to our replication efforts, we used association scans and transcriptome-wide association studies to test the involvement of leukotriene pathway genes with ASCVD. Overall, our results do not support a role for genetic variation in ALOX5 or its activating protein in ASCVD risk.
2026-10-13
Frontiers in Genetics (published)
doi.org
BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning
Iñigo Clemente‐Larramendi
Jessica Gliozzo
Lourdes Velo‐Suárez
Ozvan Bocher
Nathan Foulquier
Maria Hernandez-Valladares
Marc‐André Legault
Divi Cornec
Christophe Jamin
Alberto Gil-de-la-Fuente
Álvaro Fernández‐Ochoa
Cristian Iperi
Abstract Background Multi-omics integration has emerged as a powerful strategy to decode the molecular complexity of biological systems. How… (see more)ever, the diversity of available methods, each designed with distinct assumptions, objectives, and computational requirements, makes method selection, usage and interpretation challenging for non-expert users. Here we present BiomiX 3.0, an updated version of the BiomiX platform that extends its integration capabilities with four additional methods: Similarity Network Fusion (SNF), NEighborhood-based Multi-Omics clustering (NEMO), Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), and PRAMIGO (Phenotyping netwoRk Application for Multi-omics InteGratiOn), a novel supervised heterogeneous graph transformer (HGT) introduced in this work. Results We benchmarked all five methods, MOFA, DIABLO, SNF, NEMO, and PRAMIGO, on two independent multi-omics datasets derived from a Chronic Lymphocytic Leukemia (CLL) cohort comparing IGHV-mutated and unmutated patients, and a pulmonary tuberculosis (PTB) cohort versus healthy controls. Supervised methods (DIABLO, PRAMIGO) consistently achieved higher condition-specific discrimination as measured by the Adjusted Rand Index (ARI) and the Adjusted Mutual Information (AMI). In contrast, unsupervised methods (SNF, NEMO) revealed alternative patient stratifications driven by independent sources of biological variance while MOFA performed in a semi-supervised way occupies an intermediate position, capturing latent factors that explain both disease-associated and orthogonal sources of variance. Systematic gene-centric analysis of the top-ranked features prioritized by each method was supported by manual biological annotation of shared and method-specific signals. Across cohorts, we annotated 154 shared features (116 genes, 38 metabolites) and 60 method-unique features per cohort, demonstrating that no single integration strategy captures the full landscape of biologically relevant signals. In the CLL cohort, shared features spanned B-cell receptor biology, innate immune signaling, RAS/MAPK activation, and epigenetic regulation, while method-unique features revealed supervised-method-specific insights into vesicle trafficking (DIABLO), immune checkpoints (MOFA), and ncRNA regulation (PRAMIGO). In the PTB cohort, a convergent interferon/innate immune signature dominated shared features across all methods. Still, method-unique analysis uncovered DIABLO-specific acylcarnitine metabolic reprogramming, MOFA-specific restoration of lysosomal trafficking, and PRAMIGO-specific γδ T-cell and immunoglobulin repertoire diversity. Across both cohorts, SNF and NEMO proved useful for detecting biological and technical sources of variation that were orthogonal to the primary condition of interest. Ultimately, PRAMIGO uniquely enables the construction of heterogeneous graphs modeling cross-modal molecular interactions, uncovering epigenetic co-regulation programs in CLL and multi-omics inflammatory modules in PTB that are difficult to identify using conventional integration approaches. Conclusions BiomiX 3.0 provides a graphical user interface (GUI) multi-method integration environment that democratizes access to state-of-the-art multi-omics analysis. By combining both unsupervised and supervised integration strategies within a unified platform and introducing graph-based learning through PRAMIGO, BiomiX 3.0 enables researchers across disciplines with complementary tools to interrogate the biological sources of variation in their data, without requiring bioinformatics expertise.
2026-09-27
bioRxiv (preprint)
doi.org
Rare and Common Germline and Somatic Variants Shape Immune Cytopenia Risk and Enable Risk Stratification
Stennio Da Silva Faria
Julie Bineau
Rafael Moisan
Marc‐André Legault
Estelle Lecluze
Thomas Pincez
Abstract The genetic risk factors of immune cytopenias are unclear. Immune cytopenias have been reported in various genetic contexts: 1) inh… (see more)erited error of immunity genes, mainly due to rare germline variants, 2) systemic lupus erythematosus, associated with common germline variants, 3) hematological malignancies, and 4) clonal hematopoiesis, the latter two due to somatic variants. However, the respective contribution and interaction of these variants remain to be investigated. Here, we used two large biobanks with whole genome sequencing data to systematically investigate the genetic contribution to immune cytopenia. We found that the four types of genetic variants independently contribute to immune cytopenia risk. We notably found that carriers of variants in some autosomal recessive genes of inherited error of immunity had an increased risk of immune cytopenia. Additionally, common variant- mediated risk of systemic lupus erythematosus also increased the risk of immune cytopenia. Overall, a third to a half of patients with immune cytopenia carried at least one of the four genetic risk variants investigated. Combining the four variants allowed stratifying the risk of immune cytopenia in both general and high-risk population. In general population, the 10- year incidence of immune cytopenia in the lowest and highest risk groups was 0.8‰ and 15‰, respectively. In sum, this work identified that different genetic risk factors can lead to immune cytopenia. A large proportion of individuals with immune cytopenia carried an underlying genetic risk factor. Finally, combining these genetic risk factors enabled risk stratification.
2026-08-30
medRxiv (accepted)
doi.org
Beyond Go/No-Go Decisions: A Regional Selection Framework for Uncertainty-Aware Molecule Screening
Weihua Shi
Yixuan Li
Tian Bai
Yijie Zhang
Kaiqiong Zhao
Marc‐André Legault
Hui Peng
Yue Zhao
Eric D. Kolaczyk
Xiang Yu
Archer Y. Yang
In drug discovery, quantitative structure–activity relationship (QSAR) models are widely used to guide Go/No-Go decisions within the Desig… (see more)n–Make–Test–Analyze (DMTA) cycle. However, conventional decision heuristics typically rely on a single cutoff, leading to a rigid binary select/discard paradigm. This approach is particularly ill-suited for borderline compounds near the decision boundary, where screening decisions are especially sensitive to prediction uncertainty and premature choices may either discard viable leads or advance likely failures, thereby increasing downstream assay costs. To address this limitation, we propose Regional Selection (RS), an uncertainty-aware three-way decision framework that partitions compounds into Predicted Pass, Predicted Fail, and Predicted Indeterminate regions. By explicitly reserving high-uncertainty compounds for targeted follow-up, RS avoids the pitfalls of premature binary classification. We formalize this framework through Regional Selection Inference (RSI), which casts region assignment as a multiple-hypothesis testing problem. We develop two imple- mentations of RSI: an empirical calibration-based method (RSI-EC), which thresholds uncertainty-normalized scores via empirical calibration, and a conformal selectionbased method (RSI-CS), which constructs conformal p-values for region assignment. RSI-EC is supported by large-sample calibration arguments, whereas RSI-CS provides finite-sample, distribution-free guarantees under exchangeability. Extensive evaluations across 15 high-dimensional QSAR benchmarks show that both RSI procedures reliably control the false discovery rate while maintaining high screening power. In limited-data regimes, RSI-CS yields particularly stable FDR control, whereas RSI-EC can be slightly less conservative; both perform strongly as sample sizes increase. We further study a cost-aware extension that incorporates asymmetric downstream costs through the score construction while keeping the nominal FDR target fixed. This extension introduces a tuning parameter that can reduce realized downstream cost, with dataset-dependent trade-offs against screening power. Overall, RSI offers a mathematically grounded and resource-aware alternative to single-threshold screening, allowing discovery teams to better balance decision confidence with assay budgets.
2026-05-27
ChemRxiv (accepted)
doi.org
Robust Mendelian Randomization Estimation using Weighted Quantile Regression
Julien St-Pierre
Archer Y. Yang
Mireille E. Schnitzer
Marc-André Legault
In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships betwe… (see more)en exposures and outcomes based on observational data. However, numerous genetic studies have shown the pervasive pleiotropy of genetic variants, meaning that many, if not most, variants are associated with multiple traits, potentially violating the core assumptions of IV estimation. Uncorrelated pleiotropy occurs when genetic variants have a direct effect on the outcome that is not mediated by the exposure, while correlated pleiotropy occurs when genetic variants affect the exposure and outcome via shared heritable confounders. In this work, we propose a novel MR method, called MR-Quantile, based on weighted quantile regression (WQR) that is robust to both correlated and uncorrelated pleiotropy. We propose a procedure for selecting the optimal quantile of the ratio estimates through a likelihood-based formulation of WQR using the asymmetric Laplace distribution. Monte Carlo simulations demonstrate the empirical performance of the proposed method, especially in settings with many invalid IVs with weak pleiotropic effects. Finally, we apply our method to study the causal effect of resting heart rate on atrial fibrillation. Genetic variants associated with heart rate were identified in a genome-wide association study of 425,748 individuals from the VA Million Veteran Program, and used as instruments in a two-sample MR analysis with summary statistics from a genetic meta-analysis of 228,926 AF cases across eight studies.
2026-04-07
arXiv (preprint)
doi.org
arxiv.org
Pharmacogenomic study of the effects of saxagliptin on glucose control and hypoglycemic events
Marc‐Olivier Pilon
Simon de Denus
Géraldine Asselin
Marc‐André Legault
Hervira Brenda Ngongang Kwandjang
Amina Barhdadi
Louis‐Philippe Lemieux Perreault
Diane Valois
Ian Mongrain
Carolina Haefliger
Dirk S. Paul
Ewan R. Pearson
Jean‐Claude Tardif
Marie‐Pierre Dubé
BACKGROUND: Saxagliptin is a DPP-4 inhibitor widely used to manage type 2 diabetes, though genetic contributors to variability in response r… (see more)emain unclear. This study aimed to identify genetic variants associated with the efficacy and safety of saxagliptin using genome-wide association studies (GWAS) with data from randomized controlled trials. METHODS: GWAS were conducted on glycated hemoglobin (HbA1c) levels and hypoglycemic events in saxagliptin-treated individuals from 12 phase II or III clinical trials. Additional exploratory traits included fasting glucose, weight, body mass index, systolic and diastolic blood pressure, heartbeat, and renal function. Primary analyses focused on participants of European ancestry, followed by analyses in the overall multi-ancestry cohort. Linear mixed models for continuous traits and Cox proportional hazards models for time-to-event analyses were used, including gene-by-sex and gene-by-treatment interactions. We also performed a transcriptome-wide association study (TWAS) to identify genes whose predicted expression is associated with the response to saxagliptin using the S-PrediXcan framework. RESULTS: Among 1 016 European and 1 826 multi-ancestry saxagliptin-treated participants, no genome-wide significant associations were observed. Suggestive associations included rs2168426:T > C near UBE2E1 for HbA1c (p = 1.0 × 10⁻⁷) and rs138558907:C > T near CCSER2 for hypoglycemia (p = 6.1 × 10⁻⁸). Treatment interaction analyses showed a stronger rs2168426 effect on HbA1c among saxagliptin users compared with non-users (pint = 9.2 × 10⁻³). The TWAS revealed three genes that significantly associated with HbA1c in human tissues: OGFOD3 in aorta (p = 8.3 × 10⁻⁷), TBC1D3D in coronary artery (p = 3.3 × 10⁻⁶), and TYSND1 in amygdala (p = 3.9 × 10⁻⁶). In the multi-ancestry cohort, KDM7A in whole blood was significantly associated with HbA1c (p = 3.6 × 10⁻⁶). CONCLUSION: Larger, diverse studies are needed to identify pharmacogenomic determinants of responses to saxagliptin.
2026-03-25
BMC Medical Genomics (published)
doi.org
CETP alternative splicing variation impacts human traits
Isabel Gamache
Marc‐André Legault
Jean‐Christophe Grenier
Éric Rhéaume
J.C. Tardif
Marie‐Pierre Dubé
Julie Hussin
Abstract The cholesteryl ester transfer protein (CETP) is an important protein in reverse cholesterol transport and has been identified as a… (see more) significant factor associated with cardiovascular disease (CVD), making it a widely studied pharmaceutical target. Three protein-coding isoforms of CETP exist, distinguished by the alternative splicing of one exon each. The isoform primarily responsible for cholesterol-related functions in the plasma is well studied, but specific functions of each isoform remain poorly understood. In this study, we demonstrate the significance of considering CETP’s isoforms in analyses of human traits. Using bulk RNA-seq data from multiple tissues, we characterized the expression patterns and genetic regulation determinants of CETP transcripts. Leveraging publicly available GWAS summary statistics, we conducted multivariable Mendelian Randomisation (MVMR) to estimate the impact of variation in isoform proportions on phenotypes, highlighting the importance of CETP’s isoforms in pituitary and thyroid glands. Furthermore, we uncovered tissue-specific associations between CETP’s isoforms and CVD-associated phenotypes. Additionally, we observed that the epistatic interaction previously reported between CETP and ADCY9 , a gene implicated in modulating a CETP modulator’s response, may be mediated through the regulation of alternative splicing of exon 9. Our results underscore the importance of a comprehensive understanding of CETP’s isoforms, which can significantly impact both fundamental and clinical research efforts.
2026-03-17
bioRxiv (preprint)
doi.org
Do machine learning methods make better predictions than conventional ones in pharmacoepidemiology? A systematic review, meta-analysis, and network meta-analysis.
Ana Paula Bruno Pena-Gralle
Mireille E. Schnitzer
Sofia-Nada Boureguaa
Félix Morin
Marc-André Legault
Caroline Sirois
Alice Dragomir
Lucie Blais
2025-12-31
Artif. Intell. Medicine (published)
doi.org
PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank
Ziqi Yang
Ziyang Song
Shadi Zabad
Marc-André Legault
Yue Li
Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of e… (see more)lectronic health records (EHR). We present MixEHR-SAGE, a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications to enhance phenotyping from large-scale EHRs. By combining expert-informed priors with probabilistic inference, MixEHR-SAGE identifies over 1000 interpretable phenotype topics from UK Biobank data. Applied to 350 000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predict incident type 2 diabetes (T2D) and leukemia diagnoses. Subsequent genome-wide association studies using these continuous risk scores uncovered novel disease-associated loci, including PPP1R15A for T2D and JMJD6/SRSF2 for leukemia, that were missed by traditional binary case definitions. These results highlight the potential of probabilistic phenotyping from multi-modal EHRs to improve genetic discovery. The MixEHR-SAGE software is publicly available at: https://github.com/li-lab-mcgill/MixEHR-SAGE.
2025-12-31
Briefings in Bioinformatics (published)
doi.org
Reply to comment on "medication-based mortality prediction in COPD using machine learning and conventional statistical methods".
Ana Paula Pena-Gralle
Amélie Forget
Yohann Moanahere Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-12-31
International Journal of Medical Informatics (published)
doi.org
Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.
Ana Paula Pena-Gralle
Amélie Forget
Yohann Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-11-04
International Journal of Medical Informatics (published)
doi.org
Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification
Sara El Aouhel
Vanessa Bellegarde
Stennio Da
Silva Faria
Tristan St-Laurent
Estelle Lecluze
Anne-Laure Pham Hung d’Alexandry d’Orengiani
F. Galactéros
Pablo Bartolucci
Marc-André Legault
Guillaume Lettre
Thomas Pincez
2025-10-01
medRxiv (preprint)
doi.org