Jean‐Christophe Grenier

This paper shows PHATE can better capture continuous population structure in genomic data than PCA or UMAP by tuning scale and balancing populations.

This paper develops a deep learning framework for genomic ancestry prediction that is generalizable across diverse populations and interpretable using attribution techniques.

The paper models HLA diversity and viral evolution to assess T cell escape risk across populations, finding that escape mutations gain fitness with additional mutations.

This study shows that CETP isoforms influence human traits, particularly in pituitary and thyroid glands, and reveals tissue-specific CVD associations and epistatic interactions involving ADCY9.

The paper constructs a genetic reference panel for the Quebec population using whole-genome sequencing and GWAS to improve phenotype-genotype association discovery.

The paper identifies cell-type-specific enhancer-gene interactions in cardiac cells and maps disease-associated SNPs to enhancers in endothelial cells.

The paper identifies enriched coding variants in a French-Canadian founder population and explores their association with inflammatory bowel diseases.

The paper develops a workflow to improve the detection of intra-host SARS-CoV-2 mutations by reducing sequencing artifacts and batch effects using large-scale NGS data.

This study identifies co-evolution and co-regulation in CYP3A and CYP4F genes across human populations, linking genetic diversity to drug metabolism and health outcomes.

The paper addresses statistical concerns about detecting natural selection during the Black Death using ancient genomic data, reaffirming findings related to immune loci and the ERAP2 variant.

This study examines how sex and BMI modify the effect of genetically predicted lower CETP levels on lipid biomarkers and cardiovascular outcomes using observational genetic data.

The paper computes local feature attributions for t-SNE using gradient-based methods and validates them on synthetic and biological datasets.

This study shows that immunosuppressed patients with hematologic cancers develop viral populations with immune escape mutations, suggesting the need for combination therapies.

The paper identifies immune evasion mechanisms in SARS-CoV-2 by mapping epitopes and analyzing viral genome sequences to understand evolutionary pressures and immune responses.

The paper identifies a sex-specific evolutionary interaction between ADCY9 and CETP variants associated with dalcetrapib response and cardiovascular traits.

The paper presents computational tools for analyzing SARS-CoV-2 genetic data, showing that haplotype networks and unsupervised learning can reveal lineage emergence and population dynamics.

ImputeCoVNet is a 2D ResNet Autoencoder for imputing missing SARS-CoV-2 variants, achieving accurate results at low allele frequencies with faster computation than Hamming distance methods.

The paper investigates how early-life adversity affects epigenetic layers and gene expression in the amygdala, finding associations with immune and small GTPase dysregulation.

The paper uses single-cell RNA-sequencing to show genetic ancestry affects gene expression during viral infection, with effects varying by cell type.

The paper introduces Multiscale PHATE, a method for exploring high-dimensional biomedical data, and applies it to identify immune cell signatures associated with severe COVID-19 outcomes.

The paper shows that gene expression changes precede DNA demethylation in human dendritic cells during infection with Mycobacterium tuberculosis.

The paper investigates how environmental factors, particularly air pollution, interact with genetic variation to influence gene expression and chronic disease traits in an urban population.