Hugues Aschard

JASS is a Python package that enables joint analysis of GWAS summary statistics to detect new genetic variants, with a web interface and efficient algorithms for large-scale analysis.

The paper analyzes gene-environment interaction effects across multiple ancestries and traits, identifying population-specific genetic mechanisms and methodological biases in GxE studies.

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.

The paper uses admixture mapping to identify smoking-dependent loci affecting lung function in African Americans, finding two genome-wide significant loci associated with ancestry-smoking interactions.

The paper improves polygenic score prediction by optimizing multiple hyperparameters in Clumping and Thresholding and introduces Stacked Clumping and Thresholding (SCT) for better performance.

This study identifies 588 SNP-metabolite associations using the METSIM cohort, revealing pleiotropy, temporal changes, and age-related genetic control shifts.

RAISS improves the imputation of genetic variants from GWAS summary statistics, achieving high accuracy for small effect size variants suitable for multi-trait analyses.

This paper introduces an efficient penalized logistic regression method for genetic risk prediction, showing improved performance over existing methods like C+T and random forests.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.

The paper identifies a bias in using principal components (PCs) for confounder correction in genomic studies, showing that it can lead to false positives, especially for large genetic effects.

The paper identifies ancestry-related DNA methylation differences and their causal impact on immune gene regulation using monocytes and Bayesian analysis.

VarExp estimates phenotypic variance explained by genetic variants using GWAS summary statistics without individual-level data.

The paper introduces two R packages, bigstatsr and bigsnpr, for efficiently managing and analyzing large-scale genomic data within a single framework.

The paper introduces lme4qtl, an R package extending lme4 to enable flexible covariance structures and parameter constraints for QTL mapping in genetic studies of related individuals.

This paper develops a statistical method using genetic ancestry to detect gene-gene and gene-environment interactions in admixed populations, identifying significant interactions in methylation data.