Hugues Aschard

Gene-by-Sleep Duration Interaction for Glycemic Traits in over 480,000 Individuals

The paper identifies 16 genetic loci interacting with sleep duration to influence glycemic traits in over 480,000 individuals, revealing distinct biological mechanisms for short and long sleep.

Identification of pleiotropic genes and biological mechanisms underlying the shared genetic component between asthma, neuropsychiatric disorders, and substance use behaviors

The paper identifies pleiotropic genes and mechanisms linking asthma, neuropsychiatric disorders, and substance use behaviors through multi-trait GWAS and single-cell transcriptomics.

Identification and Functional Characterization of Regulatory Variants in DPP9 Associated with COVID-19 Severity

The paper identifies and functionally characterizes regulatory variants in DPP9 associated with severe COVID-19 by analyzing genetic risk factors and their impact on gene expression and immune cell function.

Evolution of the genetic architecture of uterine disorders

This study identifies shared genetic variants and pathways across ten uterine disorders, revealing evolutionary influences on disease risk.

Taxonomic-free metagenome GWAS to identify gut microbiome functions influencing host phenotypes

The paper develops a taxonomy-free GWAS method using k-mers to analyze gut microbiome associations with host phenotypes, identifying significant links to seven traits in a cohort of healthy participants.

Polygenic risk score prediction accuracy convergence

This study assesses the progress and limitations of polygenic risk score prediction accuracy for common diseases, showing diminishing returns from recent GWAS and highlighting the need for broader variant coverage.

Empowering multitrait brain phenotypes GWAS

This study evaluates multitrait GWAS for brain MRI phenotypes, showing predictable gains over univariate GWAS and proposing data-driven strategies for phenotype selection.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

Pitfalls in performing genome-wide association studies on ratio traits

This paper shows that genome-wide association studies on ratio traits can be misleading, as associations may be driven by the denominator rather than the numerator, and proposes a method to correct for collider bias.

Refinement of a published gene-physical activity interaction impacting HDL-cholesterol: role of sex and lipoprotein subfractions

The paper refines a gene-physical activity interaction affecting HDL-cholesterol by analyzing lipoprotein subfractions and exploring sex differences across multiple cohorts.

A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances

This study identifies 17 genetic loci associated with sleep duration and lipid levels, offering potential druggable targets for dyslipidemia in individuals with sleep disturbances.

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

This study evaluates trait selection strategies in multi-trait GWAS, finding that diverse trait sets improve variant detection compared to univariate screening and specific methods like JASS outperform others.

Multi-trait GWAS for diverse ancestries: Mapping the knowledge gap

This paper applies a multi-trait GWAS approach to non-European ancestries, identifying new associations and demonstrating increased replication of European loci.

Linking the genetic structure of neuroanatomical phenotypes with psychiatric disorders

The paper uses multitrait GWAS to identify genetic links between MRI brain volume phenotypes and psychiatric disorders, finding significant associations with ADHD and schizophrenia.

A multivariate outcome test of covariance

The paper introduces MANOCCA, a method to test the effect of predictors on the covariance matrix of multivariate outcomes, showing it outperforms existing methods in simulations and identifies significant associations in real data.

Quantile Regression for biomarkers in the UK Biobank

This paper applies quantile regression to GWAS in the UK Biobank to identify significant loci for quantitative traits, finding 259 loci only detected by quantile regression.

ChoruMM: a versatile multi-components mixed model for bacterial-GWAS

The paper develops ChoruMM, a multi-components mixed model for bacterial GWAS, showing that standard human genetics models are inadequate for highly structured bacterial genomes.

Enhancer/gene relationships: need for more reliable genome-wide reference sets

The paper evaluates two functional link methods for identifying enhancer/gene relationships using benchmark data and finds neither performs best, highlighting the need for reliable genome-wide reference data.

The Immune Factors Driving DNA Methylation Variation in Human Blood

This study identifies factors influencing DNA methylation variation in human blood, showing that cytomegalovirus infection and aging drive methylation changes, with cellular composition and DNA sequence as key predictors.

Multi-PGS enhances polygenic prediction: weighting 937 polygenic scores

This paper introduces a framework to enhance polygenic prediction by combining multiple polygenic scores, showing significant accuracy improvements for psychiatric disorders and other traits.

Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores

The paper identifies misspecifications in GWAS summary statistics and shows how to correct them to improve polygenic score predictive performance.

MGMM: An R Package for fitting Gaussian Mixture Models on Incomplete Data

MGMM fits Gaussian mixture models on incomplete data, outperforming imputation methods in recovering cluster assignments and assessing uncertainty.

Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes

The paper identifies novel genetic and non-genetic factors associated with plasma protein levels in healthy donors, highlighting their impact on immune response variability.

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

This paper introduces a gene-based testing framework that improves power and reduces false positives by integrating chromatin interactions and knockoff genotypes, showing better performance in GWAS and whole-genome sequencing data.

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

Estimating the effective sample size in association studies of quantitative traits

This paper introduces an analytical method to estimate effective sample size in mixed-model GWAS of quantitative traits, showing its performance in unrelated and related individuals.

Mapping the human genetic architecture of COVID-19 by worldwide meta-analysis

This study identifies 13 genome-wide significant loci associated with SARS-CoV-2 infection or severe COVID-19 through a global meta-analysis of 49,562 patients.

Fitting penalized regressions on very large genetic data using snpnet and bigstatsr

This paper benchmarks the performance of two R packages, snpnet and bigstatsr, for fitting penalized regressions on large genetic data, finding bigstatsr significantly faster.

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

The paper decomposes multitrait genetic associations to identify pleiotropy profiles linking SNPs to biological pathways and drug targets.

Deriving stratified effects from joint models investigating Gene-Environment Interactions

This paper develops a method to estimate stratified and marginal summary statistics from a joint gene-environment interaction model, demonstrating its accuracy through simulations and real data applications.