Hanna Julienne

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

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

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.

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

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.

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

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

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.

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

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.

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

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.

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.