Chiara Auwerx

PESTO classifies gene-phenotype associations as Novel, Hypothesized, Existing, or Established using a large language model and structured genetic evidence, identifying 7% as Novel and 21% as Hypothesized in 940 associations.

The paper compares the phenotypic and genetic structures of psychiatric symptoms in autistic and non-autistic children, finding differences in factor structures and genetic associations.

This study identifies three functional programs impacted by autism genes, contextualized across neuronal subclasses, developmental timing, and cortical regions.

Ultra-rare damaging coding variants in 87,959 schizophrenia cases and 150,587 controls implicate 40 genes (16 exome-wide, 24 at 5% FDR), half overlapping autism, developmental delay or bipolar genes.

ALLSPICE identifies heterogeneous rare variant effects across traits in gene-trait associations using summary statistics from the UK Biobank.

The paper estimates the contribution of de novo coding mutations to autism using data from 38,680 trios, finding they explain 3.4% of autism variance.

The paper identifies 253 autism-associated genes using rare variation from 62,429 individuals and finds pleiotropy across physiological systems and developmental disorders.

The paper investigates BLTP1's role in neocortical lamination by analyzing patients and mouse models, finding lipid transport defects cause structural brain abnormalities.

The paper identifies WAPL as a novel cohesinopathy gene and driver of a genomic disorder, showing its role in developmental delay and other anomalies through clinical, in vitro, and in vivo analyses.

The paper investigates how polygenic scores and copy-number variants interact, finding that assortative mating explains their positive correlation and contributes to genetic risk compounding.

The paper uses EHRs and biobanks to identify pharmacogenetic associations with drug response in cardiometabolic diseases, finding novel genetic predictors for personalized treatment.

The paper identifies pleiotropic effects of 16p11.2 BP4-5 CNVs on 46 phenotypes, showing BMI-dependent and indirect associations through mediation and Mendelian randomization.

This study investigates the relationship between telomere length and health outcomes using UK Biobank data, identifying factors that influence telomere attrition and its effects on disease risk and lifespan.

This study identifies 73 CNV-related disease signals in 331,522 UK Biobank participants, showing CNVs increase disease risk and earlier onset, with polygenic effects and gene dosage impacts.

The paper compares gene-prioritization methods for identifying drug targets across 30 traits, finding GWAS performs best with an AUC of 54.3%.

This study investigates the phenotypic effects of 22q11.2 copy number variants in the general population using UK Biobank data, finding associations with BMI, platelet volume, and other traits.

The paper develops a multi-omics Mendelian randomization framework to identify transcript-metabolite-trait causal triplets and shows it outperforms classical transcriptome-wide MR by integrating multiple omics layers.

The paper quantifies mediation between DNA methylation, transcriptome, and proteome layers and their impact on complex traits using multivariable Mendelian randomization.

The paper introduces an omics-informed quality score to improve CNV detection and association analysis by reducing false positives and increasing statistical power.

This study identifies 131 CNV signals associated with 47 complex traits in the UK Biobank, revealing new gene functions and linking CNVs to decreased longevity and health outcomes.

This paper introduces revTWMR, a method to determine causal relationships between gene expression and complex traits, finding that traits more strongly influence gene expression than vice versa.