Laura M. Huckins

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

The paper estimates genetic correlations between eating disorder and substance-use-related phenotypes using GWAS data, finding both positive and negative associations.

This study evaluates polygenic risk scores for schizophrenia in electronic health records, finding strong associations with schizophrenia and pleiotropic effects on other psychiatric and medical conditions.

The paper uses pleiotropic meta-analysis to identify genetic loci associated with cognition, education, and schizophrenia, revealing distinct biological mechanisms for concordant and discordant genetic correlations.

This study identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.

The paper identifies SNRNP35 as a trauma type-specific PTSD gene through analysis of genetically regulated gene expression in PTSD cases and controls.

This paper identifies a genome-wide significant risk locus for cannabis use disorder, implicating CHRNA2 and linking it to cognitive performance.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.

The paper integrates GWAS and conditional eQTL data to identify schizophrenia risk loci with co-localization, finding 40 loci and six with conditional eQTL co-localization.