Naomi R. Wray

The paper identifies a mid-gestational molecular mechanism linking cortical size and ADHD through genetic and single-cell RNA sequencing analyses.

The paper investigates polygenic and familial factors influencing antidepressant treatment outcomes in two cohorts, finding associations between depression polygenic scores and treatment switching, with some links to schizophrenia and bipolar disorder.

This paper introduces a summary-statistics-based method to detect and quantify ascertainment bias in genetic studies using polygenic scores across multiple biobanks.

The paper infers circadian phase from blood biomarkers in UK Biobank using machine learning and defines a new construct, circadian acceleration, with genetic and environmental associations.

The paper identifies biologically distinct subtypes of MDD based on energy-related symptoms and finds genetic and metabolic differences between them.

This study identifies genetic differences between fear-based anxiety disorders and generalised anxiety disorder using GWAS, finding partial genetic distinction and shared liability to internalising psychopathology.

This paper benchmarks parameters for TWAS signature-matching to prioritize drug candidates using GWAS data and CMap, showing sensitivity to cell line choice and proposing a best-practice framework.

The paper identifies 219 pleiotropic conflict regions in the human genome using GWAS data, showing antagonistic variants are enriched for intermediate allele frequency SNPs and under positive/balancing selection.

The paper describes the AMBER program's cohort profile, collecting questionnaire data on antidepressant response from Generation Scotland participants to develop phenotypes and link with biological data.

This paper improves causal variant identification by genome-wide fine-mapping, showing it outperforms existing methods and identifies new causal variants for complex traits.

This paper introduces SBayesAPP, a Bayesian model that estimates annotation-stratified pleiotropy and co-polygenicity between complex traits using GWAS summary statistics, revealing cell-type-specific genetic correlations.

This paper introduces Manc-COJO, a method for multi-ancestry conditional and joint analysis of GWAS summary statistics, improving detection of independent signals and reducing false positives.

This paper identifies 58 genome-wide significant loci for major anxiety disorders using a large meta-analysis and highlights GABAergic signaling as a potential mechanism.

This study examines polygenic scores for schizophrenia and major depression in Chinese adults, finding associations with various traits and highlighting ancestry-specific genetic correlations.

The paper identifies polygenic score differences between antidepressant responders and difficult-to-treat depression groups in the AGDS:Cell-o cohort, suggesting biological distinctions and potential for early stratification.

This study conducts a genome-wide association analysis of generalised anxiety symptom severity, identifying 80 significant variants and showing genetic correlations with other mental and physical health traits.

This paper introduces an unsupervised framework to identify genetic subtypes of disease by clustering variant co-occurrence, applied to type 2 diabetes and asthma without using phenotype data.

This study identifies genetic risk factors for panic attacks and disorder through genome-wide meta-analysis and links them to multiple body systems and neuronal functions.

This paper benchmarks 20 methods for mapping trait-cell type associations by integrating GWAS and scRNA-seq data, finding CATCH to be the most robust approach.

The paper identifies five genomic factors explaining most genetic variance across 14 psychiatric disorders and finds shared and disorder-specific genetic signals with distinct biological functions.

This study finds that genetic risk for major depression is associated with higher cardiovascular disease risk in females but not males, even without a depression diagnosis.

The paper identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

This study identifies two novel genomic loci associated with antidepressant non-response and suggests potential drugs for further validation.

The paper introduces R2ROC, a method to compare AUC values from polygenic scores efficiently, offering faster computation and direct mapping to R2 for better interpretation.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

The paper identifies tissue-informative DNA methylation sites in ALS using targeted sequencing and machine learning, showing accurate prediction of disease status and progression.

The paper integrates genomic data with single-cell transcriptomics to identify brain cell types and regions associated with psychiatric disorders and traits.

This study examines the genetic and phenotypic links between neonatal C3 and C4 protein concentrations and psychiatric and autoimmune disorders, finding no strong causal associations with mental disorders but evidence of lower autoimmune risk with higher C4 levels.