Oliver Pain

This paper evaluates combining expression-based surrogate variables and genotype-based principal components in differential expression analysis, finding the combined approach improves replicability and biological recall in ALS datasets.

The paper examines genetic contributions to educational attainment and occupational status in Estonia during and after the Soviet era using the Estonian Biobank, finding increased genetic influence in education but not occupation.

The paper identifies novel cross-trait associations using polytranscriptomic scores in Parkinson's disease and improves prediction models with multi-PTS integration.

This paper presents a drug repurposing pipeline that integrates knowledge graphs and network medicine, benchmarked across diseases and applied to ALS, identifying 77 prioritized candidate drugs.

This study performs a trans-ancestry GWAS of SSRI response in depression, finding suggestive associations and enrichment in serotonergic pathways but no genome-wide significant variants.

The paper identifies nuclear genetic variants influencing mtDNA transcript abundance across tissues and links them to disease risk.

The paper identifies variance QTLs (vQTLs) and gene-environment interactions for body fat distribution using UK Biobank MRI and genetic data, with replication in external cohorts.

This study evaluates polygenic score methods across diverse populations using summary statistics, finding that combining ancestry-aligned and European GWAS improves performance and computational efficiency.

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

This study uses polygenic and transcriptional risk scores to analyze psychiatric and cognitive symptoms in Parkinson's disease, finding distinct associations between genetic and transcriptional risks and symptom progression.

This study uses GWAS summary statistics to identify shared genetic loci across neuropsychiatric diseases and finds HLA region associations with potential shared causal variants.

GenoPred is a pipeline that generates interpretable polygenic scores by integrating GWAS summary statistics and genotype data, supporting diverse populations and improving reproducibility.

This study uses GWAS and functional genomics to identify ALS risk genes, repurpose drugs, and improve risk prediction using polytranscriptomic scores.

This study examines the association between CYP2C19 and CYP2D6 metabolic phenotypes and antidepressant response using genotype imputation across 13 studies, finding no significant associations after correction for multiple testing.

UPDOG improves trans-ancestral polygenic score prediction by considering genetic architecture of both discovery and test cohorts, showing improvements in two-thirds of cases across four ancestries and six phenotypes.

This study evaluates polygenic scoring methods across five biobanks and finds that biobank differences outweigh method differences, with ensemble learning improving performance.

The paper develops TWAS models using eQTL summary statistics to predict gene expression and identifies novel schizophrenia associations.

The paper investigates gene-environment interactions for depressive symptoms using UK Biobank data, finding limited polygenic-environment interactions but significant residual-environment interactions.

This study improves polygenic score prediction across traits by incorporating local genetic correlation and heritability estimates from genetically correlated phenotypes.

This paper introduces a method to convert polygenic scores to an absolute scale using summary statistics, validated on UK Biobank data, and provides web tools for implementation.

This study investigates the relationship between early life stressors and adult mental health through methylome-wide association studies, finding no evidence of methylome mediation but linking birth date to blood cell type composition.

This study evaluates polygenic scoring methods in a reference-standardized framework, finding that LDPred2, lassosum, and PRScs provide the best predictive performance.

This study evaluates gene expression risk scores (GeRS) for predicting neuropsychiatric and anthropometric traits, finding they improve prediction when combined with polygenic risk scores (PRS).

This study uses multiple depression measures to improve the validity of Major Depressive Disorder cases in the UK Biobank, finding increased genetic contribution with more measures.

This paper ranks antidepressants based on their similarity to gene expression profiles of treatment responders, finding escitalopram and citalopram show high rankings in some cell lines.

This study identifies 91 genes with differential gene expression in major depression using TWAS, including 8 high-confidence causal associations and novel risk genes.

Impute.me provides an open-source tool for calculating and interpreting polygenic risk scores from consumer genetic data to better communicate genetic risk for complex diseases.

This study examines genetic correlations between psychotic experiences across age and with psychiatric disorders, finding significant overlaps with schizophrenia and depression.

This study performs a transcriptome-wide association study to link schizophrenia risk variants to gene expression in the dorsolateral prefrontal cortex, identifying 89 genes associated with pre- and post-synaptic abnormalities.

This study integrates ASD GWAS summary statistics with gene expression data to identify differentially expressed genes in ASD and estimate heritability explained by predicted expression.