Dongbing Lai

This study identifies AUD-associated and ethanol-responsive proteins in human cortical organoids and finds candidate biomarkers linked to AUD and ethanol exposure.

The paper develops and validates polygenic scores for substance use disorders across multiple ancestries, finding clinically relevant performance in European and Latinx populations but not in African populations.

This paper conducts a multi-ancestry GWAS of stimulant use disorder, identifies 19 genome-wide significant loci, and finds genetic correlations with other psychiatric and substance use disorders.

This study uses in vivo calcium imaging to analyze M2 neuronal activity during cocaine self-administration, finding increased neuronal engagement and adaptation in cocaine-exposed mice.

This study performs a multi-ancestral GWAS of DSM-defined nicotine dependence, finding genetic correlations with other substance use disorders and health traits, and identifying a strong general addiction liability factor.

The paper improves G6PD variant interpretation using All of Us data and a yeast assay, identifying new variants and classifying some as non-pathogenic.

This study examines how polygenic risk scores for Alcohol Use Disorder affect microglial responses to ethanol exposure in human iPSC-derived microglia.

The paper uses multiomic profiling of the caudate nucleus to identify gene expression and chromatin changes in AUD, linking them to risk genes through QTL analysis.

This paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

This paper introduces M-REGLE, a multimodal deep learning method that improves genetic analysis of cardiovascular traits by integrating multiple clinical data modalities and outperforms unimodal approaches in locus discovery and risk prediction.

The paper identifies functional 3'-UTR variants linked to alcohol use disorder and related traits using MPRA and GWAS integration.

This study investigates the combined effect of rs699 and rs5051 on angiotensinogen expression and hypertension, finding no significant difference in liver AGT expression or hypertension phenotypes when the variants occur independently.

The paper uses polygenic risk scores from GWAS to assess their association with suicidal thoughts and behaviors in a sample with alcohol use disorders, finding significant contributions from multiple PRS.

This study uses machine learning to predict alcohol-related memory problems in older adults by integrating multi-domain features including brain connectivity, polygenic risk, and lifestyle factors.

This paper trains a deep neural network on noisy COPD labels to predict COPD status from spirograms, improving genetic discovery by identifying new loci and replicating existing ones.

The paper integrates GWAS data with multi-omics datasets to identify novel genes for alcohol use disorder and link them to neurodegenerative diseases.

This study identifies genetic variants shared between schizophrenia and alcohol use disorder, finding both convergent and divergent effects and evidence of brain tissue expression.

This paper conducts a multivariate GWAS of AUDIT items to correct biases and identify novel genetic associations between alcohol consumption, misuse, and health outcomes.

This study identifies genetic modifiers of LRRK2 mutations in Parkinson's disease, finding a significant variant in CORO1C and a polygenic risk score effect on penetrance.

The paper compares genome-wide data from opioid use and dependence cases to identify genetic associations and differences between exposure and dependence.

The paper identifies functional variants in human brains with alcohol use disorders by combining allele-specific expression analysis with high-throughput reporter assays.

The paper analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.

This study conducts a trans-ancestral GWAS of alcohol dependence, identifying genetic variants and correlations with psychiatric disorders.