Marc Alan Schuckit

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.

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 uses polygenic risk scores to predict substance use disorders in two samples, finding increased risk with higher scores but limited clinical utility.

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