Blood-based epigenome-wide association study and prediction of alcohol consumption.

Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.

The driving-related attitudes, beliefs and behaviours of cannabis users in the Australian Capital Territory following decriminalisation.

Introduction: In January 2020, the government of the Australian Capital Territory (ACT) decriminalised the possession and cultivation of cannabis for personal use. This study explored the driving-related attitudes, beliefs and behaviours of ACT residents who are legally cultivating and consuming cannabis.

Methods: A two-part cross-sectional study was conducted.

DNA methylation-based predictors of metabolic traits in Scottish and Singaporean cohorts.

Exploring the molecular correlates of metabolic health measures may identify their shared and unique biological processes and pathways. Molecular proxies of these traits may also provide a more objective approach to their measurement. Here, DNA methylation (DNAm) data were used in epigenome-wide association studies (EWASs) and for training epigenetic scores (EpiScores) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio, and blood-based measures of glucose, high-density lipoprotein cholesterol, and total cholesterol in >17,000 volunteers from the Generation Scotland (GS) cohort.

MethylGPT: a foundation model for the DNA methylome.

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.