Multi-Ancestry Survival GWAS of Substance Use Initiation in the ABCD Study

Substance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information.

We conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes-alcohol, nicotine, cannabis, and any substance use-using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochran's Q and I ² ), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci.

In the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p -values: alcohol ∼ 1 × 10 ^-7 , any ∼ 1 × 10 ^-7 , cannabis ∼ 5 × 10 ^-8 , nicotine ∼ 1 × 10 ^-8 ). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses ( p < 5 × 10 ^-8 ). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates.

Survival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.