To characterize the multifactorial genetic aetiology of tobacco and alcohol use, we computed genetic correlations of our EUR-stratified results with 1,141 medical, biomarker and behavioural phenotypes from the UK Biobank29 (Supplementary Tables 10 and 11). An affinity propagation clustering algorithm30 was used to aid interpretability by grouping UK Biobank phenotypes such that each of the five current phenotypes were exemplars (Supplementary Fig. 5). SmkInit and AgeSmk clustered together, as did SmkCes and CigDay, with all four forming a broad higher-level smoking cluster. Phenotypes with high positive genetic correlations with SmkInit included addiction to any substance, neighbourhood material deprivation, diagnosis of chronic obstructive pulmonary disease, and a negative correlation with age at first sexual intercourse (|rg| = 0.57–0.64). For AgeSmk, the largest genetic correlations were with reproductive phenotypes such as age at first birth (rg = 0.69–0.71) and measures of years of education and attainment (rg = 0.58–0.69). CigDay and SmkCes were most highly positively correlated with respiratory and cardiovascular diseases and cancers (rg = 0.52–0.72), highlighting their genetic link to adverse disease outcomes. Finally, DrnkWk was most strongly correlated with
