Some limitations of our study are noteworthy. First, comorbidities in the cross-disorder meta-analysis from which the PRS were derived, as well as in the target SAGE sample, may be subject to certain unmeasured confounds. For example, the PGC did not examine the extent of cocaine (or other substance) use in their sample population (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013), so our associations of SCZ PRS with severe cocaine dependence may be an artifact of increased cocaine use by people with schizophrenia (Serper et al., 1995), or of cocaine-induced psychosis resulting in a diagnosis of schizophrenia (Brady et al., 1991). Conversely, though severe psychopathology (i.e., AUT, BIP, SCZ) is likely to be uncommon in SAGE and unlikely to influence associations, more common psychopathologies (i.e., ADHD, MDD) were likely present. Therefore, associations with ADHD or MDD PRS may have been mediated by the actual expression of the disorder among SAGE participants (e.g., people with higher MDD PRS in SAGE develop MDD, which in turn is associated with substance use disorders). Though data on ADHD diagnosis in SAGE were unavailable, repeating MDD PRS analyses while controlling for DSM-IV MDD diagnosis did not alter results, indicating that associations between PRS and substance use outcomes were not directly related to disorder expression (Supplementary Table S8). Longitudinal studies of well-characterized populations, as well as an increased emphasis on the study of substance use disorders in consortia such as the PGC, will be critical to further address these questions of temporality and comorbidity.

Second, SAGE was ascertained for liability to substance dependence, specifically to alcohol, nicotine, and cocaine; the generalizability of these findings to the general population is thus unclear. Additionally, the factor structure of GENSUB might be somewhat sample-specific, and residual associations with involvement with specific substances (i.e., non-problem cannabis use) may have been artifacts of sample ascertainment. However, this ascertainment strategy allowed us to study the full range of substance involvement—from never-use to severe dependence—across both licit (i.e., alcohol, nicotine) and illicit (i.e., cannabis, cocaine, opioids) drugs, which would not have been possible in a population-based sample of comparable size. Nonetheless, it is important to replicate these findings in other samples.

Third, though multiple nominally significant relationships between genetic risk for individual psychiatric disorders and involvement with specific substances emerged, few survived correction for the large number of statistical tests performed. These results thus may represent spurious associations and should be interpreted with caution. However, given the consistency of certain associations (e.g., ADHD and nicotine use) with prior genetic (e.g., Chang et al., 2012) and epidemiological (e.g., Elkins et al., 2007; Lee et al., 2011) literature, they should not be dismissed outright. Notably, the only disorders with significant post-correction associations—MDD and SCZ—were those with the largest numbers of cases in the PGC cross-disorder meta-analysis (N_MDD = 9227, N_SCZ = 9379). Nominal associations may thus strengthen with larger discovery samples, which may provide more precise PRS estimates, as well as larger target samples. In support of this interpretation, repetition of schizophrenia PRS analyses with scores derived from the much larger second-generation PGC GWAS (N_SCZ2 = 36,989) yielded associations that were generally stronger than those from the first-generation analyses (Supplementary Tables S6 and S7; see Supplementary Figure S3 for comparison).

Fourth, while our PRS approach yielded evidence that shared common genetic architecture contributes to comorbidity between psychopathology and substance involvement, it does not provide insight into specific biological (e.g., reward-related neural responsiveness, epigenetically medicated changes in gene expression), psychological (e.g., anhedonia, impulsivity), and/or experiential (e.g., early life stress, peer group) mechanisms through which this risk is manifest (e.g., Olfson et al., 2014; Peña-Oliver et al., 2016; Ron and Barak, 2016). Compelling evidence suggests that psychopathology and substance involvement share overlapping neural systems (e.g., Buckholtz and Meyer-Lindenberg, 2012), molecular pathways (e.g., Ng et al., 2013), and environmental exposures (e.g., Kristjansson et al., 2016). Future PRS research probing biological systems and psychological traits common to both psychiatric and substance use disorders (e.g., Lancaster et al., 2016; Peña-Oliver et al., 2016) and incorporating environmental/experiential measures (e.g., French et al., 2015), alongside genomewide efforts to partition heritability into specific pathways and functional categories (e.g., Lee et al., 2012; Finucane et al., 2015), will further our understanding of the mechanisms underlying this comorbidity (Bogdan et al., 2016).