high-risk families, such as those in COGA. AUDIT-C associations in COGA using other addiction-enriched samples, such as the Million Veteran Program (Kranzler et al., 2019), where the genetic correlation between AUDIT-C and AUD is high, will likely provide insights into the extent of the effect of ascertainment differences in discovery and target samples. Interestingly, variance explained by either PRS was the highest in ALSPAC, a population-based cohort that is noticeably younger than the discovery sample (age 39–79 years). Lastly, the variance explained for AUD in the (independent) subset of UKB participants itself was markedly lower than in ALSPAC. Here, it is worth recognizing that AUDIT is a past-year screener for alcohol consumption/problems, and there may have been individuals in the original AUDIT GWAS with low scores who were formerly problem drinkers. Furthermore, using ICD codes derived from hospital records as a proxy for AUD in the UKB may have resulted in false negatives; some of the ‘controls’ could have been problem drinkers but had not been diagnosed with an alcohol related condition as a hospital inpatient. Thus, the lower prediction in the UKB sample may reflect the instruments used to measure AUD in both the discovery and target GWAS.
