In addition to multiple overlapping variants for AUDIT-C and AUD, we found a moderate-to-high genetic correlation between the traits: 0.522 in EAs and 0.930 in AAs. There are two potential explanations for the population difference in genetic correlation. First, it may reflect a bias in the assignment of AUD diagnoses by clinicians (e.g., in the context of a high AUDIT-C score, clinicians could be less likely to assign an AUD diagnosis to EAs than AAs, reducing the genetic correlation). Second, because LD structure in admixed populations is complex, LD score regression could have inflated the genetic correlation among AAs, an admixed population. Another factor relevant to this difference is the smaller number of AAs, which despite a higher rg, yielded a larger standard error. The genetic similarity between these alcohol-related traits is consistent with twin studies of alcohol dependence and alcohol consumption31,32. These findings are also consistent with the PRS analyses in the MVP sample, where both AUDIT-C and AUD PRS were associated with AUDIT-C and AUD phenotypes. Both traits also predicted multiple alcohol-related phenotypes in independent datasets, including alcohol
