Multivariate GWAS elucidates the genetic architecture of alcohol consumption and misuse, corrects biases, and reveals novel associations with disease

Abstract

Genome-wide association studies (GWASs) of the Alcohol Use Disorder Identification Test (AUDIT), a ten-item screener for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. In doing so, we identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, rg=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (rg=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by performing polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

Novel characterization of the multivariate genetic architecture of internalizing psychopathology and alcohol use

“…For all UK Biobank traits, we limited our sample to only current drinkers, to reduce misreporting and longitudinal bias. We excluded AUDIT items 6 and 9 as they both had low SNP-heritabilities (-Table S2), similar to Mallard, Savage, et al (2020). Chang et al, 2015, command: --maf 0.01 -hwe 1e-8 -indep-pairwise 50 5 0.2), which also (using the same quality control, but without LD-pruning) were used in BOLT-LMM to control for relatedness.…”

“…We modeled the genetic correlation matrix using a confirmatory factor analysis with four latent factors based on prior knowledge of the alcohol and internalizing phenotypes (K. S. Kendler, Aggen, Prescott, Crabbe, & Neale, 2012;Mallard, Savage, et al, 2020;Polimanti et al, 2019;Sanchez-Roige et al, 2019), as well as the patterns identified in the genetic correlation matrix. We chose to use confirmatory factor analysis, as opposed to exploratory factor analysis, to validate tests can be used to compare nested models (Hu & Bentler, 1998).…”

“…Xue et al showed that correcting for self‐reporting biases in alcohol use GWAS led to a positive genetic correlation of AUDIT‐C with depression ( r g = .11), but this correlation remained lower than the correlation with AUDIT‐P (Xue et al, 2021). Alternatively, individual AUDIT items may be disproportionately affecting AUDIT‐P and AUDIT‐C composite scores (Mallard, Savage, et al, 2020), where equally weighting each AUDIT item does not accurately represent measures of PAU and alcohol consumption. However, after identifying alcohol consumption and problematic use latent factors with Genomic structural equation modeling (Genomic SEM) (Grotzinger et al, 2019), psychopathology phenotypes remained more strongly genetically correlated with PAU than consumption (Mallard, Savage, et al, 2020).…”

“…However, after identifying alcohol consumption and problematic use latent factors with Genomic structural equation modeling (Genomic SEM) (Grotzinger et al, 2019), psychopathology phenotypes remained more strongly genetically correlated with PAU than consumption (Mallard, Savage, et al, 2020).…”

“…Xue et al showed that correcting for self-reporting biases in alcohol use GWAS led to a positive genetic correlation of AUDIT-C with depression (r g = .11), but this correlation remained lower than the correlation with AUDIT-P (Xue et al, 2021). Alternatively, individual AUDIT items may be disproportionately affecting AUDIT-P and AUDIT-C composite scores (Mallard, Savage, et al, 2020), where equally weighting each AUDIT item does not accurately represent measures of PAU and alcohol consumption.…”