@article {461, title = {Item-level genome-wide association study of the Alcohol Use Disorders Identification Test in three population-based cohorts}, journal = {The American Journal of Psychiatry}, volume = {179}, year = {2022}, pages = {58{\textendash}70}, abstract = {

OBJECTIVE: Genome-wide association studies (GWASs) of the Alcohol Use Disorders Identification Test (AUDIT), a 10-item screen 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 may have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders and/or medical conditions. The authors used genomic structural equation modeling to elucidate the genetics of alcohol consumption and problematic consequences of alcohol use as measured by AUDIT.

METHODS: To explore these unexpected differences in genetic correlations, the authors conducted the first item-level and the largest GWAS of AUDIT items (N=160,824) and applied a multivariate framework to mitigate previous biases.

RESULTS: The authors identified novel patterns of similarity (and dissimilarity) among the AUDIT items and found evidence of a correlated two-factor structure at the genetic level ({\textquoteleft}{\textquoteleft}consumption\&$\#$39;\&$\#$39; and {\textquoteleft}{\textquoteleft}problems,\&$\#$39;\&$\#$39; rg=0.80). Moreover, by applying empirically derived weights to each of the AUDIT items, the authors constructed an aggregate measure of alcohol consumption that was strongly associated with alcohol dependence (rg=0.67), moderately associated with several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by conducting polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, the authors identified novel genetic associations between alcohol consumption, alcohol misuse, and health.

CONCLUSIONS: This 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.

}, keywords = {Alcohol, Alcohol Consumption, ALSPAC, Genomic Structural Equation Modeling, GWAS, Substance-Related and Addictive Disorders}, doi = {10.1176/appi.ajp.2020.20091390}, author = {Mallard, Travis T and Savage, Jeanne E and Johnson, Emma C and Huang, Yuye and Edwards, Alexis C and Hottenga, Jouke J and Grotzinger, Andrew D and Gustavson, Daniel E and Jennings, Mariela V and Anokhin, Andrey and Dick, Danielle M and Edenberg, Howard J and Kramer, John R and Lai, Dongbing and Meyers, Jacquelyn L and Pandey, Ashwini K and Harden, Kathryn Paige and Nivard, Michel G and de Geus, Eco J C and Boomsma, Dorret I and Agrawal, Arpana and Davis, Lea K and Clarke, Toni-Kim and Palmer, Abraham A and Sanchez-Roige, Sandra} } @article {445, title = {Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity}, journal = {Brain and Behavior}, volume = {11}, year = {2021}, pages = {e02188}, abstract = {

BACKGROUND AND PURPOSE: The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain.

METHODS: We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations.

RESULTS: We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters.

CONCLUSION: The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.

}, keywords = {brain disorders, electroencephalography, ENIGMA, harmonization, imaging genetics, open science}, doi = {10.1002/brb3.2188}, author = {Smit, Dirk J A and Andreassen, Ole A and Boomsma, Dorret I and Burwell, Scott J and Chorlian, David B and de Geus, Eco J C and Elvs\aashagen, Torbj{\o}rn and Gordon, Reyna L and Harper, Jeremy and Hegerl, Ulrich and Hensch, Tilman and Iacono, William G and Jawinski, Philippe and J{\"o}nsson, Erik G and Luykx, Jurjen J and Magne, Cyrille L and Malone, Stephen M and Medland, Sarah E and Meyers, Jacquelyn L and Moberget, Torgeir and Porjesz, Bernice and Sander, Christian and Sisodiya, Sanjay M and Thompson, Paul M and van Beijsterveldt, Catharina E M and van Dellen, Edwin and Via, Marc and Wright, Margaret J} }