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    The Associations Between Polygenic Risk, Sensation Seeking, Social Support, and Alcohol Use in Adulthood
    (Amer Psychological Assoc, 2021) Su, Jinni; Kuo, Sally I-Chun; Aliev, Fazil; Chan, Grace; Edenberg, Howard J.; Kamarajan, Chella; McCutcheon, Vivia V.
    Genetic predispositions play an important role in alcohol use. Understanding the psychosocial mechanisms through which genetic risk unfolds to influence alcohol use outcomes is critical for identifying modifiable targets and developing prevention and intervention efforts. In this study, we examined the role of sensation seeking and social support from family and friends in linking genetic risk to alcohol use. We also examined the role of social support in moderating the associations between genetic risk and sensation seeking and alcohol use. Data were drawn from a sample of 2,836 European American adults from the Collaborative Study on the Genetics of Alcoholism (46% male, mean age = 35.65, standard deviation [SD] = 10.78). Results from path analysis indicated that genome-wide polygenic scores for alcohol consumption (alc-GPS) were associated with higher sensation seeking, which in turn was associated with higher levels of alcohol use. alc-GPS was also associated with higher alcohol use indirectly via lower levels of family support. In addition, high friend support attenuated the association between alc-GPS and sensation seeking and alcohol use. The pattern of associations was similar for males and females, with some differences in the associations between social support and alcohol use observed across age. Our findings highlight the important role of intermediate phenotypes and gene-environment interplay in the pathways of risk from genetic predispositions to complex alcohol use outcomes.
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    POLYGENIC INFLUENCES ON ALCOHOL RELATED NEUROPHYSIOLOGICAL AND NEUROCOGNITIVE PROCESSES ACROSS THE LIFESPAN
    (Elsevier, 2019) Meyers, Jacquelyn; Johnson, Emma; Salvatore, Jessica; Aliev, Fazil; Pandey, Ashwini; Kamarajan, Chella; Nurnberger, John
    [No abstract available]
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    Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach
    (Springernature, 2021) Kinreich, Sivan; McCutcheon, Vivia V.; Aliev, Fazil; Meyers, Jacquelyn L.; Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.
    Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N=1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.