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Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study

Molecular Psychiatry volume 26, pages 1133–1141 (2021)Cite this article

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Abstract

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12–15, 16–19, 20–30) and EA age group (16–19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model’s accuracy in both ancestries’ samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.

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Acknowledgements

The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators BP, VH, H Edenberg, L Bierut, includes 11 different centers: of Connecticut (V Hesselbrock); Indiana University (HJE, J Nurnberger Jr, T Foroud); University of Iowa (S Kuperman, J Kramer); SUNY Downstate (BP); Washington University in St Louis (L Bierut, J Rice, K Bucholz, A Agrawal); University of California at San Diego (MS); Rutgers University (J Tischfield, A Brooks); University of Texas Rio Grand Valley (L Almasy), Virginia Commonwealth University (D Dick), Icahn School of Medicine at Mount Sinai (A Goate), and Howard University (R Taylor). Other COGA collaborators include: LB (University of Connecticut); J McClintick, L Wetherill, X Xuei, Y Liu, D. Lai, S O’Connor, M Plawecki, S Lourens (Indiana University); G Chan (University of Iowa; University of Connecticut); JM, DC, CK, AP, JZ (SUNY Downstate); J-CW, M Kapoor, S Bertelsen (Icahn School of Medicine at Mount Sinai); AA, V McCutcheon, S Saccone (Washington University); J Salvatore, F Aliev, B Cho (Virginia Commonwealth University); and Mark Kos (University of Texas Rio Grand Valley). A Parsian and M Reilly are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, P Michael Conneally, Raymond Crowe and Wendy Reich, for their critical contributions. This national collaborative study is supported by an NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). JLM is supported by K01DA037914 from the National Institute on Drug Abuse (NIDA), JES acknowledges support from K01AA024152 (NIAAA) and AA acknowledges support from K02DA032573 (NIDA). Funding support for GWAS genotyping performed at the Johns Hopkins University Center for Inherited Disease Research was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438), and the NIH contract ‘High throughput genotyping for studying the genetic contributions to human disease’ (HHSN268200782096C). GWAS genotyping was also performed at the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine, which is partially supported by NCI Cancer Center Support Grant no. P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant no. UL1RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research.

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Authors and Affiliations

  1. Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA

    Sivan Kinreich, Jacquelyn L. Meyers, Chella Kamarajan, Ashwini K. Pandey, David B. Chorlian, Jian Zhang, Gayathri Pandey, Stacey Subbie-Saenz de Viteri, Dan Pitti & Bernice Porjesz

  2. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Adi Maron-Katz

  3. Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA

    Andrey P. Anokhin

  4. Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA

    Lance Bauer & Victor Hesselbrock

  5. Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA

    Marc A. Schuckit

  6. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Howard J. Edenberg

  7. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

    Howard J. Edenberg

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  1. Sivan Kinreich
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Correspondence to Sivan Kinreich.

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Kinreich, S., Meyers, J.L., Maron-Katz, A. et al. Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry 26, 1133–1141 (2021). https://doi.org/10.1038/s41380-019-0534-x

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