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Thomas NS, Gillespie NA, Chan G, Edenberg HJ, Kamarajan C, Kuo SIC, Miller AP, Nurnberger JI, Tischfield J, Dick DM, Salvatore JE. A Developmentally-Informative Genome-wide Association Study of Alcohol Use Frequency. Behav Genet 2024; 54:151-168. [PMID: 38108996 PMCID: PMC10913412 DOI: 10.1007/s10519-023-10170-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Contemporary genome-wide association study (GWAS) methods typically do not account for variability in genetic effects throughout development. We applied genomic structural equation modeling to combine developmentally-informative phenotype data and GWAS to create polygenic scores (PGS) for alcohol use frequency that are specific to developmental stage. Longitudinal cohort studies targeted for gene-identification analyses include the Collaborative Study on the Genetics of Alcoholism (adolescence n = 1,118, early adulthood n = 2,762, adulthood n = 5,255), the National Longitudinal Study of Adolescent to Adult Health (adolescence n = 3,089, early adulthood n = 3,993, adulthood n = 5,149), and the Avon Longitudinal Study of Parents and Children (ALSPAC; adolescence n = 5,382, early adulthood n = 3,613). PGS validation analyses were conducted in the COGA sample using an alternate version of the discovery analysis with COGA removed. Results suggest that genetic liability for alcohol use frequency in adolescence may be distinct from genetic liability for alcohol use frequency later in developmental periods. The age-specific PGS predicts an increase of 4 drinking days per year per PGS standard deviation when modeled separately from the common factor PGS in adulthood. The current work was underpowered at all steps of the analysis plan. Though small sample sizes and low statistical power limit the substantive conclusions that can be drawn regarding these research questions, this work provides a foundation for future genetic studies of developmental variability in the genetic underpinnings of alcohol use behaviors and genetically-informed, age-matched phenotype prediction.
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Affiliation(s)
- Nathaniel S Thomas
- Department of Psychology, Virginia Commonwealth University, Box 842018, Richmond, VA, 23284-2018, USA.
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
- Department of Psychiatry, University of Iowa, Carver College of Medicine, Iowa City, IA, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chella Kamarajan
- Department of Psychiatry & Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Alex P Miller
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Jay Tischfield
- Department of Genetics, Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Jessica E Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
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Ahangari M, Gentry AE, Hassan MF, Nguyen TH, Kendler KS, Bacanu SA, Peterson RE, Riley BP, Webb BT. Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557163. [PMID: 37745400 PMCID: PMC10515858 DOI: 10.1101/2023.09.11.557163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default = 1.06 x 10 -9 and P Empirical weight = 6.25 x 10 -11 ). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.
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