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      Examining interactions between polygenic scores and interpersonal trauma exposure on alcohol consumption and use disorder in an ancestrally diverse college cohort

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          Abstract

          Introduction: Genetic factors impact alcohol consumption and use disorder (AUD), with large-scale genome-wide association studies (GWAS) identifying numerous associated variants. Aggregate genetic methods in combination with important environmental factors (e.g., interpersonal trauma [IPT]) can be applied to expand our understanding of the ways by which genetic and environmental variables work together to influence alcohol consumption and disordered use. The present study aimed to detail the relationships between genome-wide polygenic scores (PGS) for alcohol phenotypes (i.e., alcohol consumption and AUD status) and IPT exposure as well as the interaction between them across ancestry.

          Methods: Data were drawn from the Spit for Science (S4S) study, a US college student population, where participants reported on IPT exposure prior to college and alcohol consumption and problems during college (N = 9,006; ancestry: 21.3% African [AFR], 12.5% Admixed Americas [AMR], 9.6% East Asian [EAS], 48.1% European [EUR], 8.6% South Asian [SAS]). Two trans-ancestry PGS were constructed, one for alcohol consumption and another for AUD, using large-scale GWAS summary statistics from multiple ancestries weighted using PRS-CSx. Regression models were applied to test for the presence of associations between alcohol-PGS and IPT main and interaction effects.

          Results: In the meta-analysis across ancestry groups, IPT exposure and PGS were significantly associated with alcohol consumption (β IPT = 0.31, P IPT = 0.0002; β PGS = 0.09, P PGS = 0.004) and AUD (OR IPT = 1.12, P IPT = 3.5 × 10 −8; OR PGS = 1.02, P PGS = 0.002). No statistically significant interactions were detected between IPT and sex nor between IPT and PGS. When inspecting ancestry specific results, the alcohol consumption-PGS and AUD-PGS were only statistically significant in the EUR ancestry group (β PGS = 0.09, P PGS = 0.04; OR PGS = 1.02, P PGS = 0.022, respectively).

          Discussion: IPT exposure prior to college was strongly associated with alcohol outcomes in this college-age sample, which could be used as a preventative measure to identify students at high risk for problematic alcohol use. Additionally, results add to developing evidence of polygenic score association in meta-analyzed samples, highlighting the importance of continued efforts to increase ancestral representation in genetic studies and inclusive analytic approaches to increase the generalizability of results from genetic association studies.

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          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2402034/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2401360/overview
                URI : https://loop.frontiersin.org/people/2018414/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2402412/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/2402102/overview
                URI : https://loop.frontiersin.org/people/1962540/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/555998/overviewRole: Role: Role: Role: Role:
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                01 February 2024
                2023
                : 14
                : 1274381
                Affiliations
                [1] 1 Virginia Institute for Psychiatric and Behavioral Genetics , Virginia Commonwealth University , Richmond, VA, United States
                [2] 2 Department of Psychiatry , Virginia Commonwealth University , Richmond, VA, United States
                [3] 3 Center for Biological Data Science , Virginia Commonwealth University , Richmond, VA, United States
                [4] 4 Department of Psychology , Virginia Commonwealth University , Richmond, VA, United States
                [5] 5 Department of Psychiatry and Behavioral Sciences , SUNY Downstate Health Sciences University , Brooklyn, NY, United States
                [6] 6 Institute for Genomics in Health , SUNY Downstate Health Sciences University , Brooklyn, NY, United States
                [7] 7 Department of Epidemiology , Virginia Commonwealth University , Richmond, VA, United States
                Author notes

                Edited by: Peristera Paschou, Purdue University, United States

                Reviewed by: Ruifeng Hu, Harvard Medical School, United States

                Zhengyang Zhou, University of North Texas Health Science Center, United States

                *Correspondence: Roseann E. Peterson, Roseann.Peterson@ 123456downstate.edu
                [ † ]

                These authors contributed equally to this work

                Article
                1274381
                10.3389/fgene.2023.1274381
                10868390
                38361984
                119155e5-5ec6-4b22-8462-1966cc4f79c5
                Copyright © 2024 Sheerin, O’Hara-Payne, Lancaster, Suarez-Rivas, Spit for Science Working Group, Chatzinakos, Prom-Wormley and Peterson.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 August 2023
                : 22 November 2023
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the National Institutes of Health (NIH) including the National Institute on Alcohol Abuse and Alcoholism (NIAAA) K01AA025692 (CMS), P50AA022537 (REP); National Institute on General Medical Sciences (NIGMS) 5K12GM093857-11 (EEL); National Institute of Drug Abuse (NIDA) R01DA054313 (EPW); National Institute of Mental Health (NIMH) R01MH125938 (REP, EEL, CC); 5R25DA051339-03 (HSR). This work was also supported by the Virginia Commonwealth University (VCU) Institute for Research on Behavioral and Emotional Health (IRBEH; REP, CMS, RO, EPW, EEL). Spit for Science has been supported by VCU, P20AA017828, R37AA011408, K02AA018755, P50AA022537, and K01AA024152 from NIAAA and UL1RR031990 from the National Center for Research Resources and NIH Roadmap for Medical Research, as well as support by the Center for the Study of Tobacco Products at VCU. REDCap support is provided by CTSA award UM1TR004360 from the National Center for Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the views of the respective funding agencies.
                Categories
                Genetics
                Original Research
                Custom metadata
                Behavioral and Psychiatric Genetics

                Genetics
                alcohol consumption,alcohol use disorder (aud),interpersonal trauma,polygenic score (pgs),college and university students,physical assault,sexual assault,trans-ancestry

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