TY - JOUR
T1 - Refining the scope of genetic influences on alcohol misuse through environmental stratification and gene–environment interaction
AU - Spit for Science Working group
AU - Savage, Jeanne E.
AU - de Leeuw, Christiaan A.
AU - Werme, Josefin
AU - Dick, Danielle M.
AU - Posthuma, Danielle
AU - van der Sluis, Sophie
AU - Chartier, Karen
AU - Amstadter, Ananda
AU - Lilley, Emily
AU - Gelzinis, Renolda
AU - Morris, Anne
AU - Bountress, Katie
AU - Adkins, Amy E.
AU - Thomas, Nathaniel
AU - Neale, Zoe
AU - Pedersen, Kimberly
AU - Bannard, Thomas
AU - Cho, Seung B.
AU - Adkins, Amy E.
AU - Pedersen, Kimberly
AU - Barr, Peter
AU - Byers, Holly
AU - Berenz, Erin C.
AU - Caraway, Erin
AU - Cho, Seung B.
AU - Clifford, James S.
AU - Cooke, Megan
AU - Do, Elizabeth
AU - Edwards, Alexis C.
AU - Goyal, Neeru
AU - Hack, Laura M.
AU - Halberstadt, Lisa J.
AU - Hawn, Sage
AU - Kuo, Sally
AU - Lasko, Emily
AU - Lend, Jennifer
AU - Lind, Mackenzie
AU - Long, Elizabeth
AU - Martelli, Alexandra
AU - Meyers, Jacquelyn L.
AU - Mitchell, Kerry
AU - Moore, Ashlee
AU - Moscati, Arden
AU - Nasim, Aashir
AU - Neale, Zoe
AU - Opalesky, Jill
AU - Overstreet, Cassie
AU - Pais, A. Christian
AU - Pedersen, Kimberly
AU - Raldiris, Tarah
N1 - Publisher Copyright:
© 2024 The Author(s). Alcohol, Clinical and Experimental Research published by Wiley Periodicals LLC on behalf of Research Society on Alcohol.
PY - 2024/10
Y1 - 2024/10
N2 - Background: Gene–environment interaction (G × E) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we use a series of genome-wide gene–environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. Methods: We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. Results: GWEIS models showed few genetic variants with significant interaction effects across gene–environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3 on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. Conclusions: Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.
AB - Background: Gene–environment interaction (G × E) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we use a series of genome-wide gene–environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. Methods: We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. Results: GWEIS models showed few genetic variants with significant interaction effects across gene–environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3 on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. Conclusions: Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.
UR - https://www.scopus.com/pages/publications/85202944319
UR - https://www.scopus.com/pages/publications/85202944319#tab=citedBy
U2 - 10.1111/acer.15425
DO - 10.1111/acer.15425
M3 - Article
AN - SCOPUS:85202944319
SN - 0145-6008
VL - 48
SP - 1853
EP - 1865
JO - Alcoholism: Clinical and Experimental Research
JF - Alcoholism: Clinical and Experimental Research
IS - 10
ER -