Novel Biological Insights Into the Common Heritable Liability to Substance Involvement: A Multivariate Genome-wide Association Study

Schoeler, Tabea; Baldwin, Jessie; Allegrini, Andrea G.; Barkhuizen, Wikus; McQuillin, Andrew; Pirastu, Nicola; Kutalik, Zoltán; Pingault, Jean‐Baptiste

doi:10.1016/j.biopsych.2022.07.027

Cited by 18 publications

(23 citation statements)

References 63 publications

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“…Namely, that SUDs are a subset of externalizing 36 (common factor model); that externalizing and SUDs are separable, but correlated, constructs 26–28 (EXT‐ARF model); that all forms of substance involvement are distinct from, but correlated with, externalizing traits reflecting behavioural disinhibition 37 (BD‐SUB model); that SUDs are a subset of externalizing with meaningful residual variance (EXT‐resARF model); and that behavioural disinhibition and substance use are specific representations of a broad liability to externalizing 36 (bifactor model). As such, we tested the following models: Common factor model: an externalizing factor onto which all externalizing phenotypes and SUDs load (Figure 1A) EXT‐ARF model: a two‐correlated factors model in which externalizing phenotypes load onto an externalizing factor (EXT) and SUDs load on an addiction risk factor (ARF; Figure 1B) BD‐SUB model: a two‐correlated factors model in which all substance use phenotypes load onto a substance use factor (SUB) 37 and remaining phenotypes comprise a behavioural disinhibition factor (BD; Figure 1C) EXT‐resARF model: a model in which all phenotypes load on an externalizing factor (EXT) and SUDs load onto a residual ARF (Figure 1D) Bifactor model: a model in which liability to externalizing is captured by a general factor onto which all phenotypes load and residual liability to behavioural disinhibition (BD) and substance use (SUB) is captured by two specific factors (Figure 1E) We assessed the performance of each alternative model using a combination of the following metrics: Goodness‐of‐fit statistics, including the comparative fit index (CFI), the Akaike information criterion (AIC) and the standardized root mean square residual (SRMR), which provide absolute and relative indices of model fit. CFI and SRMR are absolute fit indices, with values greater than 0.95 and below 0.05, respectively, indicating excellent fit 38 …”

Section: Methodsmentioning

confidence: 99%

“…We chose these models to reflect alternative conceptualizations of the relationships between externalizing and SUDs in the existing literature. Namely, that SUDs are a subset of externalizing 36 (common factor model); that externalizing and SUDs are separable, but correlated, constructs [26][27][28] (EXT-ARF model); that all forms of substance involvement are distinct from, but correlated with, externalizing traits reflecting behavioural disinhibition 37 (BD-SUB model); that SUDs are a subset of externalizing with meaningful residual variance (EXT-resARF model); and that behavioural disinhibition and substance use are specific representations of a broad liability to externalizing 36 (bifactor model). As such, we tested the following models:…”

Section: Statistical Analysesmentioning

confidence: 99%

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A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders

Poore,

Hatoum,

Mallard

et al. 2023

Addiction Biology

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Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioural under‐control, termed externalizing phenotypes. In this study, we used genomic structural equation modelling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate genome‐wide association studies of an externalizing and an addiction risk factor, respectively. We first evaluated five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two‐factor structures and a bifactor model. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. Finally, we quantified the degree of polygenic overlap between externalizing and addiction risk using MiXeR. We found that the common and two‐factor structures provided the best fit to the data, evidenced by high factor loadings, good factor reliability and no evidence of concerning model characteristics. The two‐factor models yielded high genetic correlations between factors (rgs ≥ 0.87), and between the effect sizes of genetic correlations with external traits (rg ≥ 0.95). Nevertheless, 21 of the 84 correlations with external criteria showed small, significant differences between externalizing and addiction risk factors. MiXer results showed that approximately 81% of influential externalizing variants were shared with addiction risk, whereas addiction risk shared 56% of its influential variants with externalizing. These results suggest that externalizing and addiction genetic risk are largely shared, though both constructs also retain meaningful unshared genetic variance. These results can inform future efforts to identify specific genetic influences on externalizing and SUDs.

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Section: Methodsmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders

Poore,

Hatoum,

Mallard

et al. 2023

Addiction Biology

View full text Add to dashboard Cite

show abstract

“…Next, we tested a series of a priori specified models to investigate the genetic architecture of externalizing phenotypes and SUDs. We tested the following models: Common Factor Model: an externalizing factor onto which all externalizing phenotypes and SUDs load EXT-ARF Model: a two-correlated factors model in which externalizing phenotypes load onto an externalizing factor (EXT) and SUDs load on an addiction risk factor (ARF) BD-SUB Model: a two-correlated factors model in which all substance use phenotypes load onto a substance use factor (SUB) 39 and remaining phenotypes comprise a behavioral disinhibition factor (BD) EXT-resARF Model: a model in which all phenotypes load on an externalizing factor (EXT) and SUDs load onto a residual ARF Bifactor Model: a model in which liability to externalizing is captured by a general factor onto which all phenotypes load and residual liability to behavioral disinhibition (BD) and substance use (SUB) is captured by two specific factors.…”

Section: Methodsmentioning

confidence: 99%

“…BD-SUB Model: a two-correlated factors model in which all substance use phenotypes load onto a substance use factor (SUB) 39 and remaining phenotypes comprise a behavioral disinhibition factor (BD)…”

Section: Methodsmentioning

confidence: 99%

A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders

Poore¹,

Hatoum²,

Mallard³

et al. 2022

Preprint

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Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioral undercontrol, termed externalizing phenotypes. In this study, we used Genomic Structural Equation Modeling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate GWAS of an externalizing and an addiction risk factor, respectively. Using a preregistered set of criteria, we first evaluated the performance of five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures, and a bifactor model. We used a combination of model fit, factor reliability, and model characteristics to adjudicate among the models. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. We found that a common factor model, in which all externalizing phenotypes and SUDs were influenced by a single dimension of genetic risk best characterized the relationships among our phenotypes. Although two two-factor models also performed well, we found that the factors in those models were very highly correlated with each other (rgs > .87) and similarly genetically correlated with external criteria, suggesting they did not represent meaningfully distinct dimensions. Results from this study can be used to inform future efforts to characterize genetic liability for broad externalizing as well as specific externalizing phenotypes.

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“…Smoking and alcohol use disorder (AUD) commonly co-occur in the general population [ 1 ]. Compared to the use of a single substance, smoking co-occuring with AUD has greater adverse health effects [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program

et al. 2023

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Smoking behaviors and alcohol use disorder (AUD), both moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to co-occurring smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N = 318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 17 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly co-occuring phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

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Novel Biological Insights Into the Common Heritable Liability to Substance Involvement: A Multivariate Genome-wide Association Study

Cited by 18 publications

References 63 publications

A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders

A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders

A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders

Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program

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