Gene variants associated with antisocial behaviour: A latent variable approach

Genetic factors are integral to the understanding of the etiology of antisocial behaviour, as evidenced by family and twin studies that indicate a heritability of at least 50% (Moffitt, 2005; Viding, Larsson, & Jones, 2008). Quantitative genetic studies indicate that genetic and environmental influences on the emergence of antisocial behaviour appear remarkably similar in their magnitude despite different approaches, assessment methods, age of assessment, or the gender of the participants (Rhee & Waldman, 2002; Rhee & Waldman, 2011; Lahey & Waldman, 2012). In addition, many risk factors that are traditionally thought to be environmental may also reflect genetic vulnerability (Moffitt, 2005). Over the past decade, it was widely expected that the genetic basis of common disorders would be resolved by genome-wide association studies (GWAS)--large-scale studies in which the entire genome is covered by genetic markers. Although recent GWAS have given us a clearer picture of the allelic architecture of genetic susceptibility for some pediatric-onset disorders including type I diabetes, the bulk of heritable variance remains unexplained for many pediatric-onset neuropsychiatric disorders including antisocial behaviour and substance use disorders. Information about gene function has led to the identification of a number of relevant candidate systems of genes (CSG) that influence antisocial (delinquent, criminal, and substance use) behaviours. The CSG approach provides the advantages of hypothesis-driven research, while mitigating some limitations of a candidate gene approach that usually targets only one or a small number of loci. Although a CSG approach is a viable alternative to both a candidate gene and GWAS studies, it still requires a very large number of subjects to identify allelic variations that by themselves account for a very small proportion of the phenotypic variance (Maher et al., 2011; Grigorenko et al., 2010; Yrigollen et al., 2008; Greenwood et al., 2011). While CSG studies examine related families of genes, the analytical approach often focuses on only one allelic variant at a time. Several studies have demonstrated that multiple measurements of genes are needed to fully realize the association between the variance in genes to phenotypic variation (Burt & Mikolajewski, 2008; Grigorenko et al, 2010). Thus, whereas positive CSG studies allow inferences to be made concerning the probable presence of gene-gene interactions, they do not permit an accurate estimation of the phenotypic variance explained since the variance accounted for by gene-gene interactions is unlikely to be simply additive. Indeed, very few approaches, if any, are currently available to assess systematically the presence of a ‘shared’ variance across genetic risk alleles with regard to a particular phenotype of interest. The application of a latent variable approach to genetic data should facilitate the identification of allelic variants that collectively contribute to particular phenotypic outcome. This approach has been widely used in economics and psychology to study observable behavior that may be influenced by unobservable constructs that are not directly measurable (Avery, 1979; Bollen, 2002). A clear advantage of using a latent variable analysis is that it reduces the dimensionality of data so that a large number of observable variables can be aggregated in a model to represent a single underlying factor when there are strong relationships among variables. The application of latent variable analyses to genetic data is novel. Because observed variables that have no correlation cannot result in a latent construct, the identification of a latent genetic construct provides a potential path to discover a ‘shared’ variance across genetic risk alleles. This shared variance could be additive, multiplicative or interactive. However, this latent variable approach is limited by its inability to specify the biological nature of the gene-gene interactions that underlie this shared variance. Consequently, we have chosen to refer to the “co-action” of allelic variants (additive, multiplicative, and other) in describing our findings. The choice of the genetic loci to be assessed is critically important. Several studies have reported associations between antisocial behaviour and drug use and genetic variants associated with dopaminergic and serotoninergic pathways (Chambers et al., 2001; Bierut et al., 2002; Solanto, 2002; Kim-Cohen et al., 2006; Moffitt, 2005; Blum et al., 2012; McCrory et al., 2012; Moul et al., 2013). We also selected genes associated with affiliative behaviors and stress response. The rationale for including in the CSG genetic loci implicated in affiliative behaviours is based, in part, on studies that report strong associations between inconsistent, harsh, or abusive disciplinary practices by parents and child rule-breaking behaviours, delinquency, and aggression (Stouthamer-Loeber et al., 2001; Stanger et al., 2004). Childhood conduct problems have also been associated with a lack of parental involvement, a lack of parental warmth and parent–child conflict (Burt et al., 2005; Caspi et al., 2004; Stanger et al., 2004). Dopaminergic pathways are also critically involved in affiliative (e.g., maternal care) and other reward-related behaviours (Depue & Collins, 1999; Mileva-Seitz et al., 2012). The rationale for including in the CSG genetic loci implicated in stress response is based on studies that have reported an association between cortisol levels and aggression in adolescents (Gao et al., 2009; Poustka et al., 2010; Matthys et al., 2013). In addition, the neural substrates of affiliation (parenting behaviours) are closely interrelated to the stress response, salience, and reward pathways (Leckman & Herman, 2002; Lim & Young, 2006; Gordon et al., 2011). The goals of this preliminary study were: (1) to construct a latent phenotypic antisocial variable using all, or a subset, of 15 indicators of delinquency, antisocial behaviour and drug use collected as part of the 15 year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation program in Elmira, New York (Olds et al., 1998); (2) to define a novel CSG that includes genetic loci that have been implicated in affiliative behaviours and stress response as delinquency and drug use; (3) to carry out initial univariate analyses of each of the informative single nucleotide polymorphisms (SNPs) within the CSG; (4) to perform a quasi-bootstrapping procedure to construct an initial latent genetic variable; (5) to refine and extend the latent genetic variable by reassessing each of the remaining informative SNPs one at a time leading to a final model where both latent genetic and phenotypic variables are incorporated simultaneously; (6) to determine if there is a continuous relationship between the number of risk alleles and scores from the latent antisocial variable; and (7) to identify biological pathways and processes that are over-represented by all of the genes in the CSG that were implicated in this analysis. A flow chart of these steps and procedures is presented in Figure 1. Figure 1 Flow Chart