could be multi-dimensional rather than unidimensional (Figure 1b–1c), and the GV effect could be specific to one of the phenotypes, rather than on the latent dimension (Figure 1d–1e). Recently, the field of psychology has witnessed a shift towards network models, in which relations between individual phenotypes are not believed to result from shared causal latent factors, but rather originate in direct causal influences between phenotypes over time [8]–[10]. For instance, from a network perspective, symptoms like worrying, sleeplessness and agitation are not viewed as manifestations of the latent dimension depression, but as directly and causally related: worrying interferes with sleep, and lack of sleep causes agitation. In such network models, which obviate the need to invoke latent dimensions, each phenotype could be affected by different GVs (Figure 1f). In all these alternative genotype-phenotype models, univariate conceptualisations like sum scores and case-control status result in substantial loss of power to detect underlying GVs.
