We propose that the power to identify EXT-related genes may be enhanced by using these measures in concert, effectively creating a multivariate endophenotype (e.g. Arya, Duggirala, Williams, Almasy, & Blangero, 2001; Frederick & Iacono, 2006; Iacono, Carlson, & Malone, 2000; Price et al., 2006), by capitalizing on the covariance among the multiple measures and EXT. The goal of this report was to explore a multivariate approach to identifying electrophysiological endophenotypes related to EXT psychopathology, using measures identified in the literature as promising individual endophenotypes for EXT. We specifically sought to address the following aims: 1) assess the magnitude of the phenotypic and genetic associations between each endophenotype and EXT, 2) determine if there is significant shared genetic variance among the multiple endophenotypes and EXT, and 3) determine if any of the endophenotypes share unique genetic variance with EXT (i.e. variance which is not also shared with the other endophenotypes). Also, because there is evidence for differential relationships between the brain activity measures and EXT disorders, we assessed potential gender effects. Finding significant relationships among the electrophysiological endophenotypes and EXT would
