The curse of dimensionality refers to the fact that difficulties in statistical analysis increase exponentially rather than linearly as a function of the number of variables, often precluding use of classic methods in multivariate settings [7]. The fact that there are many more individual criteria than there are composite PDs they combine to (80 vs. 10) exposes the analyst to the “curse of dimensionality”. Recent modifications of the classic methods have attempted to bring high-dimensional datasets into a more manageable and workable framework. Here we specifically make use of the Elastic Net (EN) regression method that modifies the more commonly applied regression methods [8–10]. In addition to its good predictive performance, EN regression offers a competitive alternative to the widely criticized stepwise-regression variable selection methods [11,12]. While EN regression allows us to take in account potential confounding among the PD criteria, a twin design is used to assess possible confounded etiologic mechanisms.
