An investigation of the psychopathy construct and its (novel) correlates in non-clinical samples

A substantial research literature exists regarding the psychopathy construct in forensic populations, but more recently, the construct has been extended to non-clinical populations. The purpose of the present dissertation was to investigate the content and the correlates of the psychopathy construct, with a particular focus on addressing gaps and controversies in the literature. In Study 1, the role of low anxiety in psychopathy was investigated, as some authors have proposed that low anxiety is integral to the psychopathy construct. Participants (n = 346) responded to two self-report psychopathy scales, the SRP-III and the PPI-R, as well as measures of temperament, personality, and antisociality. Of particular interest was the PPI-R Stress Immunity sub scale, which represents low anxiety content. I t was found that Stress Immunity was not correlated with SRP-III psychopathy, nor did it share common personality or temperament correlates or contribute to the prediction of anti sociality. From Study 1, it was concluded that it was unlikely that low anxiety is a central feature of the psychopathy construct. In Study 2, the relationship between SRP-III psychopathy and Ability Emotional Intelligence (Le., Emotional Intelligence measured as an ability, rather than as a self-report personality trait-like characteristic) was investigated, to determine whether psychopathy is be s t seen as a syndrome characterized by emotional deficits or by the ability to skillfully manipulate and prey upon the others' emotions. A negative correlation between the two constructs was found, suggesting that psychopathy is best characterized by deficits in perceiving, facilitating, managing, and understanding emotions. In Study 3, sex differences in the sexual behavior (i.e., promiscuity, age of first sexual behaviors, extradyadic sexual relations) and appearance-related esteem (i.e., body shame,appearance anxiety, self-esteem) correlates of SRP-III psychopathy were investigated. The sexual behavior correlates of psychopathy were quite similar for men and women, but the esteem correlates were very different, such that high psychopathy in men was related to high esteem, whereas high psychopathy in women was generally related to low esteem. This sex difference was difficult to interpret in that it was not mediated by sexual behavior, suggesting that further exploration of this topic is warranted. Together, these three studies contribute to our understanding of non-clinical psychopathy, indicating that low anxiety is likely not part of the construct, that psychopathy is related to low levels of ability in Emotional Intelligence, and that psychopathy is an important predictor of behavior, ability, and beliefs and feelings about the self

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.