RESUMO
Suicide is a complex trait resulting from the interaction of several predisposing factors, among which genes seem to play an important role. Alterations in the noradrenergic system have been observed in postmortem brain studies of suicide victims when compared to controls. The purpose of this study was to test the hypothesis that genetic variants of the alpha(2A) adrenergic receptor gene are implicated in suicide and/or have a modulatory effect on personality traits that are believed to mediate suicidal behavior. We studied a sample of suicides (N=110) and control subjects (N=130) for genetic variation at four loci, including three in the promoter region (g-1800t, c-1291 g and the g-261a) of the alpha(2A) adrenergic receptor gene, and a potentially functional locus, N251K, which leads to an amino acid change (asparagine to lysine). No significant differences were observed at the promoter loci in terms of allelic or genotypic distribution between suicides and controls. However, analysis of the functional polymorphism N251K revealed that the 251 K allele was only present among suicides, though only three suicide cases had this allele, two of which were homozygous. These results are preliminary. If confirmed, they suggest that variation at the alpha(2A) adrenergic receptor gene may play a role in a small proportion of suicide cases.
Assuntos
Transtornos Disruptivos, de Controle do Impulso e da Conduta/epidemiologia , Transtornos Disruptivos, de Controle do Impulso e da Conduta/genética , Transtornos da Personalidade/epidemiologia , Transtornos da Personalidade/genética , Receptores Adrenérgicos alfa 2/genética , Suicídio/estatística & dados numéricos , Adulto , Primers do DNA/genética , Manual Diagnóstico e Estatístico de Transtornos Mentais , Transtornos Disruptivos, de Controle do Impulso e da Conduta/diagnóstico , Expressão Gênica , Frequência do Gene , Triagem de Portadores Genéticos , Genótipo , Humanos , Pessoa de Meia-Idade , Transtornos da Personalidade/diagnóstico , Reação em Cadeia da Polimerase , Suicídio/psicologiaRESUMO
Ordinary differential equation models in biology often contain a large number of parameters that must be determined from measurements by parameter estimation. For a parameter estimation procedure to be successful, there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not uniquely structurally identifiable with the given set of outputs selected as measurements. In designing an experiment for the purpose of parameter estimation, given a set of feasible but resource-consuming measurements, it is useful to know which ones must be included in order to obtain an identifiable system, or whether the system is unidentifiable from the feasible measurement set. We have developed an algorithm that, from a user-provided set of variables and parameters or functions of them assumed to be measurable or known, determines all subsets that when used as outputs give a locally structurally identifiable system and are such that any output set for which the system is structurally identifiable must contain at least one of the calculated subsets. The algorithm has been implemented in Mathematica and shown to be feasible and efficient. We have successfully applied it in the analysis of large signalling pathway models from the literature.