The identification of family subtype based on the assessment of subclinical levels of psychosis in relatives

Genetic Risk and Outcome of Psychosis (GROUP) Investigators, Jim van Os

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Schizophrenia is a complex psychiatric disorder characterized by high phenotypic heterogeneity. Previous studies have distinguished between familial and sporadic forms of schizophrenia and have suggested clinical differentiation between patients and relatives from sporadic and multiplex families. We will introduce a more refined method to distinguish between family subtypes based on psychosis dimension profiles in the relatives of schizophrenia patients.Positive, negative, disorganization, mania, and depression scores were assessed in 1,392 relatives. Mixed Model Latent Class Analysis was used to identify family subtypes. A family subtype is a relatively homogeneous group of families with similar symptom profiles in the relatives in these families. Next, we investigated in 616 schizophrenia patients whether family subtype was associated with symptom profiles, IQ, cannabis dependence/abuse, or age of onset of psychosis.Based on the data of relatives, we identified two different family types: "healthy" and "at risk for psychiatric disorder". Patients from at risk families obtained higher positive scores compared to patients from healthy families (Wald(1)?=6.6293, p=0.010). No significant differences were found in any of the remaining variables.Our findings confirm the existence of high-risk families and although we did not establish an etiological basis for the distinction between family types, genetic studies might reveal whether family subtype is associated with genetic heterogeneity.
Original languageEnglish
Pages (from-to)71
JournalBMC Psychiatry
Volume12
DOIs
Publication statusPublished - 3 Jul 2012

Keywords

  • Family subtype
  • Familial loading
  • Multiplex
  • Sporadic
  • Phenotypic heterogeneity
  • Schizophrenia
  • Controls
  • Factor analysis
  • Latent class analysis
  • Mixed model latent class analysis

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