Segregation analysis of attention deficit hyperactivity disorder

Brion S. Maher, Mary L. Marazita, Howard B. Moss, Michael M. Vanyukov

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


We performed segregation analysis on 495 nuclear families, ascertained for the father's substance abuse diagnosis, in an attempt to determine the role of genetic and other influences in determining the variability of DSM- III-R-defined attention deficit hyperactivity disorder (ADHD). For our analyses, ADHD was treated as a quantitative variable, utilizing the semicontinuous scale provided by the 15-item symptom count within DSM-III-R. Analyses consisted of both class A and class D regressive models for which covariate effects (socioeconomic status) and sex dependence were estimated. Segregation analysis of the quantitative trait (ADHD symptom count) in the entire data set supported a transmissible non-Mendelian major effect. Models which were sex-dependent and included covariate effects provided the best fit to the data. In addition, similar analyses were performed on a 130-nuclear family subgroup of the data set in which at least one of the members of the nuclear family met DSM-III-R diagnostic criteria for ADHD. The sex-dependent Mendelian codominant model was best supported by the data, while other models could be rejected. Incorporating covariate effects did not provide a better fit for the data. Thus, this study is consistent with Mendelian transmission of ADHD symptom count in a clinically relevant population. Overall, our results support the presence of a heritable continuous trait of which ADHD represents an extreme.

Original languageEnglish (US)
Pages (from-to)71-78
Number of pages8
JournalAmerican Journal of Medical Genetics - Neuropsychiatric Genetics
Issue number1
StatePublished - Feb 5 1999
Externally publishedYes


  • Attention deficit hyperactivity disorder
  • Genetics
  • Segregation analysis

ASJC Scopus subject areas

  • Genetics(clinical)
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience


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