4.7 Article

Separation of genetic influences on attention deficit hyperactivity disorder symptoms and reaction time performance from those on IQ

期刊

PSYCHOLOGICAL MEDICINE
卷 40, 期 6, 页码 1027-1037

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S003329170999119X

关键词

ADHD; genetics; IQ; reaction time; reaction time variability

资金

  1. Wellcome Trust [GR070345MF]
  2. Economic and Social Research Council
  3. Medical Research Council [G9817803B] Funding Source: researchfish

向作者/读者索取更多资源

Background. Attention deficit hyperactivity disorder (ADHD) shows a strong phenotypic and genetic association with reaction time (RI) variability, considered to reflect lapses in attention. Yet we know little about whether this aetiological pathway is shared with other affected cognitive processes in ADHD, such as lower IQs or the generally slower responses (mean RTs). We aimed to address the question of whether a shared set of genes exist that influence RI variability, mean RI, IQ and ADHD symptom scores, or whether there is evidence of separate aetiological pathways. Method. Multivariate structural equation modelling on cognitive tasks data (providing RT data), IQ and ADHD ratings by parents and teachers collected on general population sample of 1314 twins, at ages 7-10 years. Results. Multivariate structural equation models indicated that the shared genetic influences underlying both ADHD symptom scores and RI variability are also shared with those underlying mean RT, with both types of RI data largely indexing the same underlying liability. By contrast, the shared genetic influences on ADHD symptom scores and RI variability (or mean RI) are largely independent of the genetic influences that ADHD symptom scores share with IQ. Conclusions. The finding of unique aetiological pathways between IQ and RT data, but shared components between mean RI, RI variability and ADHD symptom scores, illustrates key influences in the genetic architecture of the cognitive and energetic processes that underlie the behavioural symptoms of ADHD. In addition, the multivariate genetic model fitting findings provide valuable information for future molecular genetic analyses.

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