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CHIP: Defining a dimension of the vulnerability to attention deficit hyperactivity disorder (ADHD) using sibling and individual data of children in a community‐based sample
Author(s) -
Curran Sarah,
Rijsdijk Fruhling,
Martin Neilson,
Marusic Katja,
Asherson Philip,
Taylor Eric,
Sham Pak
Publication year - 2003
Publication title -
american journal of medical genetics part b: neuropsychiatric genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 126
eISSN - 1552-485X
pISSN - 1552-4841
DOI - 10.1002/ajmg.b.20002
Subject(s) - heritability , attention deficit hyperactivity disorder , sibling , sample (material) , correlation , psychology , rating scale , genetic correlation , sample size determination , trait , missing data , statistics , clinical psychology , developmental psychology , medicine , computer science , mathematics , genetic variation , genetics , biology , population , chemistry , geometry , environmental health , chromatography , programming language
We are taking a quantitative trait approach to the molecular genetic study of attention deficit hyperactivity disorder (ADHD) using a truncated case‐control association design. An epidemiological sample of children aged 5 to 15 years was evaluated for symptoms of ADHD using a parent rating scale. Individuals scoring high or low on this scale were selected for further investigation with additional questionnaires and DNA analysis. Data in studies like this are typically complicated. In the study reported on here, individuals have from 1 to 4 questionnaires completed on them and the sample is composed of a mixture of singletons and siblings. In this paper, we describe how we used a genetic hierarchical model to fit our data, together with a twin dataset, in order to estimate genetic factor loadings. Correlation matrices were estimated for our data using a maximum likelihood approach to account for missing data. We describe how we used these results to create a composite score, the heritability of which was estimated to be acceptably high using the twin dataset. This score measures a quantitative dimension onto which molecular genetic data will be mapped. © 2003 Wiley‐Liss, Inc.