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.
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.
Curran
Sarah
S
Social Developmental Genetic Psychiatric Research Centre, Institute of Psychiatry, De Crespigny Park, London, UK. s.curran@iop.kcl.ac.uk
Rijsdijk
Fruhling
F
Martin
Neilson
N
Marusic
Katja
K
Asherson
Philip
P
Taylor
Eric
E
Sham
Pak
P
D000293:Adolescent; D001289:Attention Deficit Disorder with Hyperactivity / Q000453:epidemiology* / Q000235:genetics*; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D004198:Disease Susceptibility; D005260:Female; D006801:Humans; D008297:Male; D008957:Models, Genetic; D012111:Residence Characteristics; D012720:Severity of Illness Index; D035781:Siblings; D013223:Statistics as Topic; D011795:Surveys and Questionnaires; D014427:Twins