Abstract
Genome-wide association studies (GWAS) of attention-deficit/hyperactivity disorder (ADHD) offer the benefit of a hypothesis-free approach to measuring the quantitative effect of genetic variants on affection status. Generally the findings of GWAS relying on ADHD status have been non-significant, but the one study using quantitative measures of symptoms found SLC9A9 and SLC6A1 were associated with inattention and hyperactivity-impulsivity. Accordingly, we performed a GWAS using quantitative measures of each ADHD subtype measured with the Strengths and Weaknesses of ADHD and Normal Behaviour (SWAN) scale in two community-based samples. This scale captures the full range of attention and kinetic behavior; from high levels of attention and appropriate activity to the inattention and hyperactivity-impulsivity associated with ADHD within two community-based samples. Our discovery sample comprised 1,851 participants (mean age = 22.8 years [4.8]; 50.6% female), while our replication sample comprised 155 participants (mean age = 26.3 years [3.1]; 68.4% females). Age, sex, age × sex, and age² were included as covariates and the results from each sample were combined using meta-analysis, then analyzed with a gene-based test to estimate the combined effect of markers within genes. We compare our results with markers that have previously been found to have a strong association with ADHD symptoms. Neither the GWAS nor subsequent meta-analyses yielded genome-wide significant results; the strongest effect was observed at rs2110267 (4.62 × 10ˉ⁷) for symptoms of hyperactivity-impulsivity. The strongest effect in the gene-based test was for GPR139 on symptoms of inattention (6.40 × 10ˉ⁵). Replication of this study with larger samples will add to our understanding of the genetic etiology of ADHD.
| Original language | English |
|---|---|
| Pages (from-to) | 560-574 |
| Journal | Twin Research and Human Genetics |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2013 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Quantitative Genetics (incl Disease and Trait Mapping Genetics)
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