Maria M Groen-Blokhuis1, Christel M Middeldorp2, Kees-Jan Kan3, Abdel Abdellaoui4, Catharina E M van Beijsterveldt3, Erik A Ehli5, Gareth E Davies5, Paul A Scheet6, Xiangjun Xiao7, James J Hudziak8, Jouke-Jan Hottenga3, Ben M Neale9, Dorret I Boomsma10. 1. VU University Amsterdam, the Netherlands; EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam. 2. VU University Amsterdam, the Netherlands; Neuroscience Campus Amsterdam, VU University Amsterdam; GGZinGeest/ VU University Medical Center. 3. VU University Amsterdam, the Netherlands. 4. VU University Amsterdam, the Netherlands; Neuroscience Campus Amsterdam, VU University Amsterdam. 5. Avera Institute for Human Genetics, Sioux Falls, SD. 6. University of Texas M. D. Anderson Cancer Center, Houston. 7. Dartmouth Medical School, Hanover, NH. 8. Vermont Center for Children, Youth, and Families and University of Vermont College of Medicine, Burlington, VT. 9. Massachusetts General Hospital and Harvard Medical School, Boston, and the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA. 10. EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam; Neuroscience Campus Amsterdam, VU University Amsterdam. Electronic address: dorret@psy.vu.nl.
Abstract
OBJECTIVE: Clinically, attention-deficit/hyperactivity disorder (ADHD) is characterized by hyperactivity, impulsivity, and inattention and is among the most common childhood disorders. These same traits that define ADHD are variable in the general population, and the clinical diagnosis may represent the extreme end of a continuous distribution of inattentive and hyperactive behaviors. This hypothesis can be tested by assessing the predictive value of polygenic risk scores derived from a discovery sample of ADHD patients in a target sample from the general population with continuous scores of inattention and hyperactivity. In addition, the genetic overlap between ADHD and continuous ADHD scores can be tested across rater and age. METHOD: The Psychiatric Genomics Consortium has performed the largest genome-wide analysis (GWA) study of ADHD so far, including 5,621 clinical patients and 13,589 controls. The effects sizes of single nucleotide polymorphisms (SNPs) estimated in this meta-analysis were used to obtain individual polygenic risk scores in an independent population-based cohort of 2,437 children from the Netherlands Twin Register. The variance explained in Attention Problems (AP) scale scores by the polygenic risk scores was estimated by linear mixed modeling. RESULTS: The ADHD polygenic risk scores significantly predicted both parent and teacher ratings of AP in preschool- and school-aged children. CONCLUSION: These results indicate genetic overlap between a diagnosis of ADHD and AP scale scores across raters and age groups and provides evidence for a dimensional model of ADHD. Future GWA studies on ADHD can likely benefit from the inclusion of population-based cohorts and the analysis of continuous scores.
OBJECTIVE: Clinically, attention-deficit/hyperactivity disorder (ADHD) is characterized by hyperactivity, impulsivity, and inattention and is among the most common childhood disorders. These same traits that define ADHD are variable in the general population, and the clinical diagnosis may represent the extreme end of a continuous distribution of inattentive and hyperactive behaviors. This hypothesis can be tested by assessing the predictive value of polygenic risk scores derived from a discovery sample of ADHDpatients in a target sample from the general population with continuous scores of inattention and hyperactivity. In addition, the genetic overlap between ADHD and continuous ADHD scores can be tested across rater and age. METHOD: The Psychiatric Genomics Consortium has performed the largest genome-wide analysis (GWA) study of ADHD so far, including 5,621 clinical patients and 13,589 controls. The effects sizes of single nucleotide polymorphisms (SNPs) estimated in this meta-analysis were used to obtain individual polygenic risk scores in an independent population-based cohort of 2,437 children from the Netherlands Twin Register. The variance explained in Attention Problems (AP) scale scores by the polygenic risk scores was estimated by linear mixed modeling. RESULTS: The ADHD polygenic risk scores significantly predicted both parent and teacher ratings of AP in preschool- and school-aged children. CONCLUSION: These results indicate genetic overlap between a diagnosis of ADHD and AP scale scores across raters and age groups and provides evidence for a dimensional model of ADHD. Future GWA studies on ADHD can likely benefit from the inclusion of population-based cohorts and the analysis of continuous scores.
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