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  • Title: Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children.
    Author: Groen-Blokhuis MM, Middeldorp CM, Kan KJ, Abdellaoui A, van Beijsterveldt CE, Ehli EA, Davies GE, Scheet PA, Xiao X, Hudziak JJ, Hottenga JJ, Psychiatric Genomics Consortium ADHD Working Group, Neale BM, Boomsma DI.
    Journal: J Am Acad Child Adolesc Psychiatry; 2014 Oct; 53(10):1123-9.e6. PubMed ID: 25245356.
    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.
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