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BackgroundIncreased reaction time variability (RTV) on cognitive tasks requiring a speeded response is characteristic of several psychiatric disorders. In attention deficit hyperactivity disorder (ADHD), the association with RTV is strong phenotypically and genetically, yet high RTV is not a stable impairment but shows ADHD-sensitive improvement under certain conditions, such as those with rewards. The state regulation theory proposed that the RTV difference score, which captures change from baseline to a rewarded or fast condition, specifically measures ‘state regulation’. By contrast, the interpretation of RTV baseline (slow, unrewarded) scores is debated. We aimed to investigate directly the degree of phenotypic and etiological overlap between RTV baseline and RTV difference scores.MethodWe conducted genetic model fitting analyses on go/no-go and fast task RTV data, across task conditions manipulating rewards and event rate, from a population-based twin sample (n=1314) and an ADHD and control sibling-pair sample (n=1265).ResultsPhenotypic and genetic/familial correlations were consistently high (0.72–0.98) between RTV baseline and difference scores, across tasks, manipulations and samples. By contrast, correlations were low between RTV in the manipulated condition and difference scores. A comparison across two different go/no-go task RTV difference scores (slow-fast/slow-incentive) showed high phenotypic and genetic/familial overlap (r = 0.75–0.83).ConclusionsOur finding that RTV difference scores measure largely the same etiological process as RTV under baseline condition supports theories emphasizing the malleability of the observed high RTV. Given the statistical shortcomings of difference scores, we recommend the use of RTV baseline scores for most analyses, including genetic analyses. [ABSTRACT FROM PUBLISHER]

Background. Oppositional defiant disorder (ODD) is frequently co-occurring with attention deficit hyperactivity disorder (ADHD) in children and adolescents. Because ODD is a precursor of later conduct disorder (CD) and affective disorders, early diagnostic identification is warranted. Furthermore, the predictability of three recently confirmed ODD dimensions (ODD-irritable, ODD-headstrong and ODD-hurtful) may assist clinical decision making. Method. Receiver-operating characteristic (ROC) analysis was used in order to test the diagnostic accuracy of the Conners' Parent Rating Scale revised (CPRS-R) and the parent version of the Strength and Difficulties Questionnaire (PSDQ) in the prediction of ODD in a transnational sample of 1093 subjects aged 5-17 years from the International Multicentre ADHD Genetics study. In a second step, the prediction of three ODD dimensions by the same parent rating scales was assessed by backward linear regression analyses. Results. ROC analyses showed adequate diagnostic accuracy of the CPRS-R and the PSDQ in predicting ODD in this ADHD sample. Furthermore, the three-dimensional structure of ODD was confirmed by confirmatory factor analysis and the CPRS-R emotional lability scale significantly predicted the ODD irritable dimension. Conclusions. The PSDQ and the CPRS-R are both suitable screening instruments in the identification of ODD. The emotional lability scale of the CPRS-R is an adequate predictor of irritability in youth referred for ADHD. [ABSTRACT FROM AUTHOR]

Objective: Because multiple genes with small effect sizes are assumed to play a role in attention-deficit/hyperactivity disorder (ADHD) etiology, considering multiple variants within the same analysis likely increases the total explained phenotypic variance, thereby boosting the power of genetic studies. This study investigated whether pathway-based analysis could bring scientists closer to unraveling the biology of ADHD., Method: The pathway was described as a predefined gene selection based on a well-established database or literature data. Common genetic variants in pathways involved in dopamine/norepinephrine and serotonin neurotransmission and genes involved in neuritic outgrowth were investigated in cases from the International Multicentre ADHD Genetics (IMAGE) study. Multivariable analysis was performed to combine the effects of single genetic variants within the pathway genes. Phenotypes were DSM-IV symptom counts for inattention and hyperactivity/impulsivity (n = 871) and symptom severity measured with the Conners Parent (n = 930) and Teacher (n = 916) Rating Scales., Results: Summing genetic effects of common genetic variants within the pathways showed a significant association with hyperactive/impulsive symptoms ((p)empirical = .007) but not with inattentive symptoms ((p)empirical = .73). Analysis of parent-rated Conners hyperactive/impulsive symptom scores validated this result ((p)empirical = .0018). Teacher-rated Conners scores were not associated. Post hoc analyses showed a significant contribution of all pathways to the hyperactive/impulsive symptom domain (dopamine/norepinephrine, (p)empirical = .0004; serotonin, (p)empirical = .0149; neuritic outgrowth, (p)empirical = .0452)., Conclusion: The present analysis shows an association between common variants in 3 genetic pathways and the hyperactive/impulsive component of ADHD. This study demonstrates that pathway-based association analyses, using quantitative measurements of ADHD symptom domains, can increase the power of genetic analyses to identify biological risk factors involved in this disorder., (Copyright © 2013 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: To examine the factor structure of attention-deficit/hyperactivity disorder (ADHD) in a clinical sample of 1,373 children and adolescents with ADHD and their 1,772 unselected siblings recruited from different countries across a large age range. Hierarchical and correlated factor analytic models were compared separately in the ADHD and sibling samples, across three different instruments and across parent and teacher informants. Specific consideration was given to factorial invariance analyses across different ages and different countries in the ADHD sample., Method: A sample of children and adolescents between 5 and 17 years of age with ADHD and their unselected siblings was assessed. Participants were recruited from seven European countries and Israel. ADHD symptom data came from a clinical interview with parents Parental Account of Childhood Symptoms and questionnaires from parents and teachers (Conners Parent and Teacher)., Results: A hierarchical general factor model with two specific factors best represented the structure of ADHD in both the ADHD and unselected sibling groups, and across informants and instruments. The model was robust and invariant with regard to age differences in the ADHD sample. The model was not strongly invariant across different national groups in the ADHD sample, likely reflecting severity differences across the different centers and not any substantial difference in the clinical presentation of ADHD., Conclusions: The results replicate previous studies of a model with a unitary ADHD component and separable specific traits of inattention and hyperactivity/impulsivity. The unique contribution of this study was finding support for this model across a large developmental and multinational/multicultural sample and its invariance across ages., (© 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.)

As part of the International Multi-centre ADHD Genetics project we completed an affected sibling pair study of 142 narrowly defined Diagnostic and Statistical Manual of Mental Disorders, fourth edition combined type attention deficit hyperactivity disorder (ADHD) proband-sibling pairs. No linkage was observed on the most established ADHD-linked genomic regions of 5p and 17p. We found suggestive linkage signals on chromosomes 9 and 16, respectively, with the highest multipoint nonparametric linkage signal on chromosome 16q23 at 99 cM (log of the odds, LOD=3.1) overlapping data published from the previous UCLA (University of California, Los Angeles) (LOD>1, approximately 95 cM) and Dutch (LOD>1, approximately 100 cM) studies. The second highest peak in this study was on chromosome 9q22 at 90 cM (LOD=2.13); both the previous UCLA and German studies also found some evidence of linkage at almost the same location (UCLA LOD=1.45 at 93 cM; German LOD=0.68 at 100 cM). The overlap of these two main peaks with previous findings suggests that loci linked to ADHD may lie within these regions. Meta-analysis or reanalysis of the raw data of all the available ADHD linkage scan data may help to clarify whether these represent true linked loci.

The International Multi-Centre ADHD Gene sample consists of 674 families from eight countries (Belgium, England, Germany, Holland, Ireland, Israel, Spain, and Switzerland) ascertained from clinics for combined-type attention definity hyperactivity disorder in an offspring. 863 SNPs were successfully genotyped across 47 autosomal genes implicated in psychiatric disorders yielding a single nucleotide polymorphism (SNP) density of approximately one SNP per 2.5 kb. A global test of heterogeneity showed 269 SNPs nominally significant (expected 43). Inclusion of the Israeli population accounted for approximately 70% of these nominally significant tests. Hardy-Weinberg equilibrium tests suggest that combining all these populations would induce stratification, but that the Northern European populations (Belgium, England, Germany, Holland, and Ireland) could be appropriate. Tag SNPs were generated using pair-wise and aggressive tagging from Carlson et al. [2004] and de Bakker et al. [2005], respectively, in each population and applied to the other populations. Cross-population performance across Northern Europe was consistent with within population comparisons. Smaller sample size for each population tended to yield more problems for the generation of aggressive tags and the application of pair-wise tags. Any case-control sample employing an Israeli sample with Northern Europeans must consider stratification. A Northern European tag set, however, appears to be appropriate for capturing the variation across populations.