Browsing Upstate Medical University by Author "Frazier-Wood, Alexis C."
Neuropsychological intra-individual variability explains unique genetic variance of ADHD and shows suggestive linkage to chromosomes 12, 13, and 17Frazier-Wood, Alexis C.; Bralten, Janita; Arias-Vasquez, Alejandro; Luman, Marjolein; Ooterlaan, Jaap; Sergeant, Joseph; Faraone, Stephen V.; Buitelaar, Jan; Franke, Barbara; Kuntsi, Jonna; et al. (Wiley, 2012-01-05)Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neuropsychiatric disorder that is usually accompanied by neuropsychological impairments. The use of heritable, psychometrically robust traits that show association with the disorder of interest can increase the power of gene-finding studies. Due to the robust association of intra-individual variability with ADHD on a phenotypic and genetic level, intra-individual variability is a prime candidate for such an attempt. We aimed to combine intra-individual variability measures across tasks into one more heritable measure, to examine the relatedness to other cognitive factors, and to explore the genetic underpinnings through quantitative trait linkage analysis. Intra-individual variability measures from seven tasks were available for 238 ADHD families (350 ADHD-affected and 195 non-affected children) and 147 control families (271 children). Intra-individual variability measures from seven different tasks shared common variance and could be used to construct an aggregated measure. This aggregated measure was largely independent from other cognitive factors related to ADHD and showed suggestive linkage to chromosomes 12q24.3 (LOD ¼ 2.93), 13q22.2 (LOD ¼ 2.36), and 17p13.3 (LOD ¼ 2.00). A common intra-individual variability construct can be extracted from very diverse neuropsychological tasks; this construct taps into unique genetic aspects of ADHD and may relate to loci conferring risk for ADHD (12q24.3 and 17p13.3) and possibly autism (12q24.3). Given that joining of data across sites boosts the power for genetic analyses, our findings are promising in showing that intra-individual variability measures are viable candidates for across site analyses where different tasks have been used.