Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined effect of multiple variants with insignificant P-values. Here, we proposed a convolutional neural network (CNN) to classify 1033 individuals diagnosed with ADHD from 950 healthy controls according to their genomic data. The model takes the single nucleotide polymorphism (SNP) loci of P-values $\le{1\times 10^{-3}}$, i.e. 764 loci, as inputs, and achieved an accuracy of 0.9018, AUC of 0.9570, sensitivity of 0.8980 and specificity of 0.9055. By incorporating the saliency analysis for the deep learning network, a total of 96 candidate genes were found, of which 14 genes have been reported in previous ADHD-related studies. Furthermore, joint Gene Ontology enrichment and expression Quantitative Trait Loci analysis identified a potential risk gene for ADHD, EPHA5 with a variant of rs4860671. Overall, our CNN deep learning model exhibited a high accuracy for ADHD classification and demonstrated that the deep learning model could capture variants’ combining effect with insignificant P-value, while GWAS fails. To our best knowledge, our model is the first deep learning method for the classification of ADHD with SNPs data.
Background: Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by lower academic achievements related to executive dysfunction, but the correlations remain unclear. The current study aimed to elucidate to what extent executive functions affect academic achievements in pediatric ADHD. Results:The results showed that mathematical achievements, reading comprehension achievements, subtraction and word semantics were all correlated with digit span and conversion. Reading comprehension achievements also had relationships with spatial span. In addition, reading comprehension achievements, subtraction and word semantics had negative relationships with the colour interference time and the semantic interference time. Furthermore, central executive function played significant meditating effects on mathematical achievements (dindirect effect=-0.04, P<0.05), subtraction (dindirect effect=-0.06, P=0.01) and word semantics (dindirect effect=-0.06, P<0.05). Inhibition played significant meditating effects on subtraction (dindirect effect=-0.03, P<0.05) and word semantics (dindirect effect=-0.06, P=0.01). Conversion had a significant mediating effect on word semantics (dindirect effect=-0.02, P=0.01). Conclusions:The findings suggested that central executive function, inhibition and conversion may have more important meditating effects on academic achievements of children with ADHD than other components of executive functions. Targeted executive function training should be used to effectively improve the targeted academic achievements of children with ADHD.
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