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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\u2019 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.<\/jats:p>","DOI":"10.1093\/bib\/bbab207","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T11:21:18Z","timestamp":1620818478000},"source":"Crossref","is-referenced-by-count":26,"title":["Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5"],"prefix":"10.1093","volume":"22","author":[{"given":"Lu","family":"Liu","sequence":"first","affiliation":[{"name":"Peking University Sixth Hospital\/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xikang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an, 710072, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haimei","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University Sixth Hospital\/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, Ministry of Health (Peking University), 100191, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Cheng Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiujin","family":"Qian","sequence":"additional","affiliation":[{"name":"Peking University Sixth Hospital\/Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & the Key Laboratory of Mental Health, 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