Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5. Issue 6 (9th June 2021)

• Record Type: Journal Article
• Authors: Liu, Lu; Feng, Xikang; Li, Haimei; Cheng Li, Shuai; Qian, Qiujin; Wang, Yufeng
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-06-09
• Subjects: ADHD identification -- deep learning -- saliency map -- GWAS; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab207
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.