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AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Behavioural Neurology"],"published-print":{"date-parts":[[2021,7,14]]},"abstract":"<jats:p>Objectives. Alzheimer\u2019s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer\u2019s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training\/validation group and one test group at a ratio of 9\u2009:\u20091. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mn>98.78<\/a:mn>\n                        <a:mo>\u00b1<\/a:mo>\n                        <a:mn>1.50<\/a:mn>\n                        <a:mi>%<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mn>98.39<\/c:mn>\n                        <c:mi>%<\/c:mi>\n                        <c:mo>\u00b1<\/c:mo>\n                        <c:mn>2.50<\/c:mn>\n                        <c:mi>%<\/c:mi>\n                     <\/c:math>\n                  <\/jats:inline-formula>, and <jats:inline-formula>\n                     <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mn>99.44<\/e:mn>\n                        <e:mi>%<\/e:mi>\n                        <e:mo>\u00b1<\/e:mo>\n                        <e:mn>1.11<\/e:mn>\n                        <e:mi>%<\/e:mi>\n                     <\/e:math>\n                  <\/jats:inline-formula> using the DLG model, while <jats:inline-formula>\n                     <g:math xmlns:g=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                        <g:mn>71.38<\/g:mn>\n                        <g:mi>%<\/g:mi>\n                        <g:mo>\u00b1<\/g:mo>\n                        <g:mn>0.63<\/g:mn>\n                        <g:mi>%<\/g:mi>\n                     <\/g:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <i:math xmlns:i=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                        <i:mn>63.13<\/i:mn>\n                        <i:mi>%<\/i:mi>\n                        <i:mo>\u00b1<\/i:mo>\n                        <i:mn>2.87<\/i:mn>\n                        <i:mi>%<\/i:mi>\n                     <\/i:math>\n                  <\/jats:inline-formula>, and <jats:inline-formula>\n                     <k:math xmlns:k=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\">\n                        <k:mn>85.59<\/k:mn>\n                        <k:mi>%<\/k:mi>\n                        <k:mo>\u00b1<\/k:mo>\n                        <k:mn>6.66<\/k:mn>\n                        <k:mi>%<\/k:mi>\n                     <\/k:math>\n                  <\/jats:inline-formula> using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.<\/jats:p>","DOI":"10.1155\/2021\/3359103","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T18:35:05Z","timestamp":1626374105000},"page":"1-15","source":"Crossref","is-referenced-by-count":18,"title":["Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer\u2019s Disease or Mild Cognitive Impairment"],"prefix":"10.1155","volume":"2021","author":[{"given":"Lanlan","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeying","family":"Yang","sequence":"additional","affiliation":[{"name":"LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, 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