Can Deep Learning Improve Genomic Prediction of Complex Human Traits?
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic pre...
| Published in: | Genetics Vol. 210; no. 3; pp. 809 - 820 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Oxford University Press / USA
Nov2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132894979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132894979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Nov2018 vid: 210 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132894979 10.1534/genetics.118.301298 132894979 ppf: 809 ppct: 11 formats: tig: atl: Can Deep Learning Improve Genomic Prediction of Complex Human Traits? aug: au: Bellot, Pau de los Campos, Gustavo Pérez-Enciso, Miguel affil: Centre for Research in Agricultural Genomics (CRAG), Consejo Superior de Investigaciones Científicas (CSIC) - Institut de Recerca i Tecnologies Agroalimentaries (IRTA) - Universitat Autònoma de Barcelona (UAB) - Universitat de Barcelona (UB) Consortium, 08193 Bellaterra, Barcelona, Spain sug: subj: Genome, Human Evaluation Artificial Intelligence Neural Networks (Computer) Phenotype Bone Density Body Mass Index Systolic Pressure Waist-Hip Ratio Algorithms Linear Regression Human ab: The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ~100k individuals, m ~500k SNPs, and k = 1000) of the interim release of the UK Biobank. We analyzed a total of five phenotypes: height, bone heel mineral density, body mass index, systolic blood pressure, and waist--hip ratio, with genomic heritabilities ranging from ~0.20 to 0.70. After hyperparameter optimization using a genetic algorithm, we considered several configurations, from shallow to deep learners, and compared the predictive performance of MLPs and CNNs with that of Bayesian linear regressions across sets of SNPs (from 10k to 50k) that were preselected using single-marker regression analyses. For height, a highly heritable phenotype, all methods performed similarly, although CNNs were slightly but consistently worse. For the rest of the phenotypes, the performance of some CNNs was comparable or slightly better than linear methods. Performance of MLPs was highly dependent on SNP set and phenotype. In all, over the range of traits evaluated in this study, CNN performance was competitive to linear models, but we did not find any case where DL outperformed the linear model by a sizable margin. We suggest that more research is needed to adapt CNN methodology, originally motivated by image analysis, to genetic-based problems in order for CNNs to be competitive with linear models. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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