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Discovery of Genetic Biomarkers for Alzheimer’s Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies

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Abstract

Objective

To identify candidate neuroimaging and genetic biomarkers for Alzheimer’s disease (AD) and other brain disorders, especially for little-investigated brain diseases, we advocate a data-driven approach which incorporates an adaptive classifier ensemble model acquired by integrating Convolutional Neural Network (CNN) and Ensemble Learning (EL) with Genetic Algorithm (GA), i.e., the CNN-EL-GA method, into Genome-Wide Association Studies (GWAS).

Methods

Above all, a large number of CNN models as base classifiers were trained using coronal, sagittal, or transverse magnetic resonance imaging slices, respectively, and the CNN models with strong discriminability were then selected to build a single classifier ensemble with the GA for classifying AD, with the help of the CNN-EL-GA method. While the acquired classifier ensemble exhibited the highest generalization capability, the points of intersection were determined with the most discriminative coronal, sagittal, and transverse slices. Finally, we conducted GWAS on the genotype data and the phenotypes, i.e., the gray matter volumes of the top ten most discriminative brain regions, which contained the ten most points of intersection.

Results

Six genes of PCDH11X/Y, TPTE2, LOC107985902, MUC16 and LINC01621 as well as Single-Nucleotide Polymorphisms, e.g., rs36088804, rs34640393, rs2451078, rs10496214, rs17016520, rs2591597, rs9352767 and rs5941380, were identified.

Conclusion

This approach overcomes the limitations associated with the impact of subjective factors and dependence on prior knowledge while adaptively achieving more robust and effective candidate biomarkers in a data-driven way.

Significance

The approach is promising to facilitate discovering effective candidate genetic biomarkers for brain disorders, as well as to help improve the effectiveness of identified candidate neuroimaging biomarkers for brain diseases.

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Acknowledgements

We thank the reviewers for their constructive comments on this article. This study was supported by NSF of China (Grant Nos. 61976058, 61772143 and U19A2067), National Key RD Program of China (Grant No. 2018YFC0910405), Science and Technology Planning Project of Guangdong (Grant No. 2021A1515012300, 2019A050510041 and 2020A1515010941), and Science and Technology Planning Project of Guangzhou (Grant Nos. 202103000034 and 202002020090). Additional funding for neurological research was from Surrey Hospital & Outpatient Centre Foundation (FHG2017-001). Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense Award No. W81XWH-12-2-0012). ADNI was funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research was providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Additional funding support for the manuscript preparation was from the Surrey Hospital & Outpatient Centre Foundation of Fraser Health, Canada.

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Authors and Affiliations

  1. Faculty of Computer, Guangdong University of Technology, Guangzhou, 510006, People’s Republic of China

    An Zeng, Huabin Rong, Longfei Jia, Yiqun Zhang & Fengyi Zhao

  2. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, People’s Republic of China

    Dan Pan

  3. College of Computer Science and Electronic Engineering, Hunan University, School of Computer Science, National University of Defense Technology, Peng Cheng Lab, Shenzhen, 518000, People’s Republic of China

    Shaoliang Peng

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  1. An Zeng
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for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)

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Correspondence to Dan Pan or Shaoliang Peng.

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Additional information

Alzheimer’s Disease Neuroimaging Initiative-Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Zeng, A., Rong, H., Pan, D. et al. Discovery of Genetic Biomarkers for Alzheimer’s Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies. Interdiscip Sci Comput Life Sci 13, 787–800 (2021). https://doi.org/10.1007/s12539-021-00470-3

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