Version 1
: Received: 11 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (07:36:46 CET)
How to cite:
Gkertsou, C.; Parameshwarappa, A.K.; Shiyanbola, A.; Balkhiyarova1, Z.; Kouchaki, S.; Prokopenko, I.; Lagou, V.; Demirkan, A. New Genetic Loci Discovery for Alzheimer’s Disease Using Explainable Deep Neural Networks. Preprints2023, 2023120813. https://doi.org/10.20944/preprints202312.0813.v1
Gkertsou, C.; Parameshwarappa, A.K.; Shiyanbola, A.; Balkhiyarova1, Z.; Kouchaki, S.; Prokopenko, I.; Lagou, V.; Demirkan, A. New Genetic Loci Discovery for Alzheimer’s Disease Using Explainable Deep Neural Networks. Preprints 2023, 2023120813. https://doi.org/10.20944/preprints202312.0813.v1
Gkertsou, C.; Parameshwarappa, A.K.; Shiyanbola, A.; Balkhiyarova1, Z.; Kouchaki, S.; Prokopenko, I.; Lagou, V.; Demirkan, A. New Genetic Loci Discovery for Alzheimer’s Disease Using Explainable Deep Neural Networks. Preprints2023, 2023120813. https://doi.org/10.20944/preprints202312.0813.v1
APA Style
Gkertsou, C., Parameshwarappa, A.K., Shiyanbola, A., Balkhiyarova1, Z., Kouchaki, S., Prokopenko, I., Lagou, V., & Demirkan, A. (2023). New Genetic Loci Discovery for Alzheimer’s Disease Using Explainable Deep Neural Networks. Preprints. https://doi.org/10.20944/preprints202312.0813.v1
Chicago/Turabian Style
Gkertsou, C., Vasiliki Lagou and Ayse Demirkan. 2023 "New Genetic Loci Discovery for Alzheimer’s Disease Using Explainable Deep Neural Networks" Preprints. https://doi.org/10.20944/preprints202312.0813.v1
Abstract
Background: Genome-wide association studies (GWAS) have shed light on various complex diseases and traits, by detecting more than 400,000 associated genetic loci. This number is expected to drastically increase because of the use of novel artificial intelligence methods offering new ways to study the effects of variants. Deep learning using artificial neural networks (ANN) is a sub-field of artificial intelligence, which simulates how the human brain learns. We aimed at assessing the potential of deep learning in human genetic studies of Alzheimer’s Disease (AD) and how these compare to the traditional statistical methods used in GWAS, by simultaneously testing the two approaches on the same dataset, while discovering new genetic loci associated to AD. Methods: To address this aim, phenotypic and genome-wide SNP data from the UK Biobank was analysed on a binary outcome, AD diagnosis, in two different data balance options, of one-to-one and one-to-two case-control datasets, using 2,764 cases vs 2,764 controls and 5,528 controls respectively matched on gender, age, ethnicity, PC1-20 and genotyping array. Genetic data handling and GWAS were performed using PLINK, whereas neural networks were trained using GenNet, a new ANN tool, with the same datasets, separated into training (60%), validation (20%) and test (20%) sets. Neural network layers were determined using biological knowledge, by annotating SNPs to genes and genes to AD related pathways, using ANNOVAR annotations followed by GeneSCF and KEGG.Results: Significant associations were detected between four SNPs linked to two different genes and AD for the 1 to 1 case-control study design and six SNPs linked to four different genes for the 1 to 2 case-control study design by using PLINK. All identified regions have been previously associated to AD. GenNet identified twelve SNPs on seven genes to be associated with AD, all with biological plausibility, achieving an AUC of 0.80 when using three biologically determined layers and 0.73 when using two layers at the neural networks. No common top SNPs were identified between the machine learning and GWAS models. Conclusion: This is one of the first studies attempting to compare the traditional GWAS to more sophisticated state-of-art methods for understanding the genetic architecture of complex phenotypes using the same dataset. More systematic comparisons with such approaches on real data are needed to enable best practises for machine learning in the analysis of genome-wide genetic data.
Keywords
Genome-wide association studies; Alzheimer's disease; Deep Learning; neural networks
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
