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Comparative Analysis of Machine Learning Algorithms for Identifying Genetic Markers Linked to Alzheimer’s Disease

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Intelligent Systems (BRACIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15414))

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Abstract

The identification of genetic markers for complex diseases like Alzheimer’s Disease (AD) is pivotal in medical genomics. This study aims to identify genetic markers associated with AD by introducing a novel approach that exclusively utilizes genetic data. Our primary goals are to benchmark explainable machine learning models against BLUPF90, an advanced mixed linear model approach, and to uncover single nucleotide polymorphisms (SNPs) crucial for AD. We analyze SNPs to achieve these goals, focusing on the genetic heritability rate of 58–79% for AD [12]. Our methodology focuses solely on genetic data to uncover SNPs crucial for AD, employing transparent computational models to ensure interpretability alongside predictive power. The findings demonstrate the efficacy of a purely genomic approach combined with Machine Learning to advance our understanding of AD. Our methodology successfully identified a robust set of SNPs associated with AD, encompassing both previously recognized and novel SNPs. The Machine Learning models employed delineated distinct SNP profiles, highlighting the complexity and heterogeneity of AD. These results not only deepen our understanding of AD’s genetic underpinnings but also facilitate the development of targeted therapeutic and diagnostic strategies, showcasing the potential of computational techniques in medical genomics.

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/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Acknowledgment

This study was financed by the São Paulo Research Foundation (FAPESP) grants #2020/08634-2, #2021/12618-5 and #2022/02981-8.

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Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, São Carlos, SP, Brazil

    Juliana Alves

  2. Corteva Agriscience™, Mogi Mirim, SP, Brazil

    Eduardo Costa & Alencar Xavier

  3. Department of Animal Sciences, Purdue University, West Lafayette, IN, USA

    Alencar Xavier & Luiz Brito

  4. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil

    Ricardo Cerri

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  1. Juliana Alves
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  3. Alencar Xavier
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  4. Luiz Brito
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Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Juliana Alves or Ricardo Cerri .

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Editors and Affiliations

  1. Universidade Federal Fluminense, Niterói, Brazil

    Aline Paes

  2. Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil

    Filipe A. N. Verri

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Alves, J., Costa, E., Xavier, A., Brito, L., Cerri, R., Alzheimer’s Disease Neuroimaging Initiative. (2025). Comparative Analysis of Machine Learning Algorithms for Identifying Genetic Markers Linked to Alzheimer’s Disease. In: Paes, A., Verri, F.A.N. (eds) Intelligent Systems. BRACIS 2024. Lecture Notes in Computer Science(), vol 15414. Springer, Cham. https://doi.org/10.1007/978-3-031-79035-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-79035-5_11

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