Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Arnal, Magdalena; Bini, Giorgio; Fernández Orth, Dietmar; Samaras, Eleftherios; Kassis, Maya; Aisopos, Fotis; Rambla de Argila, Jordi; Paliouras, George; Garrard, Peter; Giambartolomei, Claudia; Tartaglia, Gian Gaetano

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

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dc.contributor.author Arnal, Magdalena
dc.contributor.author Bini, Giorgio
dc.contributor.author Fernández Orth, Dietmar
dc.contributor.author Samaras, Eleftherios
dc.contributor.author Kassis, Maya
dc.contributor.author Aisopos, Fotis
dc.contributor.author Rambla de Argila, Jordi
dc.contributor.author Paliouras, George
dc.contributor.author Garrard, Peter
dc.contributor.author Giambartolomei, Claudia
dc.contributor.author Tartaglia, Gian Gaetano
dc.date.accessioned 2022-05-24T10:33:08Z
dc.date.available 2022-05-24T10:33:08Z
dc.date.issued 2022
dc.description.abstract Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.
dc.description.sponsorship Funding: the research leading to these results was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727658 (project iASiS), European Research Council ASTRA 855923, and the European Genome‐phenome Archive (EGA). Claudia Giambartolomei has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement No 754490–MINDED project. This research has been conducted using the UK Biobank Resource under Application Number 35916
dc.format.mimetype application/pdf
dc.identifier.citation Arnal Segura M, Bini G, Fernandez Orth D, Samaras E, Kassis M, Aisopos F et al. Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease. Alzheimers Dement (Amst). 2022 Apr 5;14(1):e12300. DOI:10.1002/dad2.12300
dc.identifier.doi http://dx.doi.org/10.1002/dad2.12300
dc.identifier.issn 2352-8729
dc.identifier.uri http://hdl.handle.net/10230/53225
dc.language.iso eng
dc.publisher Wiley Open Access
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/727658
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/855923
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/754490
dc.rights © 2022 Magdalena Arnal Segura et al. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject.other Alzheimer, Malaltia d'
dc.subject.other Genòmica
dc.subject.other Genètica
dc.title Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

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