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

Arnal, MagdalenaBini, GiorgioFernández Orth, DietmarSamaras, EleftheriosKassis, MayaAisopos, FotisRambla de Argila, JordiPaliouras, GeorgeGarrard, PeterGiambartolomei, ClaudiaTartaglia, Gian Gaetano2022-05-242022-05-242022Arnal 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/dad2.123002352-8729http://hdl.handle.net/10230/53225Introduction: 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.application/pdfeng© 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 madeAlzheimer, Malaltia d'GenòmicaGenèticaMachine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's diseaseinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/dad2.12300info:eu-repo/semantics/openAccess