A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Autor:	Vacher M; The Australian eHealth Research Centre, CSIRO Health and Biosecurity, Kensington, WA, Australia.; Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia., Canovas R; The Australian eHealth Research Centre, CSIRO Health and Biosecurity, Parkville, VIC, Australia., Laws SM; Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia.; Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia.; Curtin Medical School, Curtin University, Bentley, WA, Australia., Doecke JD; Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia.; The Australian eHealth Research Centre, CSIRO Health and Biosecurity, Herston, QLD, Australia.
Jazyk:	angličtina
Zdroj:	Frontiers in bioinformatics [Front Bioinform] 2024 Jun 19; Vol. 4, pp. 1390607. Date of Electronic Publication: 2024 Jun 19 (Print Publication: 2024).
DOI:	10.3389/fbinf.2024.1390607
Abstrakt:	Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Vacher, Canovas, Laws and Doecke.)
Databáze:	MEDLINE
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