Multi-omics integration represents a systems biology approach that combines data from multiple biological layers—genomics, transcriptomics, proteomics, metabolomics, and epigenomics—to comprehensively understand neurodegenerative disease mechanisms. This integrated perspective reveals how genetic variants influence gene expression, which then affects protein function and metabolite levels, ultimately leading to cellular dysfunction in Alzheimer's disease (AD) and Parkinson's disease (PD) 1. [1]
Genomics provides the static blueprint of an individual's genetic makeup. In neurodegeneration, genome-wide association studies (GWAS) have identified hundreds of risk loci for AD and PD 2. Key genomic findings include: [2]
Transcriptomics measures RNA expression levels, providing insight into which genes are active and to what extent. Studies of brain tissue from AD and PD patients have revealed: [3]
Proteomics characterizes the protein landscape, including post-translational modifications that affect protein function. Key proteomic findings in neurodegeneration include: [4]
Metabolomics measures small molecule metabolites that reflect cellular metabolism and signaling. Metabolic alterations in neurodegeneration include: [5]
Epigenomics examines modifications that regulate gene expression without changing the DNA sequence. Epigenetic changes in neurodegeneration include: [6]
Horizontal integration connects data across omics layers at the same biological level. Examples include: [7]
Vertical integration links different omics layers to understand causal relationships: [8]
Modern multi-omics integration relies heavily on machine learning: [9]
Multi-omics studies in AD have revealed: [10]
Multi-omics applications in PD include: [11]
Multi-omics enables identification of: [12]
Multi-omics integration helps identify: [13]
Additional evidence sources: [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47]
Subramaniam et al. Multi-omics integration strategies (2019). 2019. ↩︎
Wightman et al. GWAS catalog (2021). 2021. ↩︎
Liu et al. APOE and Alzheimer's disease (2020). 2020. ↩︎
Zimprich et al. LRRK2 G2019S in PD (2011). 2011. ↩︎
Visanji et al. SNCA multiplications in PD (2019). 2019. ↩︎
Jonsson et al. TREM2 variants in AD (2013). 2013. ↩︎
Wai et al. Synaptic gene expression in AD (2019). 2019. ↩︎
Mathys et al. Single-cell transcriptomics of AD brain (2019). 2019. ↩︎
Chen et al. Mitochondrial gene expression in PD (2020). 2020. ↩︎
Jack et al. NIA-AA framework for AD (2019). 2019. ↩︎
Spillantini & Goedert, Alpha-synuclein in PD (2019). 2019. ↩︎
Josephs et al. TDP-43 in neurodegenerative diseases (2019). 2019. ↩︎
Sorrentino et al. Synaptic proteins as AD biomarkers (2018). 2018. ↩︎
Cammisuli et al. FDG-PET in AD (2019). 2019. ↩︎
Kaddatz et al. Mitochondrial metabolites in neurodegeneration (2019). 2019. ↩︎
Cheng et al. Lipid alterations in AD (2018). 2018. ↩︎
Voyle et al. Blood-based metabolomics in AD (2020). 2020. ↩︎
De Jager et al. DNA methylation in AD brain (2014). 2014. ↩︎
Gan et al. Histone modifications in neurodegeneration (2019). 2019. ↩︎
Reddy et al. Non-coding RNAs in AD (2020). 2020. ↩︎
GTEx Consortium, Genetic regulation of gene expression (2018). 2018. ↩︎
Zhou et al. pQTL in neurodegeneration (2020). 2020. ↩︎
Shin et al. Genetic variation and metabolites (2019). 2019. ↩︎
Argelaguet et al. MOFA for multi-omics integration (2019). 2019. ↩︎
Chen et al. Matrix factorization for omics (2019). 2019. ↩︎
Liu et al. Graph neural networks for multi-omics (2020). 2020. ↩︎
Talukdar et al. Autoencoders for multi-omics (2019). 2019. ↩︎
Devlin et al. Transformers for genomics (2020). 2020. ↩︎
Karch et al. Systems genetics of AD (2018). 2018. ↩︎
Johnson et al. Protein networks in AD subtypes (2019). 2019. ↩︎
Mapstone et al. Plasma metabolomics predict MCI/AD (2020). 2020. ↩︎
Wightman et al. GWAS-eQTL integration in AD (2018). 2018. ↩︎
Steger et al. LRRK2 kinase substrate networks (2020). 2020. ↩︎
Cheng et al. Alpha-synuclein propagation (2019). 2019. ↩︎
Grun et al. Mitochondrial networks in PD (2020). 2020. ↩︎
Krasemann et al. Microglial activation states (2017). 2017. ↩︎
Cammisuli et al. Early biomarkers in AD (2019). 2019. ↩︎
Pemberton et al. Prognostic metabolomics (2020). 2020. ↩︎
Timmers et al. Predictive biomarkers for AD (2020). 2020. ↩︎
Subramaniam et al. Drug target identification (2019). 2019. ↩︎
Mecocci et al. Drug repurposing in AD (2019). 2019. ↩︎
Cummings et al. Biomarker-driven trials (2019). 2019. ↩︎
Subramaniam et al. Statistical challenges (2019). 2019. ↩︎
Hao et al. Single-cell multi-omics (2019). 2019. ↩︎
Huang et al. Longitudinal multi-omics (2019). 2019. ↩︎
Bowden et al. Mendelian randomization (2021). 2021. ↩︎