Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Abstract

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Keywords: Genetic architecture; Heritability; Multi-omics; Networks; Neurodegenerative.

Fig. 1

An illustration of the liability-threshold model and the karyotypic distribution of variants with varying effect sizes. We can conceptualize four distinct liability-threshold models: (A) characterized by the cumulative effects of a high-effect-size genetic variant (red), moderate-effect-size variants (blue, purple, and cyan), and influenced by unique sets of small-effect genetic variants (green); (B) driven by the cumulative effects of moderate-effect-size variants and unique sets of small-effect genetic variants; (C) influenced by moderate-effect-size variants, unique sets of small-effect genetic variants and environmental factors (orange); or (D) driven by the high-effect-size genetic variant alone. Each model can push the disease risk beyond the diagnostic threshold. A didactic illustration of the distribution of specific genetic variants with varying effect sizes associated with neurodegenerative diseases, namely Alzheimer’s and Parkinson’s disease, across the chromosomes, is shown (E)

Fig. 2

The genetic overlap between neurodegenerative diseases. (A) The Sankey plot illustrates the shared genes from GWAS locus-associated genes and other studies among neurodegenerative diseases, with ribbon width indicating the extent of genetic overlap. (B) The UpSet plot depicts the overlap of genetic variants across these diseases, showing gene content similarity. Green bars represent the total number of genes associated with each disease, while blue bars indicate the number of shared genes. Indigo dots connected by lines specify the genes common to the neurodegenerative diseases shown in the bar plot

Fig. 3

Neurodegenerative disease multistage interplay omics model - OmicPeak Disease Trajectory Model. In this conceptual model, the progression rates of various diseases denoted as P1-P4, are represented as distinct trajectories leading to mountain peaks. The integrative framework posits that an individual’s genetic predisposition, which may involve one or more genetic variants with varying effect sizes, contributes to the initial driver genetic component (the base camp position). Moreover, subsequent influences emerge from complex interactions within biological networks that are specific to each trajectory, as well as from environmental factors, age, and comorbidities. These factors collectively modulate the rate of disease progression, clinical manifestations, and symptom severity. This multistage, omics-based model may branch into multiple potential trajectories toward each peak, capturing the unique combinations of intrinsic and stochastic factors that underlie the complex genetic architecture of disease progression