Identifying the Key Pathogenic Factors of Neurological Disorders by Integrating Multi-omics Data
The research on human neurological disorders is inadequate in both etiological explanation and drug development and clinical treatment because of their complex molecular mechanisms, relatively rare clinical samples and data resources. For example, there is a long-standing controversy about the pathogenesis of Alzheimer's disease between the amyloid β-peptide (Aβ) deposits model and microbial infection theory. With the rapid development of high throughput technology, it’s getting easier to low-cost access multi-mics, including genome variations, transcriptome expression level, alternative splicing, epigenome, proteome, metabolome, microbiome, etc., data by the wet-lab technique. A range of analytical approaches through integrating multi-omics data from different resources have greatly enhanced understanding of the molecular mechanism in human complex diseases. For example, expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) integrate genome and transcriptome data to assess the influence of variations in the genome on gene expression and alternative splicing process, respectively; Genome-wide association study with protein levels (pGWAS) integrates genome and proteome data to detect the genome-wide significant variation-to-protein associations; Mendelian randomization (MR) integrates trait-related single nucleotide polymorphism (SNP) data of multiple genome-wide association studies to demonstrate causality between exposure and outcome. However, these studies by integrating multi-omics data in the human nervous system are limited, which is not only because of the overall scarcity of related studies but also because of relatively rare data resources of human neurological disorders.
In summary, the current challenge for understanding the molecular mechanisms of human nervous system functions and neurological disorders is to
1) develop computational approaches and analysis pipeline for integrating multi-omics data of human nervous system and neurological disorders;
2) develop statistical methods to analyse small sample data, which is a common situation in human neurological disorders;
3) build the databases to store and enrich multi-omics data of human nervous system and neurological disorders.
Therefore, we propose to conduct a Research Topic on “Exploring the Pathogenesis of Neurological Disorders by Integrating Multi-omics Data”. The subtopics include, but are not limited to
• Mechanism exploration of human nervous system functions and neurological disorders by integrating multi-omics data.
• Statistical approaches and analysis pipeline for integrating multi-omics data in the small-scale sample.
• Databases for storing multi-omics data of the human nervous system and neurological disorders.
• Drug target discovery for human neurological diseases using multi-omics data.
• Determination of the aetiology of human neurological diseases using multi-omics data.
• Predicting the key neuro-pathogenic factors through bioinformatics methods.
