Multi-Genome Wide Association Studies (GWAS) and Their Impact on Interpreting Complex Diseases Like Multiple Sclerosis

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic bases of complex diseases such as multiple sclerosis (MS), a chronic autoimmune disease of the central nervous system. Recent advancements in multi-genome wide association studies (multi-GWAS) have provided deeper insights into the intricate genetic architecture of diseases like MS, highlighting the role of both genetic and nongenetic factors in disease etiology and progression. This blog post explores how recent studies leveraging multi-GWAS approaches are enhancing our understanding of MS and paving the way for novel therapeutic strategies.

Unraveling the Complexity of MS Genetics
Multi-GWAS have identified over 200 MS-associated loci across the human genome, underscoring the disease's genetic complexity. These studies have not only broadened our understanding of the genetic underpinnings of MS but have also emphasized the challenges in translating genomic data into clinical applications. The identification of single nucleotide polymorphism (SNP) variants associated with MS reveals how these genetic variations can influence the expression and function of genes crucial to the disease's pathogenesis. For example, Mechelli et al. (2020) reviewed studies focusing on single genes of interest and highlighted the significant role of nongenetic factors, such as environmental exposures, in MS etiology, suggesting an intricate interplay between genetics and environment in determining disease risk (Mechelli et al., 2020).

Gene Networks and Pathway Analyses
The application of dense module searching in GWAS data has been instrumental in identifying gene networks associated with MS. By focusing on network-assisted analysis, researchers can better interpret the functional roles of variants with association signals. Manuel et al. (2020) applied the Dense Module Searching of GWAS tool (dmGWAS) to MS GWAS datasets, identifying significant network modules that include genes like GRB2, HDAC1, JAK2, MAPK1, and STAT3. These modules were enriched with functional terms pertinent to MS pathogenesis, such as "regulation of glial cell differentiation" and "T-cell costimulation," offering new avenues for therapeutic intervention (Manuel et al., 2020).

Towards Therapeutic Targets and Personalized Medicine
The integration of Mendelian randomization with GWAS findings has been particularly promising in identifying potential druggable targets for MS. By correlating the effects of genetic loci on MS susceptibility with data on existing therapeutic compounds, researchers have prioritized genes that could be targeted for therapy. Jacobs et al. (2020) highlighted 45 genes associated with MS susceptibility, of which 20 encode proteins currently targeted by therapeutic compounds, thus paving the way for novel therapeutic approaches and personalized medicine (Jacobs et al., 2020).

Conclusions
Recent advancements in multi-GWAS have significantly contributed to our understanding of the genetic and molecular mechanisms underlying complex diseases like MS. By uncovering the genetic loci associated with MS and elucidating the gene networks and pathways involved in disease pathogenesis, these studies offer promising avenues for the development of targeted therapies and personalized treatment strategies. The integration of genetic data with information on environmental exposures and other nongenetic factors will be crucial in developing a comprehensive understanding of MS and other complex diseases.

Reference:
Mechelli, R., Umeton, R., Manfrè, G., Romano, S., Buscarinu, M., Rinaldi, V., Bellucci, G., Bigi, R., Ferraldeschi, M., Salvetti, M., & Ristori, G. (2020). Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis. Genes, 11.
Manuel, A., Dai, Y., Freeman, L., Jia, P., & Zhao, Z. (2020). Dense module searching for gene networks associated with multiple sclerosis. BMC Medical Genomics, 13.
Jacobs, B., Taylor, T., Awad, A., Baker, D., Giovanonni, G., Noyce, A., & Dobson, R. (2020). Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis. Brain Communications, 2.