Genetics and genomics have been powerful tools in identifying hundreds of genetic loci associated with neurodevelopmental disorders and neurodegeneration. However, understanding the mechanisms by which these genes influence the brain is still in its early stages. To unravel this mystery, it is necessary to dissect the functional hierarchy that connects genetic variants to molecular pathways, cell types, neural circuits, and ultimately to cognition and behavior. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
Systems biology and gene network approaches are emerging as key players in this process. By integrating gene expression data, protein interactions, and other molecular information, these approaches allow the construction of complex networks that represent biological systems at work. Analysis of these networks can reveal modules of co-expressed genes, molecular pathways, and biological processes that are affected in diseases. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
One of the main challenges in researching neurological diseases is the heterogeneity of the brain. Different brain regions, cell types, and developmental stages exhibit distinct molecular profiles, which can obscure specific disease-related changes. Overcoming this obstacle requires methods that take into account the complex organization of the brain and the influence of factors such as age, sex, and clinical history. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
Transcriptome and gene network studies have provided new insights into neurodevelopmental disorders such as autism and schizophrenia, and neurodegenerative diseases such as Alzheimer’s disease and frontotemporal dementia. By mapping risk genes in these disorders onto co-expression networks that represent normal brain development, researchers can identify points of convergence and divergence in gene expression, revealing how genetic variants affect brain development. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
As more genetic and molecular data become available, combining different types of data into integrated networks increases the power to detect disease-relevant interactions. Network analysis can also be combined with genome-wide genetic association studies (GWAS) to identify genetic variants that affect gene expression in multiple diseases. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
The future of neuroscience research lies in the ability to integrate information from multiple sources, including genomic, transcriptomic, epigenomic, and proteomic data. Network analysis, coupled with advanced imaging methods and behavioral data, will enable the construction of more complete models of the brain and its diseases. (PARIKSHAK; GANDAL; GESCHWIND, 2015)
Reference
PARIKSHAK, NN; GANDAL, MJ; GESCHWIND, DH Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, vol. 16, no. 8, p. 471–486, 2015.
