Neurogenomics

Neurogenomics is the study of how the genome of an organism influences the development and function of its nervous system. This field intends to unite functional genomics and neurobiology in order to understand the nervous system as a whole from a genomic perspective. Neurogenomics is the study of how the genome of an organism influences the development and function of its nervous system. This field intends to unite functional genomics and neurobiology in order to understand the nervous system as a whole from a genomic perspective. The nervous system in vertebrates is made up of two major types of cells – neuroglial cells and neurons. Hundreds of different types of neurons exist in humans, with varying functions – some of them process external stimuli; others generate a response to stimuli; others organize in centralized structures (brain, spinal ganglia) that are responsible for cognition, perception, and regulation of motor functions. Neurons in these centralized locations tend to organize in giant networks and communicate extensively with each other. Prior to the availability of expression arrays and DNA sequencing methodologies, researchers sought to understand the cellular behaviour of neurons (including synapse formation and neuronal development and regionalization in the human nervous system) in terms of the underlying molecular biology and biochemistry, without any understanding of the influence of a neuron's genome on its development and behaviour. As our understanding of the genome has expanded, the role of networks of gene interactions in the maintenance of neuronal function and behaviour has garnered interest in the neuroscience research community. Neurogenomics allows scientists to study the nervous system of organisms in the context of these underlying regulatory and transcriptional networks. This approach is distinct from neurogenetics, which emphasizes the role of single genes without a network-interaction context when studying the nervous system. In 1999, Cirelli & Tononi first reported the association of genome-wide brain gene expression profiling (using microarrays) with a behavioural phenotype in mice. Since then, global brain gene expression data, derived from microarrays, has been aligned to various behavioural quantitative trait loci (QTLs) and reported in several publications. However, microarray based approaches have their own problems that confound analysis – probe saturation can result in very small measurable variance of gene expression between genetically unique individuals, and the presence of single nucleotide polymorphisms (SNPs) can result in hybridization artifacts. Furthermore, due to their probe-based nature, microarrays can miss out on many types of transcripts (ncRNAs, miRNAs, and mRNA isoforms). Probes can also have species-specific binding affinities that can confound comparative analysis. Notably, the association between behavioural patterns and high penetrance single gene loci falls under the purview of neurogenetics research, wherein the focus is to identify a simple causative relationship between a single, high penetrance gene and an observed function/behaviour. However, it has been shown that several neurological diseases tend to be polygenic, being influenced by multiple different genes and regulatory regions instead of one gene alone. There has hence been a shift from single gene approaches to network approaches for studying neurological development and diseases, a shift that has been greatly propelled by the advent of next generation sequencing methodologies. Twin studies have revealed that schizophrenia, bipolar disorder, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD) are highly heritable, genetically complex psychiatric disorders. However, linkage studies have largely failed at identifying causative variants for psychiatric disorders such as these, primarily because of their complex genetic architecture. Multiple low penetrance risk variants can be aggregated in affected individuals and families, and sets of causative variants could vary across families. Studies along these lines have determined a polygenic basis for several psychiatric disorders. Several independently occurring de novo mutations in patients Alzheimer's Disease have been found to disrupt a shared set of functional pathways involved with neuronal signalling, for example. The quest to understand the causative biology of psychiatric disorders is hence greatly assisted by the ability to analyse entire genomes of affected and unaffected individuals in an unbiased manner. With the availability of massively parallel next generation sequencing methodologies, scientists have been able to look beyond the probe based captures of expressed genes. RNA-seq, for example, identifies 25-60% more expressed genes than microarrays do. In the upcoming field of neurogenomics, it is hoped that by understanding the genomic profiles of different parts of the brain, we might be able to improve our understanding of how the interactions between genes and pathways influence cellular function and development. This approach is expected to be able to identify the secondary gene networks that are disrupted in neurological disorders, subsequently assisting drug development stratagems for brain diseases. The BRAIN initiative launched in 2013, for example, seeks to 'inform the development of future treatments for brain disorders, including Alzheimer’s disease, epilepsy, and traumatic brain injury' . Rare variant association studies (RVAS) have highlighted the role of de novo mutations in several congenital and early-childhood-onset disorders like autism. Several of these protein disrupting mutations have been able to be identified only with the aid of whole genome sequencing efforts, and validated with RNA-Seq. Additionally, these mutations are not statistically enriched in individual genes, but rather, exhibit patterns of statistical enrichment in groups of genes associated with networks regulating neurological development and maintenance. Such a discovery would have been impossible with prior gene-centric approaches (neurogenetics, behavioural neuroscience). Neurogenomics allows for a high-throughput system-based approach for understanding the polygenic basis of neuropsychiatric disorders. When autism was identified as a distinct biological disorder in the 1980s, researchers found that autistic individuals showed a brain growth abnormality in the cerebellum in their early developmental years. Subsequent research has indicated that 90% of autistic children have a larger brain volume than their peers by 2 to 4 years of age, and show an expansion in the white and gray matter content in the cerebrum. The white and gray matter in the cerebrum is associated with learning and cognition respectively, and the formation of amyloid plaques in the white matter has been associated with Alzheimer's disease. These findings highlighted the influence of structural variance in the brain on psychiatric disorders, and have motivated the use of imaging technologies to map regions of divergence between healthy and diseased brains. Furthermore, while it may not always be possible to retrieve biological specimens from different areas live human brains, neuroimaging techniques offer a noninvasive means to understanding the biological basis of neurological disorders. It is hoped that an understanding of localization patterns of different psychiatric diseases could in turn inform network analysis studies in neurogenomics. Structural Magnetic Resonance Imaging (MRI) can be used to identify the structural composition of the brain. Particularly in the context of neurogenomics, MRI has played an extensive role in the study of Alzheimer's disease(AD) over the past four decades. It was initially used to rule out other causes of dementia, but recent studies indicated the presence of characteristic changes in patients with AD. As a result, MRI scans are currently being used as a neuroimaging tool to help identify the temporal and spatial pathophysiology of Alzheimer's disease, such as specific cerebral alterations and amyloid imaging.