Neurological Disease Genomics

The Neurological Disease Genomics group is identifying the genetics underlying complex neurodevelopmental disorders including autism, intellectual disability and bipolar disorder.

It has been estimated, both from studies looking at family inheritance and from studies looking at identical twins, that many neurological disorders have a clearly inherited, or genetic, cause. However, despite several large genetic studies, the genes causing these disorders have not yet been identified. One problem in identifying these genes is that the patients suffering from one of these disorders do not all appear to have mutations affecting only one gene, i.e. people suffering from autism do not all have mutations affecting the same gene. Instead, it appears that different mutations within many different genes, possibly hundreds of different genes, are able to cause the same neurological disorder. The Neurological Disease Genomics group applies computational and statistical techniques to try and identify why many different mutations across many different genes can cause the same, or a similar, set of symptoms. Our aim is to identify the common processes in the brain that these genes participate in and that is disrupted in individuals that suffer from each of these disorders. Identifying the neurological processes disrupted in each of these disorders is an important step towards both understanding and diagnosing the disease and towards finding novel treatments and therapeutics.

The Neurological Disease Genomics group is gaining insights in to complex neurodevelopmental and neuropsychiatric disorders using functional and integrative genomics approaches.
With the advent of high throughput and cost effective microarray and sequencing technologies, patient genotypes are becoming readily accessible. However, interpreting this genetic data to identify the disease-causing variations has thus far proved to be difficult. This is particularly true when variants associated with a given disorder are dispersed across multiple genomic loci, which certainly appears to be the case for many neurodevelopmental (e.g. intellectual disability, autism, ADHD) and neuropsychiatric (e.g. schizophrenia, bipolar affective disorder) disorders. We hypothesise that the reason that mutations at these distinct loci give rise to a common set of symptoms is that they harbour functional elements that participate in common biological processes or pathways. Thus, we exploit functional and integrative genomics approaches to detect functional commonality among these disease-associated dispersed loci, thereby simultaneously identifying both the candidate functional elements and the shared functionality that may yield insights into the underlying etiopathology.
Identifying and defining functionality is vital to the success of these approaches. In addition to exploiting popular functional annotations (e.g. GO, KEGG, etc.), we exploit the collected and ontologised phenotypes following the determined disruption of genes within the mouse as a valuable functional genomics resource, able to provide unique insights in to human genetic disorders. By integrating multiple sources of genic annotations within functional linkage networks, we are able to identify detailed functional relationships between genes beyond that provided by any single set of annotations. We hypothesise that the variation in symptoms presented within complex human behavioural spectrum disorders may result from variance among the biological processes affected. Thus, we are applying these functional networks in the analyses of spectrum/variable disorders in order to associate variation in the affected biological processes/networks with the variation in patient presentations, and thereby detect patient subgroups. Current disorders studied in the group include autism, intellectual disability, dyslexia, ADHD, bipolar disorder and schizophrenia. We are applying these approaches to various data sources including Next Generation exome sequence data, CNVs and GWAS-defined association intervals. Identifying biologically-meaningful patient subgroups will be fundamental to better diagnosis, to better target of existing treatments and to the development of novel, more specific, therapeutics.
Finally, this research group and its approaches towards identifying pathways is now at the heart of a large 52m euro Innovative Medicines Initiative (IMI) StemBANCC project, in which induced pluripotent stem cells (iPSCs) derived from patients will be generated as cellular models for a range of disorders, including autism, Alzheimer’s disease, Parkinson’s disease, schizophrenia, bipolar, migraine, diabetes and various neuropathies. Webber is the work package leader for the interpretation of the molecular profile data (transcriptomes, metabolomes, proteomes) generated from these iPSC-derived cellular models of disease, employing network and pathway approaches to identify perturbed cellular pathways relevant to these disorders.