UK Parkinson's Disease Consortium

UK Parkinson's Disease Consortium - UKPDC

• Home
• News
• Background
• Principal Investigators
• Co-Investigators
• Research Groups
  • Cell Physiology
  • Clinical Neuroscience
  • Clinical Studies
  • Drosophila Genetics
  • Mitochondria
  • Molecular Biology and Biochemistry
  • Molecular Neuropathology
  • Neurogenetics
  • Neurogenomics
    • Mina Ryten
    • Daniah Trabzuni
    • Collaborators
    • Funding
    • Contact us
  • Neurological Biochemistry
  • Neurological Signalling
  • Protein Phosphorylation
• Collaborators
• Funding
• Publications
• Contact us

Neurogenomics Group

The aim of our group is to translate recently discovered genetic risk traits for complex neurological and psychiatric conditions into a deeper understanding of pathogenesis. Until recently, the complexity of common neurological and psychiatric human diseases has made a genetic understanding of these conditions appear near impossible. Thus, the aetiology of the vast majority of non-mendelian cases remained obscure. However, this has changed in the last five years with the advent of genome wide association studies (GWAs). Using single nucleotide polymorphism (SNP) chips to screen simultaneously for >500,000 genetic polymorphisms, typically in >1000 cases and >1000 controls, genetic risk factors for common neurological and psychiatric diseases have been identified, including Parkinson’s disease, Alzheimer’s disease, bipolar disease and schizophrenia.

These studies have demonstrated what has long been suspected; that “normal” variability can contribute to the risk of developing common neurological and psychiatric diseases. Furthermore, the effects of genetic risk variants can be sufficiently substantial to be clinically relevant. Common variability in the a-synuclein gene (minor allele frequencies ~10%) can predispose to Parkinson’s disease with an odds ratio of ~1.5. Thus, the genetic risk factors being identified by GWAs are comparable to the effects of, for example, raised homocysteine levels in stroke (commonly caused by a polymorphism in MTHFR: minor allele frequency 35%, OR 1.65), a risk factor which is regularly measured and treated. Even when the effect sizes are much smaller the information generated by GWAs may provide a route to drug discovery. This is demonstrated by the recent identification of genes encoding the sites of actions of important drugs (e.g. statins) on the basis of genetic loci explaining <1% of the phenotypic variation in the population.

Thus, by providing insights into disease pathogenesis, GWAs of neurological and psychiatric diseases have the potential to help generate new therapeutic strategies. However, in order to meet this potential, statistical “hits” need to be translated into genes and pathways. While this may seem straight forward, excitement has been tempered by the realisation that knowing genetic risk variants has not provided an automatic understanding of pathogenesis in most cases. In general, whereas rare Mendelian causes of neurological disease usually follow readily apparent causal pathways (for example, by altering the amino acid sequence of the protein product), common low risk genetic variants identified by GWAs typically do not map to coding regions of the genome and some have not even mapped to recognisable genes. This makes it difficult to understand how they operate to predispose to disease and so precludes functional studies. Solving this problem is the inspiration for this group’s work.

The basis of our approach is the hypothesis that heritable differences in transcriptional regulation, which are present and measurable in control populations, are important drivers of pathology in the human central nervous system (CNS). If common heritable differences in transcriptional regulation can drive pathology in the CNS, then we would expect to find strong associations between the risk SNPs identified in GWAs for human CNS diseases and specific mRNA expression phenotypes of functional significance in control human brain.

The success of previous genotypic gene expression studies provides empirical evidence for the feasibility and potential of this approach. We aim to expand on this general approach by taking the novel steps of studying multiple brain regions from the same individuals and by taking account of splice variation. The former (regional expression) will result in a unique parallel data set, which will provide the opportunity to better describe the internal structure of mRNA expression in the human brain and so potentially identify a smaller number of key expression phenotypes for association testing making it easier to detect important trans effects. The latter (splice variation), recognises the evidence for the importance of alternative splicing specifically in brain and the functional diversity of splice variants of the same gene. Since splice variants can have opposing functions the biological interpretation of overall gene expression levels (as compared to exon-specific expression) is fraught with difficulty. Thus, we hope to provide rapid insights into complex neurological and psychiatric diseases and generate a unique resource for the neuroscience community.