Biostatistics and bioinformatics

Our work focuses on the analysis of large volumes of biological data to help understand the causes of mental-health related diseases.

In recent years, there has been a marked increase in the size and complexity of genomic data available to researchers. This influx of information has the potential to yield important insights into the biological processes underlying genetic disorders.

However, the size and richness of this new data poses considerable challenges for analysis, data storage and computation. Our biostatistics and bioinformatics unit (BBU) works to meet these challenges, developing new approaches and techniques to allow research to benefit from this wealth of data.

Our biostatistics and bioinformatics research

Our research covers the following areas, spanning all the research groups in the CNGG:

Analysis of common genetic variation (SNPs)

We have performed genome-wide association studies (GWAS) on large samples of neurodegenerative and psychiatric disorders, notably schizophrenia, ADHD, late-onset Alzheimer's disease and Huntington's disease.

Analysis of rare genetic variation

We also analyse exome-chip and sequencing data to discover single rare variants implicated in disease risk, with particular interest in denovo variants. We also study rare copy number variants (CNVs) for association with disease, both in case-control and denovo samples.

Integration of bioinformatics data with genetic data

We are particularly interested in the development and application of methods to test for enrichment of genetic signal in gene sets (pathways). These can be obtained from databases or by interrogating expression or protein interaction networks. These methods have been successfully applied to GWAS and CNV studies.

Aggregation of genetic variation

We have developed methodology for aggregating genetic variation at the gene and pathway level, and has applied this within diseases to find novel susceptibility loci and across diseases to find shared genetic susceptibility. Currently, we are interested in developing methods to combine different types of genetic variation (SNPs, denovo variants, CNVs) into a single analysis.

Phenotype analysis

The genetic analysis methods described above are being applied to a wealth of clinical and phenotypic data, notably imaging data.

Collaborations

Our members are active in numerous international consortia, notably the Psychiatric Genomics Consortium (PGC), the International Genomics of Alzheimers Project (IGAP) and the International Parkinson's Disease Genomics Consortium (IPDGC).

Teaching and training

In addition to our research output, we are actively involved in training students and researchers via the MSc in Bioinformatics & Genetic Epidemiology.

Theme lead

Principal investigators