Our group is broadly interested in understanding individual differences between people that are caused by genetic factors. Our research programme focuses on methodology in statistical and quantitative genetics and application of new methods and new kinds of genetic & genomic data to answer important scientific questions. Applications include dissection of genetic variation underlying cognition and cognitive change and quantifying and deciphering the genetic architecture of psychiatric disorders.
Whole genome methods
Estimation of genetic parameters, such as heritability and genetic correlation coefficients, and applications in human genetics, evolutionary biology and animal breeding programmes, are based upon the specific theory of the resemblance between relatives due to genetic factors. The resemblance between relatives depends on the number of alleles that they share identical-by-descent (IBD) at loci influencing the trait of interest.
Until quite recently, IBD sharing between relatives could not be observed, and the theoretical expected value, based upon probabilities, is typically used in applications. The infinitesimal model of quantitative genetics leads to appealing theoretical consequences and its application has been highly successful.
Large segments of chromosomes segregate from parents to progeny, which creates variation in the proportion of the genome shared between pairs of relatives around the expected value.
The human genome contains the complete set of genes required to build a functional human being. However, the genome is only a source of information. In order to function, it must be expressed. The transcription of genes to produce RNA is the first stage of gene expression. Unlike the genome, the transcriptome is extremely dynamic. Most of our cells contain the same genome regardless of the type of cell, stage of development or environmental conditions. Conversely, the transcriptome varies considerably in these differing circumstances due to different patterns of gene expression.
Transcriptomics, the study of the transcriptome, is therefore a global way of looking at gene expression patterns. To answer specific questions about gene expression. For example, which genes are highly expressed in brain tumours but not in healthy brain tissue? Can these be used as drug targets or diagnostic markers?
Sometimes single markers are not sufficient to distinguish two similar diseases, as is often the case in cancer. Testing the expression profiles of a larger number of genes can provide accurate diagnoses. If the gene expression profile caused by a mutation is similar to that caused by a drug, it is likely the drug interacts with and inactivates the protein affected by the mutation.
Psychiatric genetics, a subfield of behavioral neurogenetics, studies the contribution of genetic factors to risk of disorders such as schizophrenia, autism and major depression. The underlying rationale is that most psychiatric disorders are highly heritable - meaning that a majority of the risk of having a diagnosis is due to genetic factors shared between relatives. The immediate goal of psychiatric genetics is to gain biological insights into the etiology of psychiatric disorders. The ultimate goal is to use that knowledge to inform the development of evidence-based treatments with improved efficacy and fewer side effects. In other words, the goal is to transform parts of psychiatry into a neuroscience-based discipline.
Our research in psychiatric genetics involves the application of novel statistical methods to high-throughput genome-wide datasets, such as that from large genome-wide association studies. A major focus is to better understand the genetic architecture of psychiatric disorders, including genetic overlap between different disorders (i.e. pleiotropy) and genetic and phenotypic heterogeneity within disorders. A related goal is to integrate methylation, RNA expression and other -omics data in order to develop more powerful genomics-based predictors that incorporate variation due to disease-relevant environmental exposures.
Motor Neuron Disease Genomics
Amyotrophic lateral sclerosis (ALS, the most common of the motor neuron diseases, MND) is a devastating disease for those affected and their family members. It is an adult-onset, rapidly progressive neurodegenerative disorder that leads to paralysis and death, typically within 2 to 5 years of first symptoms. To date, the most important fundamental insights into the underlying cellular mechanisms have resulted from studies of the known causal mutations. However, >85% of cases do not harbour known ALS mutations and application of new genomics methods is acknowledged as the strategy most likely to drive progress in unlocking the remaining molecular variations that cause and contribute to the disease. This is necessary if we are to address the desperate need for better diagnosis, prognosis and treatment of ALS. Our research exploits genome-wide genetic and epigenetic profiling methods to discover genes and functional pathways that contribute to ALS pathogenesis and progression.
Cognitive Ageing Genomics
There are large differences between people in how they age. Some people have no apparent physical or mental decline when they get older, some develop mild forms of cognitive impairment and yet others develop dementia. CNSG is involved in a number of research projects to study cognitive ageing, from genetic studies on cognitive differences, cognitive decline and dementia to systems genomics approaches to discover biomarkers for ageing. We are a member of the Centre for Cognitive Ageing and Cognitive Epidemiology at the University of Edinburgh. This Centre is directed by Professor Ian Deary with whom we have had a long-standing and productive research collaboration, centred around the Lothian Birth Cohorts (pictured above).