Quantitative Traits in Health and Disease

Our aim is to understand the genetic and environmental causes of variation in complex traits and diseases. These include common conditions such as heart disease, obesity and disorders of the eye. We develop and apply a range of statistical analysis methods to identify genetic differences that are connected with either good or bad health throughout a person’s lifetime. This will help to predict the risk of people developing particular conditions, as well as providing a foundation for the future development of personalised and precision medicine.
To achieve this we are using “Biobanks” of large numbers of volunteer research participants, to enable research ranging from simple postal surveys to clinical studies with hospital visits, and detailed and wide-ranging analyses of the collected biological samples, such as blood and urine.
The volunteers in our biobanks also gave consent for researchers to link to their NHS electronic health record data, allowing for long-term follow-up of their health via medical records, and relating this to their genetic make-up
The results will help to understand the relationships between genetic variation, gene function and health, integrating computational and experimental approaches. This will allow new scientific discoveries to be made that are relevant to a wide range of conditions and will contribute to advances in medical research and customised healthcare for patients and populations.

This programme aims to deliver biological understanding of the genetic and environmental causes of variation in complex traits and diseases. To do this we will harness the special population structures in our population cohorts, together with the unparalleled capacity in Scotland to achieve extensive linkage to eHealth records, the creation of uniquely high dimensional phenotype datasets, and the effective multidisciplinary team. We will capitalise upon the special features of our study populations together with data from Generation Scotland, UK Biobank and collaborators to dissect trait genetic architecture and identify genetic variants associated with a broad range of quantitative traits of biomedical importance. We will develop and apply computational approaches to provide understanding of the genetic and environmental sources of biomedical trait variation together with their interactions. In order to exploit the “jackpot effect” of otherwise rare variants with increased frequencies in population isolates, we focus our research on Scottish (Orkney and Shetland) and Croatian (Vis and Korcula) cohorts, increasing both the size and power of our cohorts and the depth of their phenotyping. High kinship within our isolate populations facilitates imputation of variants into all genotyped individuals, with an increase in sample size enhancing detection power for both rare and common variants, using association and gene-based mapping methods. Biobanked samples (plasma, serum and urine) allow expansion of deep phenotyping (including proteomics, metabolomics, glycomics, urine traits), enabling more detailed exploration of the biology underlying associations. We will use these rich intermediate phenotype and other data to identify pathways from the genome and epigenome, through the proteome and metabolome, and to assess their impact on human health and disease using multivariate genome-wide association studies (GWAS), phenomics, two-sample Mendelian Randomisation and other integrative approaches. We also continue to functionally characterise a number of the associations, taking findings further along the translational pathway and illuminating mechanisms. The programme will contribute to our understanding of the relationships between genetic variation, gene function and health, integrates computational and experimental approaches and strengthens population cohorts towards development of precision medicine. This research programme will complement resources such as UK Biobank, which do not focus on related individuals and have limited molecular phenotyping, identifying low frequency variants and familial environmental contributions that are opaque to such studies, and leveraging the multi-omics data to suggest causality and mechanisms through, e.g. MR approaches. In the near future as long-read DNA sequencing becomes sufficiently affordable and many thousands of proteins can be assayed inexpensively, our resources will be ideal for exploring the role of novel genomic variation and further characterisation of the protein architecture of disease.