The Genetics of Complex and Quantitative Traits

People differ from one another for almost any characteristic one looks at. These characteristics include obvious ones such as height and weight as well as less immediately apparent traits such as blood pressure or susceptibility to cancer. This variation between people is usually under the control of a number of different genes and environmental influences. If we could understand and measure the various genetic and environmental factors that contribute to differences between people then we will be in a better position to understand the causes of disease and to design new drugs to treat disease and perhaps to predict who may in future suffer from disease and treat them to reduce the risk. Scientists have only just developed the methods and studies that allow them to analyse variation between people effectively. Many genes have been identified in recent years in these studies and these have allowed new biological processes to be identified and new understanding of disease to be obtained. In this research we are attempting to develop new methods to more effectively analyse the data from such studies. This will lead to improved understanding of disease processes and ultimately to improved treatment and outcome of disease.

This programme aims to aid development of an understanding of the genetic control of complex traits. For such traits inter-individual variation within and between populations is controlled by variation at a number of genes, by environmental factors and by the complex interactions of these components. Most variation between individuals within and between populations is in the form of complex traits. Consequently variation in complex traits is responsible for most inter-individual variation in susceptibility to disease (both infectious and metabolic) and underlies responses to selection, both natural and artificial. Thus understanding the genetic architecture of complex traits, both in general and for specific traits in particular populations, is essential if we are to understand why and how individuals vary. We can only be really effective in understanding and treating many diseases, in predicting individuals risk of developing particular conditions and in dissecting the causes and consequences of natural selection if we understand the genetic control of variation in complex traits.||The research focuses on developing a general understanding of the control of complex traits illustrated by results from specific examples. It has involved the development of specific experimental studies and the resources necessary to implement these studies together with the development and application of methods and tools capable of analysing and interpreting the data generated by my group and our collaborators. Genome-wide association studies (GWAS) have identified a loci contribution to a number of different traits including both diseases and variation between healthy individuals. This has led to the identification of new loci and pathways that underlie variation. However, for most traits only a very limited proportion of the variation can be explained by the detected loci. These identified loci thus are of limited predictive value if used to identify individuals at risk of developing disease. We are exploring a number of different approaches for identifying additional genetic variation in genome-wide association studies. Current GWAS analyses are aimed at detecting individual loci with common alleles and relatively simple genetic effects. This is implemented in analyses that explore the data for associations between individual marker loci and trait variation. One line of our research is exploring the use of multiple marker loci and marker haplotypes for the identification of trait loci with rarer alleles. In a second series of projects we are analysing data for evidence of genetic interactions between loci that contribute to trait variation. In a final series of projects we are attempting to improve the power to predict disease risk by utilising genome-wide prediction algorithms that utilise information from many loci and not just those that pass stringent significance thresholds. These projects utilise real and simulated data to test the effectiveness of alternative analytical approaches. The real data is generated by collaborators within the Human Genetics Unit and elsewhere.