Next generation genome-wide association studies in complex human quantitative traits

• Main Author: Wood, Andrew Robert
• Published: University of Exeter 2012
• Subjects: 611.0181663
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574245

Since 2005, genome-wide association (GWA) studies have dominated the field of complex traits. Genetic and environmental factors play a role in causing disease and influencing the variance of a quantitative trait. GWA is a hypothesis-free approach that follows on from candidate gene and linkage studies and has markedly increased the number of loci associated with complex traits. Despite the relative success of GWA studies in identifying several hundreds of phenotypic associations, the genetic component of most complex traits remains largely unaccounted for. The field has now begun to focus its, efforts on the "missing heritability" to enhance the understanding of genetics and the associated biological pathways that underlie the aetiology of complex phenotypes. This thesis presents a series of studies that attempt to address this issue by exploring other sources of variation and statistical models that have not been extensively addressed in GWA studies to date. Chapter 1 is an introduction to genome-wide association studies. In particular it describes the origins of these studies, what we have learnt from them as well as their limitations. Chapter 2 describes a study that shows how multiple signals within a single locus can explain more of the genetic component of a complex trait, using gene expression as a model trait. 2 Chapter 3 describes a study that tests for deviation from additivity (additivity is an assumption of most GWA studies to date) through dominant, recessive and gene-gene interaction analyses using height, body mass index, and waist-hip ratio (adjusted for BMI) as model phenotypes. Chapter 4 describes a study that examines how more signals may be identified by increasing the density of variants through 1000 Genomes based imputation compared to HapMap based imputation. I use 93 phenotypes, all circulating factors, including proteins, ions and vitamins. Chapter 5 describes a study that tests whether more association signals can be discovered through low-coverage whole-genome sequencing. In particular, I compare association testing based on 1000 Genomes based imputation and sequencing. I use gene expression as a model trait. Chapter 6 discusses the research findings from the previous chapters, presents conclusions, and describes future research plans in the field of complex traits for a fuller understanding of the role of genetics. 3