Whole-genome approaches for dissecting (shared) genetic architecture and individual risk prediction of complex traits in human populations

The objective of this project is to develop statistical models and methods that dissect the genetic architecture and maximise the accuracy of risk prediction for complex human traits and diseases. The methods will be developed based upon the whole-genome linear mixed model we have pioneered. We applied the method to estimate genetic correlation and dissect shared genetic etiology between five psychiatric disorders. The applications of this method were published in high profile journals including Nature Genetics. We will extend this approach to multivariate framework to better dissect the genetic architecture of
complex traits and diseases. This project will develop and implement advanced statistical methods for whole-genome analysis of human diseases. The methods will improve upon current methods in three areas i) better identification of causal variants, ii) capturing additional genetic variance iii) increased accuracy for individual risk prediction for complex traits. Our approach is highly innovative. The outcomes will be of great significance to our understanding of the genetics of complex traits. We will contribute to improve the prevention, diagnosis and treatment of human complex diseases The genomics era brings promise for personalised genomic medicine in which diagnosis and treatment are tailored to individuals based on profiles
recorded in their genome. More feasible and realistic is the opportunity of ?stratified medicine? in which individuals are classified into treatment-relevant sub-groups based on profiles that incorporate information from both genomic and environmental risk factors. This project aims to develop advanced statistical methods to better predict an individual?s risk of disease. The approach to be developed in this project will introduce a new paradigm for predicting individual risks for complex diseases. This project would require to access individual genotypes and phenotypes for the full cohort. We also would like to access disease traits in addition to quantitative phenotypes. However, we will restrict our analysis to self-reported outcomes at baseline.