Boosting the power of GWAS using novel statistical tools
Approved Research ID: 27412
Approval date: April 21st 2017
Lay summaryThis proposal seeks to apply UK Biobank data to study the genetic architecture of human traits using novel statistical tools. We aim to investigate the relationship between mental disorders and co-morbid diseases such as cardiovascular disease, cancer and metabolic disease (as well as protective phenotypes). Genome-wide association studies (GWAS) have successfully identified many genetic variants influencing complex human traits. However, the identified genetic variants only explain a small portion of the heritability of these traits. To improve discovery of genetic variants in complex human traits, we have developed statistical tools building on a Bayesian statistical framework. This proposal seeks to increase discovery of genetic loci influencing a range of human traits and disorders. Identifying genetic factors that confer risk or protect against health-related traits is critical for understanding the causal mechanisms underlying disease, and the causal relationship shared between clinical conditions. Improved gene discovery might inform the development of genetic prediction tools and ultimately improve treatment strategies for large patient groups. Hence, the proposed research is entirely congruent with the stated aim of UK Biobank ?to improve the prevention, diagnosis and treatment of illness and the promotion of health throughout society?. We will analyze the GWAS data on complex traits in the UK Biobank cohort using novel statistical methodology. Using software and computational tools we are able to enhance gene discovery by integrating GWAS data with additional knowledge about genetic variants, including their association in related traits or their genomic position. To assess the replicability (i.e. the robustness of the results) of the identified variants, we will evaluate their association in independent GWAS cohorts. Finally, the results may inform the development of novel genetic prediction tools. We would wish to study the full UK Biobank cohort.
- Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition
- Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function
- Differences in directed functional brain connectivity related to age, sex and mental health
- Quantifying the Polygenic Architecture of the Human Cerebral Cortex: Extensive Genetic Overlap between Cortical Thickness and Surface Area
- Women's brain aging: Effects of sex-hormone exposure, pregnancies, and genetic risk for Alzheimer's disease
- Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
- Differences in directed functional brain connectivity related to age, sex and mental health.
- 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans
- Population-based body-brain mapping links brain morphology with anthropometrics and body composition