Approved research

Boosting the power of GWAS using novel statistical tools

Lay summary

This 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.

Scope extension:

This 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 traits and diseases (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. Furthermore, the statistical framework also takes into account genetic relationships across multiple traits, trying to find a more succinct and objective definition of mental disorders. To maximize the available information, we intend to extract phenotypic information from raw bulk data, from which novel phenotypic features can be derived given our statistical framework. In particular, we will derive genetically orthogonal features from biometric measurements, similar to what have been performed in the neuroimaging field and metabolomic studies. Novel derived phenotypic variables will be generated and shared with the research community to facilitate clinical translations. We will also investigate brain structures and related health phenotypes derived from imaging data, including raw bulk data, to allow more detailed investigation into the pathways underlying development of mental disorders and related traits, as well as neurodegenerative disorders, and to investigate the factors that harm and protect the brain and its functions across the lifespan.

As an extension of our polygenic statistical framework, we will also apply our recently developed Polygenic Hazard Score to a range of human disorders, including cancer and mental disease, to test and improve the efficacy of age-specific genetic risk assessment, currently an unmet need in medicine. We aim to investigate genetic risk underlying complex human disorders, in particular mental disorders, cancer, and cardiovascular disease.