| Title: | Novel Statistical Methods for Disease Prediction, Causal Inference and Discovery in Genetic and Genomic Studies |
| Lead Institution: | University of Toronto |
| Principal investigator: | Dr Dehan Kong |
Application 64875
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About
Aims and Scientific Rationale. The overall aim of our study is to utilize the UK Biobank data resource and develop powerful methods that can identify genetic variants and other (biological, lifestyle, environmental and mental) risk factors for a number of complex diseases including asthma, COVID-19, cancer, dementia, depression, diabetes, heart attack, high blood pressure, mental disorders, stroke, and Parkinson's disease. Genetic studies of complex diseases such as COVID-19 and cancer have direct impact on public health, because these studies advance our understanding of the effect of genetic variation and its interaction with environmental factors. The success of these genetic studies, however, depends on the availability of good statistical methods that can reliably extract meaningful and actionable knowledge from data. Leveraging the strength of our statistical expertise, we propose to develop powerful analytical methods to advance different genetic studies, ranging from discovery and treatment to prevention. And our research mission directly aligns with one of the UK Biobank missions: development of reliable assessment of different causes of disease. The proposed research covers two main themes: 1) Improve methods available to scientists to study the involvement of genetic factors in complex diseases, by developing more powerful methods that include previously over-looked genetic variants, and 2) Develop statistical methods to identify risk factors for diseases such as COVID-19, and further understand how risk factors affect various diseases. The result is likely to help doctors to develop effective individualized treatment rule according to patient characteristics. Project Duration. The initial duration of the project is 3 years, with possible extensions depending on the progress of the research projects. We will follow the UK Biobank data usage agreement, report relevant publications and findings, and we will return to UK Biobank all the required items. Public Health Impact. The proposed research improves public health by providing more powerful analytical toolsets to researchers who would need them to study complex and heritable traits. These studies include disease prediction, treatment and prevention. We will also publish our findings in scientific journals, release open-source software packages, and present the work in international conferences, universities and research hospitals to broadly disseminate and translate the knowledge gained through the use of UK Biobank data resource.3 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 9843 | Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank | Boxi Lin (+2) | 2024 | PLOS Genetics |
| 11859 | Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks | Zachary R. McCaw (+3) | 2024 | Nature Genetics |
| 7898 | The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait | Ziang Zhang (+1) | 2023 | Bioinformatics |
Enabling scientific discoveries that improve human health