| Application: | 22783, The joint analysis of imaging data and genetic data for early tumor detection, prevention, diagnosis and treatment |
| Title: | Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits |
| Size: | 659 B |
| Archived: | 18 May 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3421
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Notes
Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 x 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P=3.13 x 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.Application 22783
The joint analysis of imaging data and genetic data for early tumor detection, prevention, diagnosis and treatment
We intend to explore different imaging and genetic characteristics to work on automatic early tumor detection, benign and malignant tumor discrimination, and prediction of tumor progression. These are critical in human cancer research because in our understanding, existing automatic cancer detection approaches using imaging modalities are rare, while by only recognizing possible physical warning signs of cancer for early diagnosis is not accurate enough. Furthermore, investigation into genetic factor and other risk factors including body mass index, medications, smoking and dietary factors, etc., will hopefully lead to the prevention and improvement of the cancer tumor symptoms. Our methods directly addresses the aim of UK Biobank for tumor detection, prevention, diagnosis and treatment. We will make use of the wealth of UK Biobank data to provide important new insights into the cancer research. And it will lead to an improved understanding of genetic mechanism in tumor progression and tumor imaging characteristics which has the potential to inspire new and urgently needed approaches to prevention, diagnosis, and treatment of cancer. Initially we implement machine learning techniques to automatically classify the tumor area for all imaging modalities. Imaging characteristics are derived after preprocessment of the raw imaging data to discriminate different tumor types. Second, we use different statistical approaches to select oncogenes and tumor suppressor genes. A genetic-imaging interactive analysis will be conducted for predictions of tumor progression and treatment. We will develop different novel statistical tools, find those to handle the dataset more efficiently, verify efficacy of the newly developed statistical tools using simulations and existing studies, and finally get the clinical conclusions and develop companion software. The full cohort data if applicable.
| Lead investigator: | Professor Hongtu Zhu |
| Lead institution: | University of North Carolina at Chapel Hill |
3 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 3415 | 22783 | A Powerful Global Test Statistic for Functional Statistical Inference | 17 May 2021 |
| 3419 | 22783 | Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies | 18 May 2021 |
| 3420 | 22783 | Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) | 18 May 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3422 | Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits | Zhao B et al. | 2019 | Nat Genet |
Enabling scientific discoveries that improve human health