| Application: | 22783, The joint analysis of imaging data and genetic data for early tumor detection, prevention, diagnosis and treatment |
| Title: | Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) |
| Size: | 640 B |
| Archived: | 18 May 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3420
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Notes
Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 x 10-05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 x 10-12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 x 10-10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health 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 |
| 3421 | 22783 | 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 | 18 May 2021 |
| 3419 | 22783 | Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies | 18 May 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3421 | Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) | Zhao B et al. | 2021 | Mol Psychiatry |
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