| Title: | Developing Statistical Methods for Genetic and Imaging Studies of Substance Use and Dependence |
| Lead Institution: | Michigan State University |
| Principal investigator: | Dr Qing Lu |
Application 40967
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About
Substance misuse and substance use disorders (SUD) pose a significant threat to the health, families and communities of the user, and have a remarkably high global disease burden. Genetic and neurological factors have repeatedly been implicated in misuse and SUD, making them ideal targets for further investigation. Technological advances in high-throughput genotyping and imaging enable us to systematically measure genetic variants and body structures with an extremely high level of precision. This technology promises to illuminate the underlying mechanisms that culminate in misuse and SUD. Nevertheless, the massive amount of genetic and imaging data poses great analytical challenges. To address these challenges, new and better analytical methods are required. We will develop advanced statistical methods for high-dimensional genetic and imaging data analysis that can model the multi-faceted nature of substance misuse and SUD more accurately, and account for the complex relationships that we know exist between genetic, imaging and disease outcome data. We will apply these methods to the UK Biobank genetic, imaging and disease outcome data to identify genetic variants and imaging measures that convey risk for substance misuse and SUD. Due to the nature of statistical methods development we anticipate that this project will take up to three years. The results of this project will yield novel genetic and imaging biomarkers for risk of substance misuse and SUD, which may lead to better identification of those individuals at risk for developing misuse and SUD, and further our understanding of the etiology of substance misuse and SUD.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7160 | Fast heritability estimation based on MINQUE and batch training. | Mingsheng Tang (+6) | 2022 | Briefings in Bioinformatics |
| 9560 | GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS | Joshua N. Pritikin (+4) | 2021 | Behavior Genetics |
Enabling scientific discoveries that improve human health