About
Our goal is to test new computational methods for determining the genetic architecture of complex traits, including highly heritable conditions such as Type 1 Diabetes, Alzheimer's, and others. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics. The research will produce improved predictive models which, based on individual genomics, identify individuals at high risk for certain diseases. It will also identify the many alleles associated with this risk. Early intervention with high risk individuals may decrease rates of incidence and reduce health care costs. Elaboration of underlying genetic architecture is important basic science and may lead to improved treatments (e.g., drug development). We wish to obtain access to genomic data and phenotype data relevant to highly heritable disease conditions (e.g., Type 1 Diabetes) as well as complex traits such as height, BMI, cognitive ability. Advanced computational algorithms will be used to study the genetic architecture of these traits. The techniques we plan to use have been the subject of intense recent activity in fields such as optimization, signal processing and machine learning, but so far have just begun to be applied in genomics. Analysis will be performed on high-performance computing clusters. We would like access to the full cohort (SNP genotypes), and several relevant phenotypes.
4 Returns
| Return ID | App ID | Description | Archive Date |
|---|
| 3462 | 15326 | Accurate Genomic Prediction of Human Height | 26 May 2021 |
| 3460 | 15326 | Genetic architecture of complex traits and disease risk predictors | 26 May 2021 |
| 3459 | 15326 | Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer | 26 May 2021 |
| 3461 | 15326 | Sibling validation of polygenic risk scores and complex trait prediction | 26 May 2021 |
