The recent advances in high-throughput genomic technology have profoundly transformed biomedical research and provided tremendous opportunities for mathematical and computational research to contribute to biomedical research. The next generation of biomedical scientist will have significant strength in both the biological sciences and the mathematical and computational sciences. However, training such interdisciplinary researchers is very difficult. Most graduate programs and other training programs focus heavily on one of these disciplines. We propose to partner with the UCLA Institute for Pure and Applied Mathematics to offer an educational program targeted at fostering interdisciplinary research. This training program will consist of a one week didactic portion covering current research in the intersection of biological and computational research. The remainder of the program will be three week research practicum designed to provide training in research and provide participant's access to leading researchers as mentors.
The development of high-throughput genomic technology has transformed biomedical sciences and provides limitless potential for developing new treatments for disease. However, analyzing the data generated by these technologies requires tremendous computational resources and significant computational expertise by the researchers. The education program described in this proposal is a program designed to train the next generation of biomedical researchers.
| Weissbrod, Omer; Flint, Jonathan; Rosset, Saharon (2018) Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics. Am J Hum Genet 103:89-99 |
