This Interdisciplinary Training Grant in Biostatistics and Computational Biology proposal from the Harvard School of Public Health (HSPH) is a renewal application that represents an expansion of the existing interdisciplinary training grant in biostatistics at HSPH.
It aims at addressing the critical need in the "omits" era for well-trained quantitative genomic scientists who have a strong understanding of, and commitment to, cutting-edge methodological and collaborative research at the intersection of molecular biology, biostatistics, bioinformatics, computational biology, and genetic epidemiology to analyze, integrate and interpret high- dimensional genomic and environmental data. The training program will involve active participation by over thirty accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular biologists with the goal of providing our trainees with experience in all essential elements of this emerging area. The goals of our proposed training program are: * To train high-quality quantitative researchers who have excellent biological, statistical and computational knowledge, and are capable of conducting cutting-edge methodological and collaborative research at the intersection of biostatistics, bioinformatics and computational biology, genetic epidemiology, and molecular biology; * To train quantitative researchers to become strong leaders and effective communicators in an interdisciplinary research environment, and to enable them to conduct translational genomic research from basic sciences to population and clinical sciences focused on developing effective strategies for disease prevention, intervention, and treatments. Trainees will be pre-doctoral students at HSPH in the Departments of Biostatistics and Epidemiology, which will jointly administer the grant. The program proposes initial support of eight students in year 1, and two additional trainees in years 2-5. This training program combines elements of training in both "wet" labs in biological science and "dry" labs in biostatistics, computational biology, and genetic epidemiology, accomplished through lab rotations and directed interdisciplinary research that will prepare graduates to become leading quantitative genomic scientists.
Groundbreaking research and discovery in the life sciences in the 21st century are more interdisciplinary than ever. To expedite scientific advances in the "omits" era, it is critical to train the next generation of quantitative health science students who are strong in biostatistics, computational biology, molecular biology and genetics epidemiology, and who have enough basic knowledge that they can easily communicate and work with colleagues who have complementary areas of expertise.
|Yung, Godwin; Lin, Xihong (2016) Validity of using ad hoc methods to analyze secondary traits in case-control association studies. Genet Epidemiol 40:732-743|
|Lu, Donghao; Sinnott, Jennifer A; ValdimarsdÃ³ttir, Unnur et al. (2016) Stress-Related Signaling Pathways in Lethal and Nonlethal Prostate Cancer. Clin Cancer Res 22:765-72|
|Krier, Joel; Barfield, Richard; Green, Robert C et al. (2016) Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med 8:20|
|Sinnott, Jennifer A; Rider, Jennifer R; Carlsson, Jessica et al. (2015) Molecular differences in transition zone and peripheral zone prostate tumors. Carcinogenesis 36:632-8|
|Hsu, Jessie J; Finkelstein, Dianne M; Schoenfeld, David A (2015) Outcome-Driven Cluster Analysis with Application to Microarray Data. PLoS One 10:e0141874|
|Hayeck, Tristan J; Zaitlen, Noah A; Loh, Po-Ru et al. (2015) Mixed model with correction for case-control ascertainment increases association power. Am J Hum Genet 96:720-30|
|Martin, Neil E; Gerke, Travis; Sinnott, Jennifer A et al. (2015) Measuring PI3K Activation: Clinicopathologic, Immunohistochemical, and RNA Expression Analysis in Prostate Cancer. Mol Cancer Res 13:1431-40|
|Liu, Shelley H; Erion, Gabriel; Novitsky, Vladimir et al. (2015) Viral Genetic Linkage Analysis in the Presence of Missing Data. PLoS One 10:e0135469|
|Rider, Jennifer R; Fiorentino, Michelangelo; Kelly, Rachel et al. (2015) Tumor expression of adiponectin receptor 2 and lethal prostate cancer. Carcinogenesis 36:639-47|
|Parast, Layla; Tian, Lu; Cai, Tianxi (2014) Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc 109:384-394|
Showing the most recent 10 out of 32 publications