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Discovering the causes of cancer and the means of prevention

Next Generation Statistical Methods in the Post-Genome Wide Association Studies - Dr. Chatterjee

Biostatistics Branch Seminar Series

March 4, 2020 | 10:30 AM – 11:30 AM

NCI Shady Grove 6E032/034 Rockville, Maryland

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Nilanjan Chatterjee, Ph.D.
Bloomberg Distinguished Professor
Department of Biostatistics, Bloomberg School of Public Health
Department of Oncology, School of Medicine
Johns Hopkins University


In the early years of genome-wide association studies, data analysis primarily relied on fairly simplistic methods, such as running millions of univariate linear or logistic regressions, one for each genetic variant. Recently, however, as the sample sizes for some GWAS have become extremely large and various types of other genomic data have increasingly become available, analysis of such data has also become much more complex and statistically sophisticated. Because this field is mature and stable, there is tremendous opportunity to develop novel methods to address new types of questions using existing or anticipated data sources. In this talk, the speaker will try to make a case for the golden opportunities for statisticians by drawing examples from our recent work in the areas of estimation of genetic effect-size distribution, integrative analysis of GWAS and eQTL studies, modeling tumor heterogeneity and Mendelian randomization. The speaker will try to highlight gaps in these and other areas that remain fertile areas of future research.

**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods.  The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**