Genetic and environment causes of cancers and other chronic diseases ...read more
Developing methods for and analyzing big data to understand genetic and environment causes of cancers and other chronic diseases and henceforth develop risk prediction models for applications in targeted disease prevention.
Park J, Wacholder S, Gail M, Peters U, Jacobs K, Chanock S and Chatterjee N. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nature Genetics, 2010, 42:570-5, 2010
Chatterjee N, Wheeler B, Sampson S, Hartge P, Chanock S and Park J. Projecting the performance of risk prediction from polygenic analyses of genome-wide association studies. Nature Genetics 2013, 45:400-5.
Mass P, Barrdal M, Joshi AD, Auer PL, Gaudet MM, Milne RL, Schumacher FR, Anderson WF, Check D, Chattopadhyay S, Baglietto L, Berg CD, Chanock SJ, Cox DG, Figueroa JD, Gail MH, Graubard BI, Haiman CA, Hankinson SE, Hoover RN, Lindström S, Overvad K, Romieu I, Sanchez M, Southey MC, Stram DO, Tumino R, VanderWeele TJ, Willett WC, Zhang S, Buring JE, Canzian F, Gapstur SM, Henderson BE, Hunter DJ, Giles GG, Prentice RL, Ziegler RG, Kraft P, Garcia-Closas M, Chatterjee, N. Breast cancer risk from modifiable and non-modifiable risk factors among Caucasian women in the United States. Journal of the American Medical Association (JAMA)-Oncology 2016 2016 May 26 10.1001/jamaoncol.2016.1025.
Chatterjee N, Chen Y.H., Maas P and Carroll R.J. Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources. J of Am Stat Assoc 2016, 111:107-117 (followed with Discussion).
Chatterjee N, Shi J and Garcia-Closas M. Development and application of polygenic risk prediction models for stratified disease prevention. Nature Review Genetics, 2016, May 3. doi: 10.1038/nrg.2016.27.
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