April 2013_Kraft

Peter Kraft

Professor of Epidemiology

655 Huntington Avenue
Building II Room 249A
Boston, Massachusetts 02115
Phone: 617.432.4271


Research Interests

My research concentrates on the design and analysis of genetic association studies, with particular emphasis on studies linking variation in germline DNA to cancer risk. I have played a key role in multiple international consortia studying genetics and other exposures in relation to cancer risk over the last ten years: I have been a member of the statistical working group of the Breast and Prostate Cancer Cohort Consortium since its inception, and currently chair the BPC3 steering committee; I played a leading role in the design and analysis of GWAS of breast, prostate and pancreatic cancers as part of the NCI’s Cancer Genetic Markers of Susceptibility and PanScan projects; and I chair the Analytic Working Group for the NCI’s “post-GWAS” GAME-ON consortium, which aims to better understand the biological mechanisms underlying GWAS-identified cancer risk markers at five cancer sites (including breast and lung) and their public health implications.

I am also the contact PI for the epidemiology project of the breast cancer arm of GAME-ON, which focuses on gene-environment interactions and risk prediction.    I have been the primary statistical geneticist for the Nurses’ Health Study (NHS) and Health Professionals Follow-up Study (HPFS) for over ten years, and oversee the genotype databases for both studies, including genome-wide association data on over 20,000 subjects. I have collaborated on numerous analyses in the NHS and HPFS examining associations between genetic markers, behaviors (including diet and smoking), and risk of complex diseases. I have also collaborated with NHS investigators on the analyses of metabolite profiles in a case-control study of pancreatic cancer.

My current methodological research focuses on 1) efficient and interpretable “gene x environment interaction” analyses, 2) genetic risk prediction using common and rare genetic variation, biomarkers (including metabolites), and clinical and environmental risk factors, and 3) methods linking low-frequency variation, emerging functional annotation, and risk of complex disease.


PhD, Biostatistics, 2002, University of Southern California
MS, Biostatistics, 1998, University of Southern California
MA, Mathematics, 1997, University of Southern California