The availability of high-dimensional single-nucleotide (SNP) data for large samples of individuals with and without disease presents unprecedented opportunities for genetic epidemiologists, but also many statistical challenges. Our ability to bring these opportunities to fruition depends on the development and application of sound and innovative statistical methods. Areas of particularly great need include methods for dealing with multiple hypothesis-testing problems (multiple comparison issues), and methods for combining data from multiple sources to achieve sharper inferences (data synthesis issues). The goals of this research are to develop new or improved methods for addressing these issues, and to apply them to data on cancers of the breast, ovary and prostate. To accomplish these goals, the investigators will build on more than 15 years of previous work. Specifically, in 1988 the National Cancer Institute (NCI) awarded an Outstanding Investigator Grant (OIG) to the Principle Investigator for the development and application of new and improved statistical methods for use in epidemiological research. In 1995 this grant was renewed until NCI terminated the program in 2001. The research was then funded by R01 CA94069 for the period January 2002 to December 2006. This competing renewal application requests funds to continue this work and to apply it to new areas of emerging importance.
The specific aims are fourfold: 1) to evaluate new and existing methods for controlling confounding and tail count variability in case-control genome-wide association (GWA) studies;2) to evaluate risks associated with missense mutations of unknown significance in genes of established disease relevance;3) to combine multiple independent sources of data in assessing the joint carcinogenic effects of groups of genes;and 4) to integrate data on tumor and patient characteristics for improved assessment of phenotype-specific etiology. We will evaluate the new methods by simulations, and we will illustrate them by application to existing data on cancers of the breast, ovary and prostate, and emerging data from GWA studies of breast and prostate cancer. The existing data have been collected either by the investigators and the consultants to this project, or as part of three collaborations in which the investigators participate: the Breast Cancer Family Registry (Breast-CFR), the Ovarian Cancer Association Consortium (OCAC), and the International Consortium for Prostate Cancer Genetics (ICPCG). In summary, we need sound, reliable methods for analyzing the vast amounts of emerging genetic data from individuals with and without a given type of cancer. The goals of this research are to develop such methods, and thereby to help generate new knowledge useful for cancer prevention and treatment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Verma, Mukesh
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code
Scannell Bryan, Molly; Argos, Maria; Andrulis, Irene L et al. (2017) Limited influence of germline genetic variation on all-cause mortality in women with early onset breast cancer: evidence from gene-based tests, single-marker regression, and whole-genome prediction. Breast Cancer Res Treat 164:707-717
Ioannidis, Nilah M; Rothstein, Joseph H; Pejaver, Vikas et al. (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99:877-885
McGuire, Valerie; Hartge, Patricia; Liao, Linda M et al. (2016) Parity and Oral Contraceptive Use in Relation to Ovarian Cancer Risk in Older Women. Cancer Epidemiol Biomarkers Prev 25:1059-63
Quante, Anne S; Whittemore, Alice S; Shriver, Tom et al. (2015) Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk. J Natl Cancer Inst 107:
Gong, Gail; Quante, Anne S; Terry, Mary Beth et al. (2014) Assessing the goodness of fit of personal risk models. Stat Med 33:3179-90
Sieh, Weiva; Rothstein, Joseph H; McGuire, Valerie et al. (2014) The role of genome sequencing in personalized breast cancer prevention. Cancer Epidemiol Biomarkers Prev 23:2322-7
Ahsan, Habibul; Halpern, Jerry; Kibriya, Muhammad G et al. (2014) A genome-wide association study of early-onset breast cancer identifies PFKM as a novel breast cancer gene and supports a common genetic spectrum for breast cancer at any age. Cancer Epidemiol Biomarkers Prev 23:658-69
Kurian, Allison W; Hare, Emily E; Mills, Meredith A et al. (2014) Clinical evaluation of a multiple-gene sequencing panel for hereditary cancer risk assessment. J Clin Oncol 32:2001-9
Quante, Anne S; Whittemore, Alice S; Shriver, Tom et al. (2012) Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance. Breast Cancer Res 14:R144
Zhou, Baiyu; Shi, Jianxin; Whittemore, Alice S (2011) Optimal methods for meta-analysis of genome-wide association studies. Genet Epidemiol 35:581-91

Showing the most recent 10 out of 31 publications