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Grant Details

Grant Number: 5R01CA094069-10 Interpret this number
Primary Investigator: Whittemore, Alice
Organization: Stanford University
Project Title: "Statistical Methods for Genetic Epidemiology"
Fiscal Year: 2011
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Abstract

DESCRIPTION (provided by applicant): 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.

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Publications

Performance of prediction models for BRCA mutation carriage in three racial/ethnic groups: findings from the Northern California Breast Cancer Family Registry.
Authors: Kurian AW, Gong GD, John EM, Miron A, Felberg A, Phipps AI, West DW, Whittemore AS
Source: Cancer Epidemiol Biomarkers Prev, 2009 Apr;18(4), p. 1084-91.
EPub date: 2009 Mar 31.
PMID: 19336551
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Prevalence of pathogenic BRCA1 mutation carriers in 5 US racial/ethnic groups.
Authors: John EM, Miron A, Gong G, Phipps AI, Felberg A, Li FP, West DW, Whittemore AS
Source: JAMA, 2007 Dec 26;298(24), p. 2869-76.
PMID: 18159056
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Significance levels for studies with correlated test statistics.
Authors: Shi J, Levinson DF, Whittemore AS
Source: Biostatistics, 2008 Jul;9(3), p. 458-66.
EPub date: 2007 Dec 18.
PMID: 18089626
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Comparison of admixture and association mapping in admixed families.
Authors: Clarke G, Whittemore AS
Source: Genet Epidemiol, 2007 Nov;31(7), p. 763-75.
PMID: 17508341
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Assessing environmental modifiers of disease risk associated with rare mutations.
Authors: Whittemore AS
Source: Hum Hered, 2007;63(2), p. 134-43.
EPub date: 2007 Feb 2.
PMID: 17283442
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Sex steroid hormones in young manhood and the risk of subsequent prostate cancer: a longitudinal study in African-Americans and Caucasians (United States).
Authors: Tsai CJ, Cohn BA, Cirillo PM, Feldman D, Stanczyk FZ, Whittemore AS
Source: Cancer Causes Control, 2006 Dec;17(10), p. 1237-44.
PMID: 17111254
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Nonparametric linkage analysis using person-specific covariates.
Authors: Whittemore AS, Halpern J
Source: Genet Epidemiol, 2006 Jul;30(5), p. 369-79.
PMID: 16671107
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Getting more from digital SNP data.
Authors: El Karoui N, Zhou W, Whittemore AS
Source: Stat Med, 2006 Sep 30;25(18), p. 3124-33.
PMID: 16397858
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Prostate specific antigen levels in young adulthood predict prostate cancer risk: results from a cohort of Black and White Americans.
Authors: Whittemore AS, Cirillo PM, Feldman D, Cohn BA
Source: J Urol, 2005 Sep;174(3), p. 872-6; discussion 876.
PMID: 16093978
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Prevalence of BRCA1 mutation carriers among U.S. non-Hispanic Whites.
Authors: Whittemore AS, Gong G, John EM, McGuire V, Li FP, Ostrow KL, Dicioccio R, Felberg A, West DW
Source: Cancer Epidemiol Biomarkers Prev, 2004 Dec;13(12), p. 2078-83.
PMID: 15598764
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Covariate adjustment in family-based association studies.
Authors: Whittemore AS, Halpern J, Ahsan H
Source: Genet Epidemiol, 2005 Apr;28(3), p. 244-55.
PMID: 15593089
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Classifying disease chromosomes arising from multiple founders, with application to fine-scale haplotype mapping.
Authors: Yu K, Martin RB, Whittemore AS
Source: Genet Epidemiol, 2004 Nov;27(3), p. 173-81.
PMID: 15389930
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Genetic association tests for family data with missing parental genotypes: a comparison.
Authors: Whittemore AS, Halpern J
Source: Genet Epidemiol, 2003 Jul;25(1), p. 80-91.
PMID: 12813729
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Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene.
Authors: Gong G, Whittemore AS
Source: Genet Epidemiol, 2003 Apr;24(3), p. 173-80.
PMID: 12652521
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