Skip to main content

COVID-19 Resources

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.cdc.gov/coronavirus

Get the latest research information from NIH: https://www.covid19.nih.gov

Grant Details

Grant Number: 5R03CA130045-02 Interpret this number
Primary Investigator: Mukherjee, Bhramar
Organization: University Of Michigan At Ann Arbor
Project Title: Synergism of Gene and Environment in Cancer Studies: a New Bayesian Approach
Fiscal Year: 2008


Abstract

DESCRIPTION (provided by applicant): The past decade has witnessed the growing importance of statistical planning and inferential techniques in providing solutions to complex problems in medical and health sciences. Two scientific teams are currently dominating clinical medicine and public health: the molecular biology approach with an emphasis on genetics, and the quantitative approach with an emphasis on epidemiology. The developments in these areas jointly are making fundamental contributions to the study of cancer. This application lies in that new interface of human genetics, epidemiology and statistics in cancer research. Case-control studies are being increasingly used for studying the association between a disease and a candidate gene. However, except for some rare diseases, such as Huntington or Tay Sachs disease which may be the result of a deficiency of a single gene product, most common human diseases like cancer have a multifactorial etiology involving complex interplay of many genetic and environ- mental factors. By identifying and characterizing such complicated gene-environment interactions through clinical and epidemiological studies, one has more opportunities to study etiology, diagnosis, prognosis and treatment of complex diseases. In case-control studies of gene-environment association with disease, when genetic and environmental exposures can be assumed to be independent in the underlying population, one may exploit the independence in order to derive more efficient estimation techniques than the traditional logistic regression analysis. Many of the classical results for case- control analysis, which assume the covariate distribution to be non-parametric, do not hold under a constrained space of exposure distributions. However, the gain in efficiency of modern retrospective methods comes at the cost of lack of robustness, since large biases are introduced in the retrospective estimates under violation of the gene-environment independence assumption. The main objective of this research application is to find a natural analytical tool to solve the model specification dilemma of modern retrospective analysis of studies of gene-environment interaction, under three commonly used epidemiological designs. We posit the problem in a Bayesian framework that incorporates uncertainty regarding the assumed constraint of gene-environment independence in a natural data adaptive way. Preliminary results indicate that the proposed estimator is still able to maintain attractive efficiency properties, without relying on unverifiable model constraints. Epidemiologists have often anguished whether to use the case-control or the case-only estimator of gene-environment interaction for a given study, and the current application tries to resolve the question in a novel Bayesian framework. The methods developed may be routinely applied to various epidemiological studies of gene-environment interaction.



Publications

Bayesian modeling for genetic anticipation in presence of mutational heterogeneity: a case study in Lynch syndrome.
Authors: Boonstra P.S. , Mukherjee B. , Taylor J.M. , Nilbert M. , Moreno V. , Gruber S.B. .
Source: Biometrics, 2011 Dec; 67(4), p. 1627-37.
EPub date: 2011-05-31.
PMID: 21627626
Related Citations

High risk of colorectal and endometrial cancer in Ashkenazi families with the MSH2 A636P founder mutation.
Authors: Mukherjee B. , Rennert G. , Ahn J. , Dishon S. , Lejbkowicz F. , Rennert H.S. , Shiovitz S. , Moreno V. , Gruber S.B. .
Source: Gastroenterology, 2011 Jun; 140(7), p. 1919-26.
EPub date: 2011-03-16.
PMID: 21419771
Related Citations

Missing exposure data in stereotype regression model: application to matched case-control study with disease subclassification.
Authors: Ahn J. , Mukherjee B. , Gruber S.B. , Sinha S. .
Source: Biometrics, 2011 Jun; 67(2), p. 546-58.
EPub date: 2010-06-16.
PMID: 20560931
Related Citations

Risk of colorectal cancer in self-reported inflammatory bowel disease and modification of risk by statin and NSAID use.
Authors: Samadder N.J. , Mukherjee B. , Huang S.C. , Ahn J. , Rennert H.S. , Greenson J.K. , Rennert G. , Gruber S.B. .
Source: Cancer, 2011-04-15; 117(8), p. 1640-8.
EPub date: 2010-11-08.
PMID: 21472711
Related Citations

MRE11 deficiency increases sensitivity to poly(ADP-ribose) polymerase inhibition in microsatellite unstable colorectal cancers.
Authors: Vilar E. , Bartnik C.M. , Stenzel S.L. , Raskin L. , Ahn J. , Moreno V. , Mukherjee B. , Iniesta M.D. , Morgan M.A. , Rennert G. , et al. .
Source: Cancer research, 2011-04-01; 71(7), p. 2632-42.
EPub date: 2011-02-07.
PMID: 21300766
Related Citations

A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome.
Authors: Boonstra P.S. , Gruber S.B. , Raymond V.M. , Huang S.C. , Timshel S. , Nilbert M. , Mukherjee B. .
Source: Genetic epidemiology, 2010 Nov; 34(7), p. 756-68.
PMID: 20878717
Related Citations

Case-control studies of gene-environment interaction: Bayesian design and analysis.
Authors: Mukherjee B. , Ahn J. , Gruber S.B. , Ghosh M. , Chatterjee N. .
Source: Biometrics, 2010 Sep; 66(3), p. 934-48.
PMID: 19930190
Related Citations

Shrinkage estimation for robust and efficient screening of single-SNP association from case-control genome-wide association studies.
Authors: Luo S. , Mukherjee B. , Chen J. , Chatterjee N. .
Source: Genetic epidemiology, 2009 Dec; 33(8), p. 740-50.
PMID: 19434716
Related Citations

Bayesian inference for the stereotype regression model: Application to a case-control study of prostate cancer.
Authors: Ahn J. , Mukherjee B. , Banerjee M. , Cooney K.A. .
Source: Statistics in medicine, 2009-11-10; 28(25), p. 3139-57.
PMID: 19731262
Related Citations

Calculation of risk of colorectal and endometrial cancer among patients with Lynch syndrome.
Authors: Stoffel E. , Mukherjee B. , Raymond V.M. , Tayob N. , Kastrinos F. , Sparr J. , Wang F. , Bandipalliam P. , Syngal S. , Gruber S.B. .
Source: Gastroenterology, 2009 Nov; 137(5), p. 1621-7.
EPub date: 2009-07-18.
PMID: 19622357
Related Citations

Semiparametric Bayesian modeling of random genetic effects in family-based association studies.
Authors: Zhang L. , Mukherjee B. , Hu B. , Moreno V. , Cooney K.A. .
Source: Statistics in medicine, 2009-01-15; 28(1), p. 113-39.
PMID: 18792083
Related Citations

Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs.
Authors: Mukherjee B. , Ahn J. , Gruber S.B. , Rennert G. , Moreno V. , Chatterjee N. .
Source: Genetic epidemiology, 2008 Nov; 32(7), p. 615-26.
PMID: 18473390
Related Citations

Fitting stratified proportional odds models by amalgamating conditional likelihoods.
Authors: Mukherjee B. , Ahn J. , Liu I. , Rathouz P.J. , Sánchez B.N. .
Source: Statistics in medicine, 2008-10-30; 27(24), p. 4950-71.
PMID: 18618428
Related Citations

Association between 24-hour urinary cadmium and pulmonary function among community-exposed men: the VA Normative Aging Study.
Authors: Lampe B.J. , Park S.K. , Robins T. , Mukherjee B. , Litonjua A.A. , Amarasiriwardena C. , Weisskopf M. , Sparrow D. , Hu H. .
Source: Environmental health perspectives, 2008 Sep; 116(9), p. 1226-30.
PMID: 18795167
Related Citations

Inference of the haplotype effect in a matched case-control study using unphased genotype data.
Authors: Sinha S. , Gruber S.B. , Mukherjee B. , Rennert G. .
Source: The international journal of biostatistics, 2008-05-08; 4(1), p. Article 6.
EPub date: 2008-05-08.
PMID: 20231916
Related Citations




Back to Top