Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data

Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data

Michael A. Newton , and Yoonjung Lee.

Biometrics , 56, 1088-1097, 2000.

(Originally issued May 7, 1999, as Department of Biostatistics and Medical Informatics Technical Report #135.)

Abstract:

Cancerous tumor growth creates cells with abnormal DNA. Allelic-loss experiments identify genomic deletions in cancer cells, but sources of variation and intrinsic dependencies complicate inference about the location and effect of suppressor genes; such genes are the target of these experiments and are thought to be involved in tumor development. We investigate properties of an instability-selection model of allelic-loss data, including likelihood-based parameter estimation and hypothesis testing. By considering a special complete-data case, we derive an approximate calibration method for hypothesis tests of sporadic deletion. Parametric bootstrap and Bayesian computations are also developed. Data from three allelic-loss studies are reanalyzed to illustrate the methods.
Key words: Allelic imbalance; Cancer gene mapping; Chromosomal deletions; Correlated binary data; LOD score; Loss of heterozygosity.

This manuscript is a follow up to

M.A. Newton , M.N. Gould, C. A. Reznikoff, and J.D. Haag (1998). On the statistical analysis of allelic-loss data. Statistics in Medicine 17 , 1425-1445.


Data Sets Analyzed (in R/Splus dput/dget format):
Allelic loss data is in the matrix data (LOH=1,MOH=0,no data=-1); marker positions relative to (0,1) are in the vector pos .

R/Splus code