PLACE:319 Snedecor Hall

SPEAKER:
Jeffrey Morris,
Texas A & M University

TITLE:
Parametric and Nonparametric Methods for Understanding the Relationship Between Carcinogen Induced DNA Adduct Levels in Distal and Proximal Regions of the Colon

ABSTRACT:

Joint work with Joanne R. Lupton, Allen Chair of Nutrition, Texas A & M University

An  important problem in studying the etiology of colon cancer is understanding the relationship between DNA adduct levels (broadly, DNA damage) in cells within colonic crypts in distal and proximal parts of the colon, following treatment with a carcinogen and different type of diets.  In particular, it is important to understand whether rats who have elevated adduct levels in particular positions in distal region crypts also have elevated levels in the same positions of the crypts in proximal regions, and whether this relationship
depends on diet.  We cast this problem as estimating the correlation function of two responses as a function of a covariate for studies where both responses are measured on the same experimental units but not the same subsampling units.  The measurements of the responses are taken at the subsampling level for various levels of the covariate.  Parametric and nonparametric methods are developed and applied to a data set from an ongoing study, leading to potentially important and surprising biological results. Theoretical calculations suggest that the nonparametric method, based on nonparametric regression, should in fact have statistical properties nearly the same as if the functions nonparametrically estimated were known. The methodology used in this paper can be applied to other settings when the goal of the study is to model the correlation of two continuous repeated measurement responses as a function of a covariate, while the two responses of interest can be measured on the same experimental units but not on the same subsampling units.  In our example, the two responses were measured in two different regions of the colon. Quantitative trait loci (QTL) are genes that affect the quantitative characteristics of plants and animals.  QTL are typically mapped by searching for associations between genetic markers and the trait of interest.  New technologies are enabling the construction of highly dense maps of molecular markers.  QTL mapping in line crosses can be viewed as a regression problem with a large number of highly collinear categorical predictor variables.  Strategies for detecting QTL and estimating their effects through principal components regression will be presented.
 

COFFEE: 3:45 p.m., 104 Snedecor Hall