Methods for Using Multivariate Phenotypic Information
in Genetic Linkage Analysis
ABSTRACT:
In genetic linkage analysis, families are examined to find patterns of
genetic marker transmissions that coincide with patterns of disease.
This analysis can identify chromosomal regions likely to harbour
genes that increase susceptibility to disease. However, for complex
diseases, it is likely that multiple genes act on different
symptoms or comorbid conditions to increase risk of disease.
Although clinical data on symptoms is normally available, it is not
clear what is the best way to use such information.
I will describe an extension to "model-free" linkage analysis methods
that uses multivariate clinical data. This method adaptively finds the
individual characteristics that are associated with the strongest evidence
for linkage, through the use of classification and regression trees (CART).
Bootstrapping can be used to stabilize cutpoint selection, and
cross-validation optimizes the size of the regression tree.
The methods will be illustrated on a data set of 68 families ascertained
to have at least two cases of asthma. Due to the adaptive nature
of the algorithm, results must be interpreted cautiously and validated
in independent data.
About the Speaker
Dr. Greenwood is currently an epidemiologist at the Hospital for Sick
Children Research Institute in Toronto. She also holds an assisstant
professor position in the Department of Public Health Sciences at the
University of Toronto. Dr. Greenwood did her PhD in Biostatistics at
the University of Toronto and followed that with a post-doctoral
position at McGill University in Montreal studying Human Genetics.
Prior to moving to Toronto, Dr. Greenwood was an assistant professor
at McGill and a medical reseach scientist at the McGill University
Health Centre Research Institute. Her research interests focus on
statistical techniques in the analysis of genetic data.
Reference
D. C. Rao (1998) CAT scans, PET scans, and genomic scans.
Genetic Epidemiology, 15, 1-18.
