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Department of Health Informatics
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Ph.D. Dissertation Defense
STATISTICAL STRATEGIES FOR GENE
MAPPING STUDIES OF COMPLEX DISEASE
By Andrea Lynn Maes
Ph.D. Program in Biomedical Informatics
UMDNJ- School of Health Related Professions
Research Advisor/Committee Chair: Scott R. Diehl, Ph.D.
Professor of Oral Biology
Director, Center for Pharmacogenomics and Complex Disease Research
UMDNJ-New Jersey Dental School
Committee: Tara C. Matise, Ph.D.
Associate Professor of Genetics
Rutgers University, Piscataway, NJ
Committee: Masayuki Shibata, Ph.D.
Associate Professor of Biomedical Informatics
UMDNJ-School of Health Related Professions
Monday September 10, 2007
1:00 p.m.
D986, Oral Health Pavilion (NJDS)
The University is an affirmative action/equal opportunity employer
Abstract
Disease gene mapping is a powerful strategy for uncovering the genetic basis of
complex human diseases. Various methodological and statistical approaches for linkage
and association analyses have been implemented to identify the genes underlying
oligogenic traits. Careful consideration needs to be given to the design aspects of such
studies in order to maximize their potential for detecting disease-causing variants.
These include subject ascertainment and DNA marker map selection, as well as their
effects on the statistical analysis of the data. This thesis first examines effects of
pedigree structure, ascertainment, map density, and genotyping error on linkage
analyses of affected sibling pairs (ASPs) when maps of either SNPs or microsatellite
markers are used. The predictive power of the entropy-based information content (IC)
for two common measures of linkage is explored under varying conditions of the above
design characteristics. For genetic association studies, various study designs and
statistical analysis methods that can handle both family-based and case-control data
are compared. These approaches are contrasted with traditional family-based tests.
Finally, a novel procedure for reducing the genotyping effort required for the analysis of
pedigrees is explored. This method uses previously obtained linkage data in order to
infer a subset of genotypes for a genetic association analysis. The primary conclusions
are: i) unaffected siblings add as much or more power to a linkage study of ASPs as
both parents; ii) the IC statistic is an insensitive predictor of linkage power; iii) clusters of
tightly linked SNPs perform well for linkage; iv) a modest genotyping error rate of 1% is
tolerable for linkage analysis of ASPs when additional family members are available for
genotyping; v) statistical tests that accommodate both family and case-control data are
powerful for detecting genetic association; vi.) enriching cases and controls based on
family history of disease can provide very substantial increases in power for gene
mapping by association; and vii) only limited increases in power for genetic association
are obtained when inferring genotypes for a subset of a family, and the reduced cost of
genotyping may make this strategy an inefficient approach.
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