DESCRIPTION (Applicant's abstract): Variation is a fundamental property of data
obtained in modern cancer research, be they genomic changes, gene expression
abnormalities, or phenotypic data from gene mapping. Statistical methods arise
from a logic that decomposes variation into that which is sporadic and that
which may have some biological significance. The purpose of this research
proposal is to develop statistical methods tailored to current and emerging
data structures in cancer biology. If successful, this research will improve
inference about cancer biology by enabling more efficient and robust extraction
of information from the complex data that will be upon us. Four specific
problems will be tackled. New microarray-based technologies have enabled DNA
sequence copy number variations to be measured at very high resolution in
cancer tumor cells, thus enhancing the characterization of suppressor genes and
oncogenes. Sources of variation complicate inference. The first aim is to
develop statistical methods for analyzing copy-number variation by extending
existing models of allelic-imbalance data. New mathematical formulations and
inference methods are proposed for this purpose. Microarray technology is also
creating a wealth of data on gene expression in cancer cells. In Aim 2,
hierarchical modeling methods are proposed to characterize the normal variation
of these profiles, to enable comparison at various levels, such as among genes,
or among microarrays, and to enable data reduction via nonparametric mixture
modeling. The third aim concerns interval mapping methods which have for some
time enabled the localization of genes in controlled animal experiments.
Methods which are nonparametric in the phenotype distribution are highly
robust, but available methods can lose too much information by working with
sums of ranks. Sensitive nonparametric interval mapping methodology is proposed
to enhance efficiency. Finally, phenotype-driven mutagenesis experiments based
on quantitative phenotypes require statistical methods to efficiently screen
mutagenized animals and to trace mutant genotypes through progeny testing and
mapping. Parametric and nonparametric methods are proposed for this purpose.
Developments on these four specific aims are linked by common biological
features, by structural similarities in the statistical models, and in the
computational issues raised by data analysis.
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