ABSTRACT; Period 2003-2007
Techniques will be developed for genetic analysis of complex diseases
segregating in
extended pedigrees of arbitrary structure.
Diseases such as hypertension
and psychiatric illness have both environmental and genetic
components. However, identification of genes contributing to increased
risk of such diseases has been limited by both computational and
statistical constraints.
The development of Markov chain Monte Carlo
(MCMC) methods have helped to overcome these limitations,
providing information for gene localization, trait model
estimation, haplotype analysis, and genetic map analyses, using data on
extended pedigrees.
The research now proposed is concerned with the
extension of MCMC methods in several areas.
We will develop improved methods for the MCMC analysis of gene
descent in extended pedigrees, given data at a dense genome screen of
markers, together with
methods for the detection of associations or relationships at the
population level.
The current approach to efficient
MCMC sampling
on pedigrees will be extended to allow explicit models for complex
phenotypes to be included within Bayesian and
likelihood-based joint segregation and linkage analyses.
Where marker and phenotypic data are available on members of an
extended pedigree,
current methods will be extended
to handle additional trait measures such as multivariate
and ordered categorical phenotypes, to
incorporate more complex patterns of censoring, and to allow for
missing covariate information.
We will develop measures of haplotype
identity by descent (ibd) computationally and statistically well suited to
linkage detection using data on extended pedigrees. We will compare
the computational efficiency and statistical power of
ibd-based linkage-detection methods with those of Bayesian and
likelihood-based joint segregation and linkage analysis.
We will develop MCMC methods for analysis of genetic map
heterogeneity, genetic interference, and pedigree and genotyping
errors, and assess the impact of these on the localization of
genes contributing to complex traits. Methods will be evaluated by
analyses on several simulated and real data sets, including pedigrees
segregating cardiovascular disease or
psychiatric disorders.
These real data sets include several on which are
available genome-wide
marker screens or more localized multigene haplotypes.
Finally, software will be developed that implements these
methods, and will be documented and released for use by practitioners.