DESCRIPTION: Our long-term objective has been to develop statistical
methods for fitting models for complex traits to family data in order
to test hypotheses about genetic effects and gene-environment
interactions and to localize genes. We have pursued a variety of
approaches, including parametric models based on an extension of the
proportional hazards model to family data to incorporate major genes and
polygenes; MCMC and GEE methods for fitting complex genetic likelihoods;
Bayesian methods for models with many parameters; and investigation of
the bias and relative efficiency of various study designs In this cycle,
we propose to split our activities into two separate grants: in this
competing continuation application, we focus on the design and analysis
of family studies for testing candidate gene associations and residual
familial aggregation; a new proposal will continue the research on
computational methods. Specifically, in this application, we propose
to;
1. Investigate study designs and analysis methods that allow for
efficient and unbiased estimation of population parameters (e.g.
penetrance and population allele frequency);
2. Develop models for "complex diseases" (including GCS and gag
interactions, age effects, etc.);
3. Develop conditional and marginal model approaches to allow for
residual familial aggregation in major gene models for multivariate
survival data; and
4. Explore extensions of these designs for gene mapping studies aimed
at finding genes that interact with environmental agents.
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- The DCCPS Team.
