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Grant Details

Grant Number: 5R03CA128103-02 Interpret this number
Primary Investigator: Wu, Chih-Chieh
Organization: University Of Tx Md Anderson Can Ctr
Project Title: Development of Statistical Approaches Allowing for Genetic Covariates
Fiscal Year: 2008
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DESCRIPTION (provided by applicant): To determine the genetic basis of a complex trait, it is necessary to use methods that take account of the joint effects of multiple genetic components underlying the trait. However, this is not possible using a usual segregation analysis, which is often efficient only when the variation of the trait in a family is largely due to a mutation segregating at a single putative locus. In response to this, we propose to incorporate genetic covariates adjusted for mutation carrier status in segregation analysis models that account for the genetic complexity and heterogeneity of a complex trait. Segregation analysis models that include genetic covariates will be more accurate for modeling complex genetic effects than the usual segregation analysis models. Recently, we used an independent genetic covariate for p53 mutation status in the segregation analyses of families with Li-Fraumeni syndrome. This study will be published in Cancer Research in August. However, the use of independent genetic covariates in that study did not take full account of intrafamilial correlation in hereditary mutation distributions. This problem could be more complicated and serious when mutation genotypes are only available for some relatives in a family. The central theme of this proposal is to develop statistical approaches that allow for dependent genetic covariates. It is novel and desirable to develop dependent genetic covariates that account for intrafamilial correlation in hereditary mutation distributions in segregation analysis models. We propose to use simulation-based approaches to quantitatively determine the effects of dependent genetic covariates. Two types of complex segregation analysis models by maximum likelihood will be used as age-specific risk models in this project. The newly developed statistical approaches will be used to determine the genetic basis of breast cancer in association with mutations in BRCA1/2. The Texas Cancer Genetic Consortium, a Cancer Genetic Network regional center, will provide the breast cancer data.

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Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li-Fraumeni syndrome.
Authors: Wu C.C. , Krahe R. , Lozano G. , Zhang B. , Wilson C.D. , Jo E.J. , Amos C.I. , Shete S. , Strong L.C. .
Source: Human genetics, 2011 Jun; 129(6), p. 663-73.
EPub date: 2011-02-09.
PMID: 21305319
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Effects of measured susceptibility genes on cancer risk in family studies.
Authors: Wu C.C. , Strong L.C. , Shete S. .
Source: Human genetics, 2010 Mar; 127(3), p. 349-57.
EPub date: 2009-12-29.
PMID: 20039063
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Exact Statistical Tests for Heterogeneity of Frequencies Based on Extreme Values.
Authors: Wu C.C. , Grimson R.C. , Shete S. .
Source: Communications in statistics: Simulation and computation, 2010; 39(3), p. 612-623.
PMID: 25558124
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Detection of disease-associated deletions in case-control studies using SNP genotypes with application to rheumatoid arthritis.
Authors: Wu C.C. , Shete S. , Chen W.V. , Peng B. , Lee A.T. , Ma J. , Gregersen P.K. , Amos C.I. .
Source: Human genetics, 2009 Aug; 126(2), p. 303-15.
EPub date: 2009-05-05.
PMID: 19415332
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