? ? 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. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
5R03CA128103-02
Application #
7380043
Study Section
Special Emphasis Panel (ZCA1-SRRB-F (J1))
Program Officer
Freedman, Andrew
Project Start
2007-04-01
Project End
2010-03-31
Budget Start
2008-04-01
Budget End
2010-03-31
Support Year
2
Fiscal Year
2008
Total Cost
$77,000
Indirect Cost
Name
University of Texas MD Anderson Cancer Center
Department
Type
Schools of Medicine
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Wu, Chih-Chieh; Krahe, Ralf; Lozano, Guillermina et al. (2011) Joint effects of germ-line TP53 mutation, MDM2 SNP309, and gender on cancer risk in family studies of Li-Fraumeni syndrome. Hum Genet 129:663-73
Wu, Chih-Chieh; Grimson, Roger C; Shete, Sanjay (2010) Exact Statistical Tests for Heterogeneity of Frequencies Based on Extreme Values. Commun Stat Simul Comput 39:612-623
Wu, Chih-Chieh; Strong, Louise C; Shete, Sanjay (2010) Effects of measured susceptibility genes on cancer risk in family studies. Hum Genet 127:349-57
Wu, Chih-Chieh; Shete, Sanjay; Chen, Wei V et al. (2009) Detection of disease-associated deletions in case-control studies using SNP genotypes with application to rheumatoid arthritis. Hum Genet 126:303-15