Grant Details
Grant Number: |
1R03CA128103-01 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: |
2007 |
Abstract
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.
Publications
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.
PMID: 21305319
Related Citations
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.
PMID: 20039063
Related Citations
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
Related Citations
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.
PMID: 19415332
Related Citations