Abstract

The overall goal of this project is to develop novel statistical methods to assist in the positional identification of genetic factors influence quantitative traits. Towards this aim we propose six major aims. First, we will refine statistical methods for linkage analysis of complex traits. We propose model development for detecting effects due to gene-environment interactions and from imprinted loci. The developments we propose are applicable to any type of genetic modeling but are specifically studied in applications to variance components models.
Our second aim will evaluate methods for gene localization of quantitative traits when very fine mapping is conducted. For this aim, we will evaluate methods that partition variability into sources attributable to linkage transmission disequilibrium tests with unconditional tests (ANOVA type) that incorporate genome control for varying degrees of population stratification. We will also study Bayesian transmission disequilibrium tests for quantitative traits.
Our third aim develops empirical Bayes approaches to obtain more accurate and precise estimators of variance components in meta-analysis of linkage studies.
Our fourth aim will study empirical Bayes methods for evaluating and characterizing intrastudy and interstudy heterogeneity. Our fifth aim will develop software to assist users in the planning and analysis of studies to identify quantitative traits. Finally, we will apply the methods that we are developing as part of existing studies of obesity, cancer-predisposition and rheumatoid arthritis.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
5R01ES009912-07
Application #
6930938
Study Section
Genome Study Section (GNM)
Program Officer
Mcallister, Kimberly A
Project Start
1999-01-01
Project End
2007-07-31
Budget Start
2005-08-26
Budget End
2006-07-31
Support Year
7
Fiscal Year
2005
Total Cost
$496,021
Indirect Cost

  Institution

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

  Publications

Williams, K Y; Yoo, Yun Joo; Patki, Amit et al. (2011) Real data examples in statistical methods papers: Tremendously valuable, and also tremendously misvalued. Stat Interface 4:267-272
Peng, Gang; Luo, Li; Siu, Hoicheong et al. (2010) Gene and pathway-based second-wave analysis of genome-wide association studies. Eur J Hum Genet 18:111-7
Dong, Hua; Siu, Hoicheong; Luo, Li et al. (2010) Investigation gene and microRNA expression in glioblastoma. BMC Genomics 11 Suppl 3:S16
Luo, Li; Peng, Gang; Zhu, Yun et al. (2010) Genome-wide gene and pathway analysis. Eur J Hum Genet 18:1045-53
Remmers, Elaine F; Cosan, Fulya; Kirino, Yohei et al. (2010) Genome-wide association study identifies variants in the MHC class I, IL10, and IL23R-IL12RB2 regions associated with Behçet's disease. Nat Genet 42:698-702
Tiwari, Hemant K; Patki, Amit; Allison, David B (2010) Within-Cluster Resampling for Analysis of Family Data: Ready for Prime-Time? Stat Interface 3:169-176
Ma, Jianzhong; Amos, Christopher I (2010) Theoretical formulation of principal components analysis to detect and correct for population stratification. PLoS One 5:
Ma, Jianzhong; Daw, E Warwick; Amos, Christopher I (2010) Power of competing strategies of linkage analysis for complex traits. Hum Hered 70:55-62
Padilla, Miguel A; Divers, Jasmin; Vaughan, Laura K et al. (2009) Multiple imputation to correct for measurement error in admixture estimates in genetic structured association testing. Hum Hered 68:65-72
Vaughan, Laura K; Divers, Jasmin; Padilla, Miguel et al. (2009) The use of plasmodes as a supplement to simulations: A simple example evaluating individual admixture estimation methodologies. Comput Stat Data Anal 53:1755-1766

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