The overall goal of this project is to evaluate statistical genetic methods for detecting, characterizing, and mapping the genes that influence complex diseases and their precursors and risk factors. We will pursue this goal by: (l) Continuing the organization of the Genetic Analysis Workshops (GAWs). The Genetic Analysis Workshops are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen for their relevance to current analytical issues in genetic epidemiology, and sets of computer-simulated or real data are distributed to investigators worldwide. Results of analyses are discussed and compared at a 2 1/2 day meeting. GAW10 will be held in 1996, and GAW11, in 1998. Planning and data distribution for GAW12 will be ongoing by the end of the requested period of support in 2000. (2) Conducting evaluations of methods of genetic analysis. Some issues concerning the strengths and limitations of statistical genetic methods can be more readily addressed outside a workshop setting. Using computer- simulated data, we will address questions of power or sensitivity, specificity, and robustness of various analytical methods. Because many current analytical problems in genetic epidemiology involve complex diseases with associated quantitative precursors and risk factors, we will focus on analysis of such quantitative traits. We will evaluate (l) the utility of multivariate linkage analysis for detecting loci that contribute to quantitative traits, and (2) the effects on statistical inference of including or disregarding genotype x environment (GxE) interaction. This research will complement the Workshops in the sense that many of the same simulation programs and data sets will be used, and special problems that are revealed in the Workshops will be pursued in greater detail. (3) Distributing simulation programs, simulated data, and programs for genetic analysis. As a result of our efforts in generating data for the GAWs and in evaluating methods of genetic analysis, we are developing computer programs and simulated data sets that are of potential value to other investigators. We will continue to document these programs and data sets and provide them to interested investigators.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM031575-17S1
Application #
6314979
Study Section
Epidemiology and Disease Control Subcommittee 2 (EDC)
Program Officer
Eckstrand, Irene A
Project Start
1983-04-01
Project End
2003-08-31
Budget Start
2000-08-01
Budget End
2000-08-31
Support Year
17
Fiscal Year
2000
Total Cost
$18,500
Indirect Cost
Name
Southwest Foundation for Biomedical Research
Department
Type
DUNS #
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Wei, Runmin; Wu, Yanyan (2018) Modification effect of fenofibrate therapy, a longitudinal epigenomic-wide methylation study of triglycerides levels in the GOLDN study. BMC Genet 19:75
Xia, Xiaoxuan; Weng, Haoyi; Men, Ruoting et al. (2018) Incorporating methylation genome information improves prediction accuracy for drug treatment responses. BMC Genet 19:78
Shen, Xiaoxi; Lu, Qing (2018) Joint analysis of genetic and epigenetic data using a conditional autoregressive model. BMC Genet 19:71
Justice, Anne E; Howard, Annie Green; Fernández-Rhodes, Lindsay et al. (2018) Direct and indirect genetic effects on triglycerides through omics and correlated phenotypes. BMC Proc 12:22
Lent, Samantha; Xu, Hanfei; Wang, Lan et al. (2018) Comparison of novel and existing methods for detecting differentially methylated regions. BMC Genet 19:84
Peralta, Juan M; Blackburn, Nicholas B; Porto, Arthur et al. (2018) Genome-wide linkage scan for loci influencing plasma triglyceride levels. BMC Proc 12:52
Gao, Tony Huayang; Zhang, Jianjun; Miguelangel, Diaz Medina et al. (2018) Methods to evaluate rare variants gene-age interaction for triglycerides. BMC Proc 12:49
Wang, Biqi; DeStefano, Anita L; Lin, Honghuang (2018) Integrative methylation score to identify epigenetic modifications associated with lipid changes resulting from fenofibrate treatment in families. BMC Proc 12:28
Fuady, Angga M; Lent, Samantha; Sarnowski, Chloé et al. (2018) Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20. BMC Genet 19:72
Blackburn, Nicholas B; Porto, Arthur; Peralta, Juan M et al. (2018) Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships. BMC Proc 12:34

Showing the most recent 10 out of 631 publications