The goal of this project is to facilitate the development and evaluation of statistical methods for identifying and characterizing the genetic contribution to complex diseases and their precursors and risk factors. We will pursue this goal by continuing the organization of the Genetic Analysis Workshops (GAWs), which began in 1982. The GAWs 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 real and computer-simulated data are distributed to investigators worldwide. Participants submit the results of their analyses, which are discussed and compared at a 2.5-day meeting. Before the Workshops, participants communicate with others who have done similar types of analyses and plan integrated presentations. The GAW submissions invariably contain new ideas for methods to handle complex phenotypes. Recent GAWs have included genome scan data and simulated sequence data, giving participants an opportunity to try out new methods for localizing disease-causing genes. GAW13 (to be held in 2002) will feature genetic analysis of longitudinal data from families in the Framingham Heart Study, together with multiple replicates of a simulated data set modeled after the Framingham data. GAW14 will be held in 2004 and GAW15 in 2006. All planning and data distribution for GAW16 will be essentially complete by the end of the requested period of support in 2008. We will continue to distribute real and simulated data from past GAWs and programs for genetic analysis. Long after each GAW is over, investigators continue to use real and simulated GAW data sets to evaluate new analytical methods and software, to estimate power and false positive rates, and to demonstrate the feasibility of statistical techniques for finding disease genes. GAW data also are extensively used in teaching and dissertation research.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM031575-23
Application #
6931943
Study Section
Epidemiology and Disease Control Subcommittee 2 (EDC)
Program Officer
Anderson, Richard A
Project Start
1983-04-01
Project End
2007-08-31
Budget Start
2005-09-01
Budget End
2006-08-31
Support Year
23
Fiscal Year
2005
Total Cost
$453,680
Indirect Cost
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
007936834
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Das, Sarmistha; Mondal, Pronoy Kanti; Ghosh, Saurabh et al. (2018) Family-based genome-wide association of inflammation biomarkers and fenofibrate treatment response in the GOLDN study. BMC Proc 12:41
Chen, Yuning; Peloso, Gina M; Dupuis, Josée (2018) Evaluation of a phenotype imputation approach using GAW20 simulated data. BMC Proc 12:56
Canty, Angelo J; Paterson, Andrew D (2018) Evidence of batch effects masking treatment effect in GAW20 methylation data. BMC Proc 12:32
Almeida, Marcio; Peralta, Juan; Garcia, Jose et al. (2018) Modeling methylation data as an additional genetic variance component. BMC Proc 12:29
Lim, Elise; Xu, Hanfei; Wu, Peitao et al. (2018) Network analysis of drug effect on triglyceride-associated DNA methylation. BMC Proc 12:27
Li, Liming; Wang, Chan; Lu, Tianyuan et al. (2018) Indirect effect inference and application to GAW20 data. BMC Genet 19:67
Cherlin, Svetlana; Howey, Richard A J; Cordell, Heather J (2018) Using penalized regression to predict phenotype from SNP data. BMC Proc 12:38
de Andrade, Mariza; Warwick Daw, E; Kraja, Aldi T et al. (2018) The challenge of detecting genotype-by-methylation interaction: GAW20. BMC Genet 19:81
Kraja, Aldi T; An, Ping; Lenzini, Petra et al. (2018) Simulation of a medication and methylation effects on triglycerides in the Genetic Analysis Workshop 20. BMC Proc 12:25
Kulkarni, Hemant; Mukhopadhyay, Indranil; Ghosh, Saurabh (2018) Transmission-based association mapping of triglyceride levels in a longitudinal framework using quasi-likelihood. BMC Proc 12:39

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