The 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. The investigators will pursue this goal by 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 world-wide. Participants submit the results of their analyses, which are discussed and compared at a 2.5 day meeting. The GAW submissions invariably contain new ideas for methods to handle complex phenotypes. GAW11, held in September 1998, was devoted to problems in genetic analysis of data from the Collaborative Study on the Genetics of Alcoholism and analysis of multiple replicates of simulated data for a multilocus disorder with varying severity and with heterogeneity between populations. GAW11 had 210 participants from the United States, Canada, France, Germany, England, Switzerland, Australia, and Argentina. GAW12 will be held in 2000, and GAW13 in 2002. All planning and data distribution for GAW14 will be essentially complete by the end of the requested period of support in 2004.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM031575-17
Application #
2902586
Study Section
Epidemiology and Disease Control Subcommittee 2 (EDC)
Project Start
1983-04-01
Project End
2003-08-31
Budget Start
1999-09-01
Budget End
2000-08-31
Support Year
17
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Southwest Foundation for Biomedical Research
Department
Type
DUNS #
City
San Antonio
State
TX
Country
United States
Zip Code
78245
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
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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
Yasmeen, Summaira; Burger, Patricia; Friedrichs, Stefanie et al. (2018) Relating drug response to epigenetic and genetic markers using a region-based kernel score test. BMC Proc 12:47
Xu, Zheng; Duan, Qing; Cui, Juan et al. (2018) Analysis of genetic and nongenetic factors influencing triglycerides-lowering drug effects based on paired observations. BMC Proc 12:46
Porto, Arthur; Peralta, Juan M; Blackburn, Nicholas B et al. (2018) Reliability of genomic predictions of complex human phenotypes. BMC Proc 12:51
Nustad, Haakon E; Page, Christian M; Reiner, Andrew H et al. (2018) A Bayesian mixed modeling approach for estimating heritability. BMC Proc 12:31

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