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 propose to pursue this goal by continuing the organization of the Genetic Analysis Workshops (GAWs), which began in 1982. The Genetic Analysis Workshops are a collaborative effort among genetic epidemiologists and statistical geneticists to evaluate and compare genetic analysis 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 3 1/2 day meeting. Participation at GAWs has increased tremendously, from fewer than 30 at GAW1 in 1982 to 272 at GAW17. In the current grant period GAW16 (2008) and GAW17 (2010) were held. The GAW17 proceedings are currently in press with publication anticipated in December 2011 and planning for GAW18 has begun. During the proposed grant period, the GAW18 Proceedings will be published and two GAWs will be held: GAW19 (in 2014) and GAW20 (in 2016). Before the Workshops, participants devote months to data analysis, 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 and a number of widely used analytical techniques had their start at a GAW. Recent GAWs have included genome-wide association data and exome sequence data, giving participants an opportunity to try out new methods for localizing and characterizing variants influencing disease risk. Topics for future GAWs will be selected from among currently challenging analytical problems. Suggestions from GAW participants include methods for analyzing whole genome sequence;genome-wide methylation profiles;comparisons of study designs for next generation sequencing projects;analyzing sequence data in admixed populations;gene-environment and gene-gene interactions;and copy number variation. We also will continue to distribute real and simulated data from past GAWs with the permission of the data providers. Long after each GAW is over, investigators continue to use 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 are extensively used in grant proposals and in teaching and dissertation research.

Public Health Relevance

The goal of this project is to facilitate the development and evaluation of statistical methods for identifying genes contributing to risk of complex diseases and related risk factors. Continuing innovation in analytical methods is needed to keep pace with new study designs and new types of data made possible by advances in laboratory technologies and computing. Development of new statistical methods has the potential to significantly advance research in biomedical genetics because such methods can be applied in studies of many complex human diseases.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
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Texas Biomedical Research Institute
San Antonio
United States
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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
Nustad, Haakon E; Page, Christian M; Reiner, Andrew H et al. (2018) A Bayesian mixed modeling approach for estimating heritability. BMC Proc 12:31
Porto, Arthur; Peralta, Juan M; Blackburn, Nicholas B et al. (2018) Reliability of genomic predictions of complex human phenotypes. BMC Proc 12:51
Hsu, Yayun; Auerbach, Jonathan; Zheng, Tian et al. (2018) Coping with family structure in genome-wide association studies: a comparative evaluation. BMC Proc 12:42
Cantor, Rita; Navarro, Linda; Pan, Calvin (2018) Identifying fenofibrate responsive CpG sites. BMC Proc 12:43
Sun, Rui; Weng, Haoyi; Men, Ruoting et al. (2018) Gene-methylation epistatic analyses via the W-test identifies enriched signals of neuronal genes in patients undergoing lipid-control treatment. BMC Proc 12:53
Zhou, Xiaofei; Wang, Meng; Zhang, Han et al. (2018) Logistic Bayesian LASSO for detecting association combining family and case-control data. BMC Proc 12:54

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