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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM031575-30
Application #
8706883
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
Project Start
1983-04-01
Project End
2017-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
30
Fiscal Year
2014
Total Cost
$627,287
Indirect Cost
$284,507
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
007936834
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Wu, Zheyang; Hu, Yijuan; Melton, Phillip E (2014) Longitudinal data analysis for genetic studies in the whole-genome sequencing era. Genet Epidemiol 38 Suppl 1:S74-80
Sha, Qiuying; Zhang, Shuanglin (2014) A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children. Genet Epidemiol 38:135-43
Chen, Han; Malzahn, Dörthe; Balliu, Brunilda et al. (2014) Testing genetic association with rare and common variants in family data. Genet Epidemiol 38 Suppl 1:S37-43
Malzahn, Dörthe; Müller-Nurasyid, Martina; Heid, Iris M et al. (2014) Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R. Eur J Hum Genet 22:1217-24
Blue, Elizabeth M; Sun, Lei; Tintle, Nathan L et al. (2014) Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond. Genet Epidemiol 38 Suppl 1:S21-8
Satten, Glen A; Biswas, Swati; Papachristou, Charalampos et al. (2014) Population-based association and gene by environment interactions in Genetic Analysis Workshop 18. Genet Epidemiol 38 Suppl 1:S49-56
Cordell, Heather J (2014) Summary of results and discussions from the gene-based tests group at Genetic Analysis Workshop 18. Genet Epidemiol 38 Suppl 1:S44-8
Li, Dalin; Zhou, Jin; Thomas, Duncan C et al. (2014) Complex pedigrees in the sequencing era: to track transmissions or decorrelate? Genet Epidemiol 38 Suppl 1:S29-36
Paterson, Andrew D (2014) Drinking from the Holy Grail: analysis of whole-genome sequencing from the Genetic Analysis Workshop 18. Genet Epidemiol 38 Suppl 1:S1-4
Thornton, Timothy A; Bermejo, Justo Lorenzo (2014) Local and global ancestry inference and applications to genetic association analysis for admixed populations. Genet Epidemiol 38 Suppl 1:S5-S12

Showing the most recent 10 out of 343 publications