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 2017). 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.
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.
|Cao, Hongyan; Li, Zhi; Yang, Haitao et al. (2017) Longitudinal data analysis for rare variants detection with penalized quadratic inference function. Sci Rep 7:650|
|Li, Jian-Long; Wang, Peng; Fung, Wing Kam et al. (2017) Generalized disequilibrium test for association in qualitative traits incorporating imprinting effects based on extended pedigrees. BMC Genet 18:90|
|Yang, Xinlan; Wang, Shuaichen; Zhang, Shuanglin et al. (2017) Detecting association of rare and common variants based on cross-validation prediction error. Genet Epidemiol 41:233-243|
|Chen, Carla Chia-Ming; Keith, Jonathan Macgregor; Mengersen, Kerrie Lee (2017) Accurate phenotyping: Reconciling approaches through Bayesian model averaging. PLoS One 12:e0176136|
|Konigorski, Stefan; Yilmaz, Yildiz E; Pischon, Tobias (2017) Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits. PLoS One 12:e0178504|
|Lin, Wan-Yu; Chen, Wei J; Liu, Chih-Min et al. (2017) Adaptive combination of Bayes factors as a powerful method for the joint analysis of rare and common variants. Sci Rep 7:13858|
|Chiu, Yen-Feng; Lee, Chun-Yi; Hsu, Fang-Chi (2016) Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes. BMC Proc 10:315-320|
|Wang, Zhenchuan; Wang, Xuexia; Sha, Qiuying et al. (2016) Joint Analysis of Multiple Traits in Rare Variant Association Studies. Ann Hum Genet 80:162-71|
|Wang, Chi; Liu, Jinpeng; Fardo, David W (2016) Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty. BMC Proc 10:411-415|
|Valcarcel, Alessandra; Grinde, Kelsey; Cook, Kaitlyn et al. (2016) A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests. BMC Proc 10:349-355|
Showing the most recent 10 out of 563 publications