Title: Exact Hardy-Weinburg testing for Genome Wide Association Studies
Author: Ian Painter, GENEVA project, University of Washington
Description: This package contains a function to do exact
Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
as typically obtained in a Genome Wide Association Study

Author: Ian Painter, University of Washington

Title: Identifying Significant SNPs in Genome Wide Association Studies
Author: Yan Xu, Li Xing, Jessica Su, Xuekui Zhang<UBC.X.Zhang@gmail.com>, Weiliang Qiu <Weiliang.Qiu@gmail.com>
Imports: stats, snpStats, methods, rootSolve, limma
Description: Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.
Packaged: 2019-10-10 01:10:31 UTC; weiliangqiu

Title: Nonlocal Prior Based SNP Selection Tool for Genome-Wide
Authors@R: c(person("Nilotpal", "Sanyal", role = c("aut", "cre"), email = "nisanyal@ucsd.edu"))
Description: Performs variable selection and prediction with data from Genome-wide association studies (GWAS) using non-local prior based Bayesian general linear model as described in Sanyal et al. (2017) [submitted to Bioinformatics].
Packaged: 2017-11-09 19:02:05 UTC; nilotpalsanyal

Title: Nonlocal Prior Based Iterative SNP Selection Tool for
Description: Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018) [submitted].
Packaged: 2018-02-02 03:24:26 UTC; nilotpalsanyal

Authors@R: c(person("Nilotpal", "Sanyal", role = c("aut", "cre"), email = "nilotpal.sanyal@gmail.com"))
Description: Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018).
Packaged: 2018-07-19 18:12:51 UTC; nilotpalsanyal