GMMAT-package: Generalized Linear Mixed Model Association Tests
In GMMAT: Generalized Linear Mixed Model Association Tests
GMMAT-package R Documentation
Generalized Linear Mixed Model Association Tests
Description
An R package for performing association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT), including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Details
Package: GMMAT
Type: Package
Version: 1.4.2
Date: 2023-11-17
License: GPL (>= 3)
Author(s)
Han Chen, Matthew P. Conomos, Duy T. Pham
Maintainer: Han Chen <Han.Chen.2@uth.tmc.edu>
References
Brent, R.P. (1973) "Chapter 4: An Algorithm with Guaranteed Convergence for Finding a Zero of a Function", Algorithms for Minimization without Derivatives, Englewood Cliffs, NJ: Prentice-Hall, ISBN 0-13-022335-2.
Breslow, N.E. and Clayton, D.G. (1993) Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88, 9-25.
Chen, H., Huffman, J.E., Brody, J.A., Wang, C., Lee, S., Li, Z., Gogarten, S.M., Sofer, T., Bielak, L.F., Bis, J.C., et al. (2019) Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. The American Journal of Human Genetics 104, 260-274.
Chen, H., Wang, C., Conomos, M.P., Stilp, A.M., Li, Z., Sofer, T., Szpiro, A.A., Chen, W., Brehm, J.M., Celedón, J.C., Redline, S., Papanicolaou, G.J., Thornton, T.A., Laurie, C.C., Rice, K. and Lin, X. (2016) Control forpopulation structure and relatedness for binary traits in genetic association studies via logistic mixed models. The American Journal of Human Genetics 98, 653-666.
Gilmour, A.R., Thompson, R. and Cullis, B.R. (1995) Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics 51, 1440-1450.
Lee, S., Teslovich, T., Boehnke, M., Lin, X. (2013) General framework for meta-analysis of rare variants in sequencing association studies. The American Journal of Human Genetics 93, 42-53.
Lee, S., Wu, M.C., Lin, X. (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762-775.
Nelder, J.A. and Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal 7, 308-313.
Sun, J., Zheng, Y., Hsu, L. (2013) A unified mixed-effects model for rare-variant association in sequencing studies. Genetic Epidemiology 37, 334-344.
Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. The American Journal of Human Genetics 89, 82-93.
Yang, J., Lee, S.H., Goddard, M.E. and Visscher, P.M. (2011) GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics 88, 76-82.
Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nature Genetics 44, 821-824.
GMMAT documentation built on Nov. 17, 2023, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| GMMAT-package | R Documentation |
Generalized Linear Mixed Model Association Tests
Description
An R package for performing association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT), including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Details
| Package: | GMMAT |
| Type: | Package |
| Version: | 1.4.2 |
| Date: | 2023-11-17 |
| License: | GPL (>= 3) |
Author(s)
Han Chen, Matthew P. Conomos, Duy T. Pham
Maintainer: Han Chen <Han.Chen.2@uth.tmc.edu>
References
Brent, R.P. (1973) "Chapter 4: An Algorithm with Guaranteed Convergence for Finding a Zero of a Function", Algorithms for Minimization without Derivatives, Englewood Cliffs, NJ: Prentice-Hall, ISBN 0-13-022335-2.
Breslow, N.E. and Clayton, D.G. (1993) Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88, 9-25.
Chen, H., Huffman, J.E., Brody, J.A., Wang, C., Lee, S., Li, Z., Gogarten, S.M., Sofer, T., Bielak, L.F., Bis, J.C., et al. (2019) Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. The American Journal of Human Genetics 104, 260-274.
Chen, H., Wang, C., Conomos, M.P., Stilp, A.M., Li, Z., Sofer, T., Szpiro, A.A., Chen, W., Brehm, J.M., Celedón, J.C., Redline, S., Papanicolaou, G.J., Thornton, T.A., Laurie, C.C., Rice, K. and Lin, X. (2016) Control forpopulation structure and relatedness for binary traits in genetic association studies via logistic mixed models. The American Journal of Human Genetics 98, 653-666.
Gilmour, A.R., Thompson, R. and Cullis, B.R. (1995) Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models. Biometrics 51, 1440-1450.
Lee, S., Teslovich, T., Boehnke, M., Lin, X. (2013) General framework for meta-analysis of rare variants in sequencing association studies. The American Journal of Human Genetics 93, 42-53.
Lee, S., Wu, M.C., Lin, X. (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762-775.
Nelder, J.A. and Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal 7, 308-313.
Sun, J., Zheng, Y., Hsu, L. (2013) A unified mixed-effects model for rare-variant association in sequencing studies. Genetic Epidemiology 37, 334-344.
Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. The American Journal of Human Genetics 89, 82-93.
Yang, J., Lee, S.H., Goddard, M.E. and Visscher, P.M. (2011) GCTA: A Tool for Genome-wide Complex Trait Analysis. The American Journal of Human Genetics 88, 76-82.
Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nature Genetics 44, 821-824.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.