Package: milorGWAS 0.7
Hervé Perdry
milorGWAS: Mixed Logistic Regression for Genome-Wide Analysis Studies (GWAS)
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices in plink format, or dosage (VCF files). The methods are described in details in Milet et al (2020) <doi:10.1101/2020.01.17.910109>.
Authors:
milorGWAS_0.7.tar.gz
milorGWAS_0.7.tar.gz(r-4.5-noble)milorGWAS_0.7.tar.gz(r-4.4-noble)
milorGWAS_0.7.tgz(r-4.4-emscripten)milorGWAS_0.7.tgz(r-4.3-emscripten)
milorGWAS.pdf |milorGWAS.html✨
milorGWAS/json (API)
NEWS
| # Install 'milorGWAS' in R: |
| install.packages('milorGWAS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 months agofrom:4e24b9342b. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 21 2024 |
| R-4.5-linux-x86_64 | OK | Dec 21 2024 |
Exports:association.test.logisticassociation.test.logistic.dosageqqplot.pvaluesSNP.category
Dependencies:gastonRcppRcppEigenRcppParallel
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Mixed logistic regression for GWAS | association.test.logistic |
| Mixed logistic regression for GWAS, using dosages | association.test.logistic.dosage |
| Stratified QQ-plot of p-values | qqplot.pvalues |
| SNP.category | SNP.category |