This is a light version of the original package,
menbayes, that only contains
the likelihood ratio approaches. This is for easier maintenance and
install. See the menbayes
package for the Bayesian tests.
Provides a suite of tests for segregation distortion in F1 polyploid populations (for now, just tetraploids). This is under different assumptions of meiosis. The main functions are:
multi_lrt(): Run any of the likelihood ratio tests for segregation distortion in parallel across many SNPs.multidog_to_g: Format the genotyping output fromupdog::multidog()to be compatible withe input ofmulti_lrt().lrt_men_g4(): Likelihood ratio test for segregation distortion using known genotypes.lrt_men_gl4(): Likelihood ratio test for segregation distortion using genotype likelihoods.offspring_gf_2(): Offspring genotype frequencies under the two parameter model of meiosis.offspring_gf_3(): Offspring genotype frequencies under the three parameter model of meiosis.simf1g(): Simulate genotypes from an F1 population of tetraploids.simf1gl(): Simulate genotype likelihoods from an F1 population of tetraploids.
We also provide some functions from competing methods, which we do not recommend using:
polymapr_test(): Test frompolymapR.chisq_g4(): Chi-squared test (not accounting for double reduction and preferential pairing) when genotypes are known.chisq_gl4(): Chi-squared test (not accounting for double reduction and preferential pairing) using genotype likelihoods.
Details of these methods may be found in Gerard et al. (2025).
You can install the stable version of segtest from CRAN with:
install.packages("segtest")You can install the development version of segtest from GitHub with:
# install.packages("devtools")
devtools::install_github("dcgerard/segtest")Please note that the segtest project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Gerard D, Thakkar M, & Ferrão LFV (2025). “Tests for segregation distortion in tetraploid F1 populations.” Theoretical and Applied Genetics, 138(30), p. 1–13. doi:10.1007/s00122-025-04816-z.
This material is based upon work supported by the National Science Foundation under Grant No. 2132247.