Tests for Segregation Distortion in Polyploids
Chi Square test when genotypes are known
Chi-Sq for GL
EM algorithm from Li (2011)
Converts genotype counts to genotype vectors.
Inverse function of gcount_to_gvec().
Tests if the two parameter model is valid
Iterator over array
Likelihood under three parameter model when genotypes are known
Likelihood under three parameter model when genotypes are known
Likelihood under three parameter model when using offspring genotypes ...
Likelihood under three parameter model when using offspring genotypes ...
Objective function for em_li()
Likelihood ratio test for segregation distortion with known genotypes
Likelihood ratio test using genotype likelihoods.
Parallelized likelihood ratio test for segregation distortion.
Converts multidog output to a format usable for multi_lrt()
Next element in an array
Simulates genotypes given genotype frequencies.
Calculates offspring genotype frequencies under the two-parameter mode...
Calculates offspring genotype frequencies under the three-parameter mo...
Jointly tests for segregation distortion and number of incompatible ge...
Generate genotype likelihoods from offspring genotypes.
Run segregation distortion tests as implemented in the polymapR packag...
Tetraploid gamete frequencies of gametes when one parent's genotype is...
Tetraploid gamete frequencies of gametes when one parent's genotype is...
segtest: Tests for Segregation Distortion in Polyploids
Simulate genotype counts from F1 individuals
Simulate genotype likelihoods of F1 individuals.
Simulate genotype likelihoods from genotype counts
Convert from three parameters to two parameters
Provides a suite of tests for segregation distortion in F1 polyploid populations (for now, just tetraploids). This is under different assumptions of meiosis. Details of these methods are described in Gerard et al. (2024) <doi:10.1101/2024.02.07.579361>. This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation.