Coalescence computations for large samples drawn from populations of time-varying sizes.
We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.
Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.
Andrzej
A
Polanski
Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.
Agnieszka
A
Szczesna
Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.|The Linnaeus Centre for Bioinformatics, Uppsala University, BMC, Uppsala, Sweden.
Mateusz
M
Garbulowski
Systems Engineering Group, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.|Department of Statistics, Rice University, M.S. 138, 6100 Main Street, Houston, TX 77005, United States of America.
Marek
M
Kimmel
D000465:Algorithms; D004272:DNA, Mitochondrial; D030561:Databases, Nucleic Acid; D005787:Gene Frequency; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D011336:Probability; D015203:Reproducibility of Results
D004272:DNA, Mitochondrial