|Title||Bayesian inference of local trees along chromosomes by the sequential Markov coalescent|
|Author(s)||Zheng, Chaozhi; Kuhner, Mary K.; Thompson, Elizabeth A.|
|Source||Journal of Molecular Evolution 78 (2014)5. - ISSN 0022-2844 - p. 279 - 292.|
|Department(s)||Mathematical and Statistical Methods - Biometris|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Ancestral recombination graph - Bayesian inference - Coalescent - Markov chain Monte Carlo - Sequential Markov coalescent|
We propose a genealogy-sampling algorithm, Sequential Markov Ancestral Recombination Tree (SMARTree), that provides an approach to estimation from SNP haplotype data of the patterns of coancestry across a genome segment among a set of homologous chromosomes. To enable analysis across longer segments of genome, the sequence of coalescent trees is modeled via the modified sequential Markov coalescent (Marjoram and Wall, Genetics 7:16, 2006). To assess performance in estimating these local trees, our SMARTree implementation is tested on simulated data. Our base data set is of the SNPs in 10 DNA sequences over 50 kb. We examine the effects of longer sequences and of more sequences, and of a recombination and/or mutational hotspot. The model underlying SMARTree is an approximation to the full recombinant-coalescent distribution. However, in a small trial on simulated data, recovery of local trees was similar to that of LAMARC (Kuhner et al. Genetics 156:1393-1401, 2000a), a sampler which uses the full model.