title: A new method for modeling coalescent processes with recombination.
abstract: BACKGROUND
  Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein's, SMC, SMC', and MaCS.
  
  
  RESULTS
  In this study, a new method of modeling coalescence with recombination, Spatial Coalescent simulator (SC), was developed, which considerably improved the algorithm described by Wiuf and Hein. The present algorithm constructs ARG spatially along the sequence, but it does not produce any redundant branches which are inevitable in Wiuf and Hein's algorithm. Interestingly, the distribution of ARG generated by the present new algorithm is identical to that generated by a typical back-in-time model adopted by ms, an algorithm commonly used to model coalescence. It is here demonstrated that the existing approximate methods such as the sequentially Markov coalescent (SMC), a related method called SMC', and Markovian coalescent simulator (MaCS) can be viewed as special cases of the present method. Using simulation analysis, the time to the most common ancestor (TMRCA) in the local trees of ARGs generated by the present algorithm was found to be closer to that produced by ms than time produced by MaCS. Sample-consistent ARGs can be generated using the present method. This may significantly reduce the computational burden.
  
  
  CONCLUSIONS
  In summary, the present method and algorithm may facilitate the estimation and description of recombination in population genomics and evolutionary biology.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhou
authors.forename: Linfeng
authors.initials: L
authors.lastname: Li
authors.forename: Xian
authors.initials: X
authors.lastname: Chen
authors.forename: Yuting
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. mazm@amt.ac.cn.
authors.forename: Zhi-Ming
authors.initials: ZM
authors.lastname: Ma
authors.forename: Shuhua
authors.initials: S
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D005828:Genetics, Population; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D011995:Recombination, Genetic
references.citation: J Comput Biol. 1996 Winter;3(4):479-502
references.pmid: 9018600
references.citation: Genetics. 2001 Nov;159(3):1299-318
references.pmid: 11729171
references.citation: Theor Popul Biol. 1999 Jun;55(3):248-59
references.pmid: 10366550
references.citation: BMC Genet. 2006 Mar 15;7:16
references.pmid: 16539698
references.citation: Theor Popul Biol. 1983 Apr;23(2):183-201
references.pmid: 6612631
references.citation: Nat Rev Genet. 2003 Dec;4(12):959-68
references.pmid: 14631356
references.citation: Genetics. 2000 Feb;154(2):931-42
references.pmid: 10655242
references.citation: Bioinformatics. 2002 Feb;18(2):337-8
references.pmid: 11847089
references.citation: Philos Trans R Soc Lond B Biol Sci. 2005 Jul 29;360(1459):1387-93
references.pmid: 16048782
references.citation: Genetics. 2000 Sep;156(1):439-47
references.pmid: 10978306
references.citation: Genome Res. 2009 Jan;19(1):136-42
references.pmid: 19029539
references.citation: Genetics. 2000 May;155(1):451-62
references.pmid: 10790416
references.citation: J Comput Biol. 2005 Mar;12(2):147-69
references.pmid: 15767774
references.citation: Curr Biol. 2003 Jan 8;13(1):1-8
references.pmid: 12526738