Literature DB >> 28065901

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination.

Sajad Mirzaei, Yufeng Wu.   

Abstract

Motivation: : Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination.
Results: : In this paper, we present a new approach called RENT+  for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+  builds on a previous genealogy inference approach called RENT , which infers a set of related genealogical trees at different genomic positions. RENT+  represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT . The key components of RENT+  are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+  is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+  in the inference of population demographic history from haplotypes, which outperforms several existing methods. Availability and Implementation: : RENT+  is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus . Contacts: : sajad@engr.uconn.edu or ywu@engr.uconn.edu. Supplementary information: : Supplementary data are available at Bioinformatics online.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Mesh:

Year:  2017        PMID: 28065901      PMCID: PMC5860023          DOI: 10.1093/bioinformatics/btw735

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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