Jenkins, Paul and Griffiths, Robert C. (2011) Inference from samples of DNA sequences using a two-locus model. Journal of Computational Biology , Vol.18 (No.1). pp. 109-127. doi:10.1089/cmb.2009.0231

Research output not available from this repository, contact author.

Request Changes to record.

Abstract

Performing inference on contemporary samples of DNA sequence data is an important and challenging task. Computationally intensive methods such as importance sampling (IS) are attractive because they make full use of the available data, but in the presence of recombination the large state space of genealogies can be prohibitive. In this article, we make progress by developing an efficient IS proposal distribution for a two-locus model of sequence data. We show that the proposal developed here leads to much greater efficiency, outperforming existing IS methods that could be adapted to this model. Among several possible applications, the algorithm can be used to find maximum likelihood estimates for mutation and crossover rates, and to perform ancestral inference. We illustrate the method on previously reported sequence data covering two loci either side of the well-studied TAP2 recombination hotspot. The two loci are themselves largely non-recombining, so we obtain a gene tree at each locus and are able to infer in detail the effect of the hotspot on their joint ancestry. We summarize this joint ancestry by introducing the gene graph, a summary of the well-known ancestral recombination graph.

Item Type:	Journal Article
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Journal of Computational Biology
Publisher:	Mary Ann Liebert, Inc. Publishers
ISSN:	1066-5277
Official Date:	2011
Dates:	Date Event 2011 Published
Volume:	Vol.18
Number:	No.1
Page Range:	pp. 109-127
DOI:	10.1089/cmb.2009.0231
Status:	Peer Reviewed
Publication Status:	Published

Request changes or add full text files to a record

Repository staff actions (login required)

View Item