Application of Graph Theory to the elaboration of personal genomic data for genealogical research

, , , ,
1 Institute of Chemistry of Organometallic Compounds, Research Area of National Research Council, Pisa, Italy
2 Department of Civilizations and Forms of Knowledge, University of Pisa, Pisa, Italy
3 Division of Biological Anthropology, University of Cambridge, Cambridge, United Kingdom
4 Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
5 Institute of Sciences and Technology of Information, National Research Council, Pisa, Italy
6 Department of Biology, University of Pisa, Pisa, Italy
DOI
10.7287/peerj.preprints.1201v1
Published
Accepted
Subject Areas
Computational Biology, Artificial Intelligence, Visual Analytics
Keywords
DNA Analysis, Personal Genomics, Genealogy, Ancestry reconstruction, Statistical methods, Graph Theory, Genetic Genealogy
Copyright
© 2015 Palleschi et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Palleschi V, Pagani L, Pagnotta S, Amato G, Tofanelli S. 2015. Application of Graph Theory to the elaboration of personal genomic data for genealogical research. PeerJ PrePrints 3:e1201v1

Abstract

In this communication a representation of the links between DNA-relatives based on Graph Theory is applied to the analysis of personal genomic data to obtain genealogical information. The method is tested on real data and discussed its applicability to the field of genealogical research. We envisage the proposed approach as a valid tool for a streamlined application to the publicly available data generated by many online personal genomic companies. By this way, anonymized matrices of pairwise genome sharing counts will enable to improve the retrieval of genetic relationship between customers who provided explicit consent to the treatment of their data.

Author Comment

This is a preprint of a manuscript currently under review at PeerJ Computer Science.

Supplemental Information

Supplementary Figures and Tables

DOI: 10.7287/peerj.preprints.1201v1/supp-1