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IPED: Inheritance path based pedigree reconstruction algorithm using genotype data

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dc.contributor.authorHe, Dan-
dc.contributor.authorWang, Zhanyong-
dc.contributor.authorHan, Buhm-
dc.contributor.authorParida, Laxmi-
dc.contributor.authorEskin, Eleazar-
dc.date.accessioned2023-04-26T05:11:44Z-
dc.date.available2023-04-26T05:11:44Z-
dc.date.created2023-04-21-
dc.date.created2023-04-21-
dc.date.issued2013-04-
dc.identifier.citationLecture Notes in Computer Science, Vol.7821, pp.75-87-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://hdl.handle.net/10371/191626-
dc.description.abstractThe problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time consuming and inaccurate for complicated pedigrees such as pedigrees with inbreeding. In this work, we propose an efficient algorithm which is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation backwards in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path based approach implemented using an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations and therefore it can reconstruct pedigrees which have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data. © 2013 Springer-Verlag.-
dc.language영어-
dc.publisherSpringer Verlag-
dc.titleIPED: Inheritance path based pedigree reconstruction algorithm using genotype data-
dc.typeArticle-
dc.identifier.doi10.1007/978-3-642-37195-0_7-
dc.citation.journaltitleLecture Notes in Computer Science-
dc.identifier.scopusid2-s2.0-84875483925-
dc.citation.endpage87-
dc.citation.startpage75-
dc.citation.volume7821-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorHan, Buhm-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • College of Medicine
  • Department of Medicine
Research Area Bioinformatics, Genomics, Statistical Genetics

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