Haplotype inference in complex pedigrees

Bonnie Kirkpatrick*, Javier Rosa, Eran Halperin, Richard M. Karp

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite the desirable information contained in complex pedigree datasets, analysis methods struggle to efficiently process these datasets. The attractiveness of pedigree data sets is their power for detecting rare variants, particularly in comparison with studies of unrelated individuals. In addition, rather than assuming individuals in a study are unrelated, knowledge of their relationships can avoid spurious results due to confounding population structure effects. However, a major challenge for the applicability of pedigree methods is the ability handle complex pedigrees, having multiple founding lineages, inbreeding, and half-sibling relationships. A key ingredient in association studies is imputation and inference of haplotypes from genotype data. Existing haplotype inference methods either do not efficiently scales to complex pedigrees or their accuracy is limited. In this paper, we present algorithms for efficient haplotype inference and imputation in complex pedigrees. Our method, PhyloPed, leverages the perfect phylogeny model, resulting in an efficient method with high accuracy. In addition, PhyloPed effectively combines the founder haplotype information from different lineages and is immune to inaccuracies in prior information about the founders.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 13th Annual International Conference, RECOMB 2009, Proceedings
PublisherSpringer Verlag
Pages108-120
Number of pages13
ISBN (Print)9783642020070
DOIs
StatePublished - 2009
Event13th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2009 - Tucson, AZ, United States
Duration: 18 May 200921 May 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5541 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2009
Country/TerritoryUnited States
CityTucson, AZ
Period18/05/0921/05/09

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