Haplotype Inference in Complex Pedigrees

Authors:
Bonnie Kirkpatrick;Javier Rosa;Eran Halperin;Richard M. Karp
Affiliations:
Computer Science Dept, University of California, Berkeley,;Computer Science Dept, Rutgers, The State University of New Jersey, New Brunswick,;School of Computer Science and the Dept. of Biotechnology, Tel-Aviv University, and the International Computer Science Institute, Berkeley,;Computer Science Dept, University of California, Berkeley and the International Computer Science Institute,
Venue:
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Year:
2009

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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.