2026-09-19T12:56:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/584532022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
David K. Gifford.
Williams, Amy Lynne, Ph.D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-09-03T18:54:56Z
2010-09-03T18:54:56Z
2010
2010
http://hdl.handle.net/1721.1/58453
635472064
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 81-83).
Hapi is a novel dynamic programming algorithm for haplotyping nuclear families that outperforms contemporary family-based haplotyping algorithms. Haplotypes are useful for mapping and identifying genes which cause and contribute to the etiology of human disease, and for analyzing the products of meiosis to locate recombinations, enabling the identification of recombination hotspots and gene conversions. They can also be used to study population history, including expansion, contraction, and migration patterns in humans and other species. Hapi's efficiency is a result of eliminating or ignoring states and state transitions that are unnecessary for computing haplotypes. When applied to a dataset containing 103 families, Hapi performs over 3.8-320 times faster than state-of-the-art algorithms. These efficiency gains are practically important as they enable Hapi to haplotype family datasets which current algorithms are either unable to handle or are impractical for because of time constraints. Hapi infers both minimum-recombinant and maximum likelihood haplotypes, and because it applies to related individuals, the haplotypes it infers are highly accurate over large genomic distances.
by Amy Lynne Williams.
Ph.D.
83 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Efficient haplotyping for families
Thesis
application/pdf
Publication
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unknown
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<Abstract>Hapi is a novel dynamic programming algorithm for haplotyping nuclear families that outperforms contemporary family-based haplotyping algorithms. Haplotypes are useful for mapping and identifying genes which cause and contribute to the etiology of human disease, and for analyzing the products of meiosis to locate recombinations, enabling the identification of recombination hotspots and gene conversions. They can also be used to study population history, including expansion, contraction, and migration patterns in humans and other species. Hapi's efficiency is a result of eliminating or ignoring states and state transitions that are unnecessary for computing haplotypes. When applied to a dataset containing 103 families, Hapi performs over 3.8-320 times faster than state-of-the-art algorithms. These efficiency gains are practically important as they enable Hapi to haplotype family datasets which current algorithms are either unable to handle or are impractical for because of time constraints. Hapi infers both minimum-recombinant and maximum likelihood haplotypes, and because it applies to related individuals, the haplotypes it infers are highly accurate over large genomic distances.</Abstract>
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