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dc.contributor.authorBabu, Ramya S
dc.date.accessioned2018-10-23T13:22:40Z
dc.date.available2018-10-23T13:22:40Z
dc.date.issued2018-05-30
dc.identifier.otherBabu_cornell_0058O_10313
dc.identifier.otherhttp://dissertations.umi.com/cornell:10313
dc.identifier.otherbibid: 10489498
dc.identifier.urihttps://hdl.handle.net/1813/59413
dc.description.abstractCorrectly inferring relatedness among samples is essential for genetic analysis. It can be helpful for preventing false signals in genetic association studies and finding relatives in forensic genetics. However, relatedness among samples is not always obtained when collecting sample data; in most cases, the relatedness is unknown and needs to be determined. Here, we develop an algorithm to infer relatedness among samples that we aim to be more efficient than current related approaches such as PLINK. Our approach is based on finding stretches of shared alleles across windows of moderate length (3-5 centiMorgans) in the genome. With this information, the algorithm infers the degree of relatedness up to the third degree from the number of windows that are similar between a pair of individuals.
dc.language.isoen_US
dc.subjectGenetics
dc.subjectEfficient
dc.subjectRelatedness Inference
dc.subjectComputer science
dc.titleAn Efficient Algorithm for Relationship Inference
dc.typedissertation or thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Computer Science
dc.contributor.chairWilliams, Amy L
dc.contributor.committeeMemberClark, Andrew
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4RN363M


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