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Accounting for sampling bias in ancestral state reconstruction

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2022-08-16
Authors/Contributors
Author: Song, Yexuan
Abstract
Viral transmission plays an essential role in our understanding of and response to infectious diseases, for example, by informing policy decisions about transportation and borders. Phylogenetic methods take advantage of the evolutionary relationships between the genome sequences of viruses to infer geographical locations of unobserved ancestors from sampled data. Here I introduce a new approach to examine the inference of ancestral locations and predict the geographic movement of viral lineages from the known locations of samples. In contrast to existing methods, my method accounts for differences in sampling policies among areas, to avoid biased inference of the ancestral locations. I begin by summarizing existing methods for ancestral state reconstruction. I then introduce an ancestral state reconstruction method that accounts for variation in sampling rate among locations and compare it to the classic Maximum Likelihood method. I show that my method infers ancestral states for small trees more accurately than this classic approach.
Document
Extent
57 pages.
Identifier
etd22102
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Colijn, Caroline
Thesis advisor: MacPherson, Ailene
Language
English
Member of collection
Download file Size
etd22102.pdf 3.03 MB

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