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osu1039121487.pdf (524.17 KB)
ETD Abstract Container
Abstract Header
Sequential Imputation and Linkage Analysis
Author Info
Skrivanek, Zachary
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487
Abstract Details
Year and Degree
2002, Doctor of Philosophy, Ohio State University, Statistics.
Abstract
Multilocus calculations using all available information on all pedigree members are important for linkage analysis. Exact calculation methods in linkage analysis are limited in either the number of loci or the number of pedigree members they can handle. In this thesis, we propose a Monte Carlo method for linkage analysis based on sequential imputation. Unlike exact methods, sequential imputation can handle both a moderate number of loci and a large number of pedigree members. Sequential imputation does not have the problem of slow mixing encountered by Markov chain Monte Carlo methods because of high correlation between samples from pedigree data. This Monte Carlo method is an application of importance sampling in which we sequentially impute ordered genotypes locus by locus and then impute inheritance vectors conditioned on these genotypes. The resulting inheritance vectors together with the importance sampling weights are used to derive a consistent estimator of any linkage statistic of interest. The linkage statistic can be parametric or nonparametric; we focus on nonparametric linkage statistics. We showed that sequential imputation can produce accurate estimates within reasonable computing time. Then we performed a simulation study to illustrate the potential gain in power using our method for multilocus linkage analysis with large pedigrees. We also showed how sequential imputation can be used in haplotype reconstruction, an important step in genetic mapping. In all of the applications of sequential imputation we can incorporate interference, which often is ignored in linkage analysis due to computational problems. We demonstrated the effect of interference on haplotyping and linkage analysis. We have implemented sequential imputation for multilocus linkage analysis in a user-friendly software package called SIMPLE (Sequential Imputation for Multi-Point Linkage Estimation). SIMPLE currently can estimate LOD scores, IBD sharing statistics and haplotype configuration probabilities for both simple and complex pedigrees with or without interference.
Committee
Shili Lin (Advisor)
Subject Headings
Statistics
Keywords
linkage analysis
;
gene mapping
;
Monte Carlo
;
sequential imputation
;
importance sampling
;
nonparametric
;
IBD
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Citations
Skrivanek, Z. (2002).
Sequential Imputation and Linkage Analysis
[Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487
APA Style (7th edition)
Skrivanek, Zachary.
Sequential Imputation and Linkage Analysis.
2002. Ohio State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487.
MLA Style (8th edition)
Skrivanek, Zachary. "Sequential Imputation and Linkage Analysis." Doctoral dissertation, Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039121487
Chicago Manual of Style (17th edition)
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Document number:
osu1039121487
Download Count:
907
Copyright Info
© 2002, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.