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Bayesian statistical inference of non-allelic homologous recombination in the human genome using high-throughput sequencing data

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Overview

Year:
2014
Contributor:
Parks, Matthew (creator)
Lawrence, Charles (Director)
Thompson, William (Reader)
Raphael, Benjamin (Reader)
Brown University. Applied Mathematics (sponsor)
Genre:
theses
Subject:
bayesian
bayesian statistics
sequencing
NAHR
genome rearrangement
structural variation
Bayesian statistical decision theory
Statistics
Language:
English
Extent:
11, 139 p.
DOI
https://doi.org/10.7301/Z04J0CGN

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Description

Abstract:
Non-allelic homologous recombination (NAHR) plays a major role in genome rearrangement and is implicated in numerous genetic disorders. But detection of NAHR poses a serious technical challenge because its breakpoints occur in nearly identical regions of highly homologous repeats. While a few structural variation algorithms identify rearrangements in repeat regions, reliable detection of NAHR remains out of reach. We present a probabilistic model of NAHR and demonstrate its ability to find previously-undetected NAHR rearrangements from low coverage sequencing data. We identify a reliable subset of calls and discuss their significance: segregation of NAHR in different populations, effects on highly studied genes such as GBA and CYP2E1, and associated features of NAHR.
Notes:
Thesis (Ph.D. -- Brown University (2014)

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Citation

Parks, Matthew, "Bayesian statistical inference of non-allelic homologous recombination in the human genome using high-throughput sequencing data" (2014). Applied Mathematics Theses and Dissertations. Brown Digital Repository. Brown University Library. https://doi.org/10.7301/Z04J0CGN

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