Spontaneous Mutation Discovery via High-Throughput Sequencing of Pedigrees

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Title: Spontaneous Mutation Discovery via High-Throughput Sequencing of Pedigrees
Author: Keebler, Jonathan Edward Myers
Advisors: Alison Motsinger, Committee Member
Jeffrey Thorne, Committee Member
Ignzaio Carbone, Committee Member
Eric Stone, Committee Chair
Philip Awadalla, Committee Member
Abstract: Recent technological advances have made high-throughput DNA sequencing a routine laboratory experiment. This progression in technology has been made possible by the parallel production of millions of short fragments of sequence. The responsibility of garnering biological information from these DNA fragments has shifted from the wet-lab to the bioinformatician. As sequencing technology is applied to a growing number of individual human genomes, entire families are now being sequenced. Information contained within the pedigree of a sequenced family can be leveraged when inferring the donors’ genotypes, a task that is not necessarily trivial using high-throughput sequencing reads. A violation of Mendelian inheritance laws observed amid the resequenced genomes of family members can indicate the presence of a de novo mutation. A method for locating de novo mutations by probabilistically inferring genotypes across a pedigree using high-throughput sequencing is presented and applied to two resequenced nuclear families: one as a collaborative effort within The 1,000 Genomes Project, and the second in an attempt to discover candidate driver and passenger mutations within the genome of an Acute Lymphoblastic Leukemia. The mutation findings within these projects are presented, and the approach is examined in detail, highlighting areas where method improvements may be made. Considering the challenges experienced in these studies within the larger context of the nascent field of Personal Genomics, an honest assessment is presented of developments that must be made before the application of whole-genome sequencing on the scale of an individual human can unequivocally be used to predict, diagnose, or treat human disease.
Date: 2010-04-20
Degree: PhD
Discipline: Bioinformatics
URI: http://www.lib.ncsu.edu/resolver/1840.16/6168


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