Degree Type

Dissertation

Date of Award

2010

Degree Name

Doctor of Philosophy

Department

Genetics, Development and Cell Biology

First Advisor

Patrick S. Schnable

Abstract

In this dissertation, a novel adaptor-mediated PCR-based method, Digestion-ligation-amplification (DLA), was developed to overcome difficulties of amplifying unknown sequences flanking known DNA sequences in large genomes. Two DLA-based strategies were developed to isolate Mu-tagged alleles. The utility of each approach was validated by independently cloning the gl4 (glossy4) gene. Mutants of gl4 lack the normal accumulation of epicuticular waxes. The gl4 gene is a homolog of the Arabidopsis CUT1 gene, which encodes a condensing enzyme involved in the synthesis of very-long-chain fatty acids, which are precursors of epicuticular waxes. Using this novel genome walking strategy, >40,000 non-redundant Mu insertion sites were amplified from Mu stocks and sequenced via 454 technology. The chromosomal and genic patterns of Mu insertions were revealed by analyzing the distributions of these Mu insertions. Mu insertions exhibit similar chromosomal and genic patterns as recombination events, indicating the common component(s) may be involved in the both events. The finding that Mu insertions and meiotic recombination sites both concentrate in genomic regions marked with epigenetic marks of open chromatin provides support for the hypothesis that open chromatin enhances rates of both Mu insertion and meiotic recombination. At the last part of the dissertation, a rapid gene mapping approach based on Sequenom-based SNP-typing was developed. The quantitative nature of Sequenom-based SNP assays led to the development of a time- and cost-efficient strategy to genetically map mutants via quantitative Bulked Segregant Analysis (BSA). This strategy was used to rapidly map the loci associated with several dozen recessive mutants.

DOI

https://doi.org/10.31274/etd-180810-1919

Copyright Owner

Sanzhen Liu

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

134 pages

Share

COinS