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The Washington Research Foundation Fellowship
Evan Boyle, Microbiology & Biochemistry, 2012-13 WRFF
Long fascinated by the often subtle distinction between healthy and diseased conditions, I joined the Gelb Lab in the summer following my freshman year, where I genotyped multiple transgenic mouse lines and expressed recombinant enzymes in E. coli and Sf9 cells to study the role of secreted phospholipases A2 in asthma and other conditions. Being exposed to the immensity of what remains unknown in the field of biomedical research motivated me to explore the emergent technologies of genomic profiling, which led me to the lab of Jay Shendure in the fall of my junior year.
My time in the Shendure Lab has been unimaginably instructive thanks to the remarkable dedication and industriousness of its lab members. Recent advances in information technology and microfluidics have enabled the pursuit of new research questions that would have been deemed inconceivable only ten years prior. In the Shendure Lab, the depth and complexity of the data sets being generated on a daily basis have convinced me that solving the hitherto intractable mysteries that have plagued the field of medicine will require leveraging the increasingly sophisticated computational tools being developed at the UW and other universities worldwide -- and I am determined to do my part.
My time spent on undergraduate research has been an invaluable experience, allowing me to meet many extraordinary people here at the UW and fuelling my desire to pursue graduate school; for this reason, I deeply appreciate the support of the Washington Research Foundation.
Mentor: Jay Shendure, Genome Sciences
Project Title: Methods & Algorithms Development for Rapid, Ultra-Low-Cost, Targeted DNA Sequencing
Abstract: New theories accounting for the unexplained heritability of complex diseases posit the existence of rare alleles of large effect size that have escaped detection by genome-wide association studies. Unfortunately, whole genome sequencing remains prohibitively expensive to be used for routine rare variant detection due to the large sample sizes and volume of sequencing necessary to achieve statistical significance. Previous work has shown success overcoming this hurdle through the use of Molecular Inversion Probe technology to test candidate genes in a highly targeted manner, which allows the genotyping of hundreds of individuals for a fraction of the cost. However, the same studies demonstrate that variation in Molecular Inversion Probe capture efficiency leads to severe non-uniformity across interrogated sites, which ultimately manifests in the form of incomplete data sets. To ameliorate this non-uniformity, we seek to validate a new probe selection methodology derived from analysis of a new molecular inversion probe sequencing run targeting random, unbiased regions of the genome. By building a statistical model derived from large, independent capture reactions, we will identify new features of molecular inversion probes that correlate with capture efficiency. Incorporating these features into the probe design will improve in silico predictions of molecular inversion probe capture efficiencies and by extension achieve more acceptable levels of uniformity across targeted sites.