Date of Award
Doctor of Philosophy (PhD)
An estimated one in eight couples in the United States are diagnosed with infertility. There is a significant genetic contribution to infertility, with estimates of heritability ranging from 0.2 to 0.5. We know surprisingly little about the genetic causes, with only slightly more than a hundred genes known to cause human infertility. I have been translating recent advances in genomics to study infertility in a more efficient manner, in order to improve our knowledge of the genetic causes. By using high throughput genomics and proteomics datasets from other groups, I was able to feed that into a machine learning algorithm to predict novel fertility function genes. While not perfect, this computational model performs comparably to other publish prediction models. In order to test the top predicted fertility genes I also developed an experimental technique to simultaneously screen up to hundreds of genes for spermatogenesis function in vivo in mice. This method is based off of RNAi, and I was able to benchmark its performance to demonstrate that it performed comparably to other benchmarked RNAi screens in flies. I then used this method to test the top 26 predicted spermatogenesis genes and showed that most of them (24/26) have an important role in spermatogenesis. Using this technique, other groups can screen genes for spermatogenesis function in a fraction of the time and cost compared to the traditional approach of generating knockout mouse lines. Finally, I describe the progress I have made in using genetic engineering to rescue spermatogenesis in mice. By analyzing the missteps I have made in delivering constitutively expressed transgenes and CRISPR genes into mouse testes, I describe the probably reasons for my failure and how to implement future experiments to get more success.
Chair and Committee
Donald F. Conrad
Joseph Dougherty, Barak Cohen, Liang Ma, John Edwards,
Yuan Ho, Nicholas Rui, "Application of Genomic Technologies to Study Infertility" (2016). Arts & Sciences Electronic Theses and Dissertations. 786.
Bioinformatics Commons, Genetics Commons, Molecular Biology Commons
Permanent URL: https://doi.org/10.7936/K7KP80F8