Optimized cell models increase the power of functional genomic studies of complex human traits /

Saved in:
Bibliographic Details
Author / Creator:Kagan, Courtney, author.
Ann Arbor : ProQuest Dissertations & Theses, 2015
Description:1 electronic resource (202 pages)
Format: E-Resource Dissertations
Local Note:School code: 0330
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10773244
Hidden Bibliographic Details
Other authors / contributors:University of Chicago. degree granting institution.
Notes:Includes supplementary digital materials.
Advisors: Yoav Gilad Committee members: Eileen Dolan; Marcelo Nobrega; Carole Ober.
This item is not available from ProQuest Dissertations & Theses.
Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Summary:Recent advances in genetics have allowed the emergence of genome-wide association studies of complex human traits. While sample size is no longer a major impediment to the identification of associations, the resulting data has had limited value and, contrary to hopes, has not drastically improved our understanding of the genetic contribution to complex human traits. One issue is that the vast majority of associated variants are in non-coding regions of the genome where function is not apparent. This leaves researchers sifting through results and performing many more costly follow-up functional studies. As research shifts to more complex traits, which are more likely caused by a large number of common variants with small effects, we have decreased power to detect associations. To overcome issues in multiple testing and our low power to detect variants with small effect sizes, more recently researchers have begun to use expression quantitative trait loci (eQTL). Studies have shown that trait-associated variants are more enriched with eQTLs mapped in a tissue type relevant to the trait rather than an unrelated tissue type. Furthermore, eQTLs can be used as a priori variants of interest by limiting association tests only to these loci. This provides results that have known function within the regulatory landscape and also reduces the multiple testing burden of analysis. The work in this thesis builds upon this new strategy. In Chapter 2 we apply this strategy to female fertility traits and show that using eQTLs to identify functional variants in a relevant tissue can lead to novel associations with complex human traits. In the remaining chapters we focus on a new cellular model that has unique properties that remediate our current limitations in power to detect variants with small effect sizes and improve our functional understanding of variants identified in association studies.