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dc.contributor.advisor Kimmel, Marek
dc.creatorBanuelos, Rosa
dc.date.accessioned 2013-09-16T14:30:07Z
dc.date.accessioned 2013-09-16T14:30:10Z
dc.date.available 2013-09-16T14:30:07Z
dc.date.available 2013-09-16T14:30:10Z
dc.date.created 2013-05
dc.date.issued 2013-09-16
dc.date.submitted May 2013
dc.identifier.urihttps://hdl.handle.net/1911/71920
dc.description.abstract Although complete understanding of the mechanisms of rare genetic variants in disease continues to elude us, Next Generation Sequencing (NGS) has facilitated significant gene discoveries across the disease spectrum. However, the cost of NGS hinders its use for identifying rare variants in common diseases that require large samples. To circumvent the need for larger samples, designing efficient sampling studies is crucial in order to detect potential associations. This research therefore evaluates sampling designs for rare variant - quantitative trait association studies and assesses the effect on power that freely available public cohort data can have in the design. Performing simulations and evaluating common and unconventional sampling schemes results in several noteworthy findings. Specifically, the extreme-trait design is the most powerful design for analyzing quantitative traits. This research also shows that sampling more individuals from the extreme of clinical interest does not increase power. Variant filtering has served as a "proof-of-concept" approach for the discovery of disease-causing genes in Mendelian traits and formal statistical methods have been lacking in this area. However, combining variant filtering schemes with existing rare variant association tests is a practical alternative. Thus, this thesis also compares the robustness of six burden-based rare variant association tests for Mendelian traits after a variant filtering step in the presence of genetic heterogeneity and genotyping errors. This research shows that with low locus heterogeneity, these tests are powerful for testing association. With the exception of the weighted sum statistic (WSS), the remaining tests were very conservative in preserving the type I error when the number of affected and unaffected individuals was unequal. The WSS, on the other hand, had inflated type I error as the number of unaffected individuals increased. The framework presented can serve as a catalyst to improve sampling design and to develop robust statistical methods for association testing.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectRare variants
Common disease
Power
Mendelian
Sampling
dc.title A Simulation-based Approach to Study Rare Variant Associations Across the Disease Spectrum
dc.contributor.committeeMember Leal, Suzanne
dc.contributor.committeeMember Thompson, James R.
dc.contributor.committeeMember Nakhleh, Luay K.
dc.date.updated 2013-09-16T14:30:10Z
dc.identifier.slug 123456789/ETD-2013-05-353
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Banuelos, Rosa. "A Simulation-based Approach to Study Rare Variant Associations Across the Disease Spectrum." (2013) Diss., Rice University. https://hdl.handle.net/1911/71920.


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