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Vanderbilt University
1.
Cummings, Anna Christine.
Power and type 1 error for large pedigree analyses of binary traits.
Degree: MS, Interdisciplinary Studies: Applied Statistics, 2012, Vanderbilt University
URL: http://hdl.handle.net/1803/14871 
► Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests…
(more)
▼ Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests need to be corrected for relatedness among study participants, and linkage analyses require subdividing and simplifying the pedigree structures. In this thesis work I simulated SNP (single nucleotide polymorphism) data in complex pedigree structures based on an Amish pedigree. I evaluated type 1 error rates and power when performing two-point and multipoint linkage after dividing the pedigree into subpedigrees. I also ran MQLS (modified likelihood score test) to test for allelic association in the subpedigrees and in the unified pedigree.
Advisors/Committee Members: Jonathan L. Haines (committee member), William S. Bush (committee member), Wells%22%29&pagesize-30">
Tricia A.
Thornton-
Wells (committee member).
Subjects/Keywords: association; linkage; simulations; power; type 1 error; pedigrees
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APA (6th Edition):
Cummings, A. C. (2012). Power and type 1 error for large pedigree analyses of binary traits. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14871
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Cummings, Anna Christine. “Power and type 1 error for large pedigree analyses of binary traits.” 2012. Thesis, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/14871.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Cummings, Anna Christine. “Power and type 1 error for large pedigree analyses of binary traits.” 2012. Web. 28 Feb 2021.
Vancouver:
Cummings AC. Power and type 1 error for large pedigree analyses of binary traits. [Internet] [Thesis]. Vanderbilt University; 2012. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/14871.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Cummings AC. Power and type 1 error for large pedigree analyses of binary traits. [Thesis]. Vanderbilt University; 2012. Available from: http://hdl.handle.net/1803/14871
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Vanderbilt University
2.
Sivley, Robert Michael.
Clustering Rare Event Features to Increase Statistical Power.
Degree: MS, Computer Science, 2013, Vanderbilt University
URL: http://hdl.handle.net/1803/12064
► Rare genetic variation has been put forward as a major contributor to the development of disease; however, it is inherently difficult to associate rare variants…
(more)
▼ Rare genetic variation has been put forward as a major
contributor to the development of disease; however, it is inherently difficult to associate rare variants with disease, as the low number of observations greatly reduces statistical power. Binning is a method that groups several variants together and merges them into a single feature, sacrificing resolution to increase statistical power. Binning strategies are applicable to rare variant analysis in any field, though their effectiveness is dependent on the method used to group variants. This thesis presents a flexible workflow for rare variant analysis, comprised of five sequential steps: identification of rare variants, annotation of those variants, clustering the variants, collapsing those clusters, and statistical analysis. There are no restrictions on which clustering algorithms are applied, so a review of the core clustering paradigms is provided as an introduction for readers unfamiliar with the field. Also presented is RVCLUST, an R package that facilitates all stages of the described workflow and provides a collection of interfaces to common clustering algorithms and statistical tests. The utility of RVCLUST is demonstrated in a genetic analysis of rare variants in gene regulatory regions and their effect on gene expression. The results of this analysis suggest that informed clustering is an effective alternative to existing strategies, discovering the same associations while avoiding the statistical complications introduced by other binning methods.
Advisors/Committee Members: Wells%22%29&pagesize-30">Dr.
Tricia A.
Thornton-
Wells (committee member),
Dr. William S. Bush (committee member),
Dr. Douglas H. Fisher (Committee Chair).
Subjects/Keywords: power; statistics; rvclust; clustering; rare event; rare variant
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APA (6th Edition):
Sivley, R. M. (2013). Clustering Rare Event Features to Increase Statistical Power. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12064
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Thesis, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/12064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sivley, Robert Michael. “Clustering Rare Event Features to Increase Statistical Power.” 2013. Web. 28 Feb 2021.
Vancouver:
Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Internet] [Thesis]. Vanderbilt University; 2013. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/12064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sivley RM. Clustering Rare Event Features to Increase Statistical Power. [Thesis]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/12064
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Vanderbilt University
3.
Veatch, Olivia Jean.
Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT.
Degree: PhD, Human Genetics, 2013, Vanderbilt University
URL: http://hdl.handle.net/1803/15041
► Autism Spectrum Disorder is a neurodevelopmental condition with evidence for genetic susceptibility. However, effect sizes for implicated loci are small, and current evidence does not…
(more)
▼ Autism Spectrum Disorder is a neurodevelopmental condition with evidence for genetic susceptibility. However, effect sizes for implicated loci are small, and current evidence does not explain the heritability. Phenotypic heterogeneity could be complicating genetic factor identification. We used multiple sources of behavioral and physiological data to identify ASD subgroups. Principal Components Analysis refined these data from an ASD dataset into 15 components best explaining phenotypic variance. Clustering identified two distinct groups, primarily based on phenotype severity. Using an independent dataset, we identified 15 data components and confirmed the severity-based dichotomy. There is significant familial clustering within groups, suggesting the clusters recapitulate genetic etiology. We performed separate family-based analyses of genetic data based on subgroup. Pathway analysis was performed. Five biological pathways uniquely associate with the ‘less severe’ subgroup. Ten genes potentially drive these associations. Five different pathways uniquely associate with the ‘more severe’ subgroup. 24 genes potentially drive associations with this subgroup. There is minimal overlap when comparing genes associated with different subgroups. We validated results in an independent dataset and see unique biological mechanisms associate with the ‘more severe’ and ‘less severe’ subgroups. Our results suggest meaningful subgroup definitions can help clarify the genetics of ASD. Uncovering pathways associated with subgroups further elucidated potential genes involved in trait expression. To progress toward understanding how these findings could be useful for treatment, functional characterization was necessary. Ample evidence indicates many drugs have altered efficacy and side effects in relation to genetic background. For many compounds used to treat ASD comorbid symptoms, individuals exhibit sleep problems. We evaluated the enzyme catalyzing the final reaction in melatonin synthesis, Acetylserotonin O-methyltransferase. We screened cell lines generated from patient DNA for differential expression effects against compounds presently used to treat symptoms. We replicated previous findings indicating homozygous presence of ASD risk alleles at promoter SNPs results in decreased gene expression. We also observe previously unreported expression effects attributable to heterozygosity at promoter SNPs, and a SNP in the 5'-UTR. Results show no significant changes in gene expression upon exposure to small molecule compounds for the non-risk haplotype.
Advisors/Committee Members: Douglas P. Mortlock (committee member), Colleen M. Niswender (committee member), Jeremy M. Veenstra-VanderWeele (committee member), Jonathan L. Haines (committee member), Wells%22%29&pagesize-30">
Tricia A.
Thornton-
Wells (Committee Chair).
Subjects/Keywords: Autism Spectrum Disorder; Human Genetics; Multivariate Statistics; Pharmacogenetics; Phenotyping; Ne
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APA ·
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APA (6th Edition):
Veatch, O. J. (2013). Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15041
Chicago Manual of Style (16th Edition):
Veatch, Olivia Jean. “Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/15041.
MLA Handbook (7th Edition):
Veatch, Olivia Jean. “Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT.” 2013. Web. 28 Feb 2021.
Vancouver:
Veatch OJ. Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/15041.
Council of Science Editors:
Veatch OJ. Identifying biological pathways implicated in defined subgroups of phenotypic expression for Autism Spectrum Disorders and evaluating small molecule effects on expression of ASMT. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/15041
Vanderbilt University
4.
Oetjens, Matthew Thomas.
Pharmacogenetic Discovery in an EMR-Biorepository.
Degree: PhD, Human Genetics, 2014, Vanderbilt University
URL: http://hdl.handle.net/1803/11314
► Adverse drug reactions (ADRs) can be highly influenced by genetic variation. For example, immunosuppressants prescribed to patients who have undergone organ transplantation have severe nephrotoxic…
(more)
▼ Adverse drug reactions (ADRs) can be highly influenced by genetic variation. For example, immunosuppressants prescribed to patients who have undergone organ transplantation have severe nephrotoxic side effects. Pharmacogenetic studies aimed at identifying genetic risk factors for ADRs show promise as an important step in their prevention. Biorepositories linked to electronic medical records (EMR), such as Vanderbilt’s BioVU, are an emerging resource for the generation of large datasets to identify genetic variants involved in ADRs. Described in this dissertation is the utilization of BioVU to assess of the performance of Ilumina’s ADME Core Panel, a pharmacogenetic genotyping platform in the research setting. The platform was then applied in BioVU for two research studies. The first study demonstrated the potential of EMR-linked biorepositories to characterize the genetic risk factors for immunosuppressant induced nephrotoxicity. Next, the ADME Core Panel was applied in a phenome-wide association study to identify novel genotype-phenotype associations. An association between a transporter expressed in the proximal tubule of the kidney and renal osteodystrophy was identified.
Advisors/Committee Members: Dana C. Crawford (committee member), Josua C. Denny (committee member), Wells%22%29&pagesize-30">
Tricia A.
Thornton-
Wells (committee member),
Marylyn D. Ritchie (committee member),
William S. Bush (Committee Chair).
Subjects/Keywords: Pharmacogenetics; SNP; genetics; genomics; BioVU
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APA (6th Edition):
Oetjens, M. T. (2014). Pharmacogenetic Discovery in an EMR-Biorepository. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11314
Chicago Manual of Style (16th Edition):
Oetjens, Matthew Thomas. “Pharmacogenetic Discovery in an EMR-Biorepository.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/11314.
MLA Handbook (7th Edition):
Oetjens, Matthew Thomas. “Pharmacogenetic Discovery in an EMR-Biorepository.” 2014. Web. 28 Feb 2021.
Vancouver:
Oetjens MT. Pharmacogenetic Discovery in an EMR-Biorepository. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/11314.
Council of Science Editors:
Oetjens MT. Pharmacogenetic Discovery in an EMR-Biorepository. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/11314
Vanderbilt University
5.
Hoffman, Joshua David.
Modeling Macular Degeneration Using Quantitative Phenotypes.
Degree: PhD, Human Genetics, 2015, Vanderbilt University
URL: http://hdl.handle.net/1803/10680
► Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the United States (US). Although a multitude of studies have…
(more)
▼ Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the United States (US). Although a multitude of studies have shown that both genetic and environmental factors contribute to the pathogenesis of AMD, little is known on how genetics affects the disease’s rate of progression. We performed a quantitative genetic analysis of drusen progression during the intermediate stage of the disease to understand the role of known AMD genetic variation to this phenotype. Drusen progression was tested against 19 previously identified genetic variants using a cumulative genetic risk score, single variant analyses, and a pathway analysis. We do not observe significant correlation between the 19 variant cumulative genetic risk score and drusen progression (rho = 0.039; p = 0.543). Single marker tests of the remaining 15 variants shows a nominally significant association with rs943080 in VEGFA (p = 0.028). The most highly associated pathway in our pathway analysis is the cell adhesion molecule pathway (p < 0.0001). To understand the contribution of rare-genetic variation to AMD, we performed exome sequencing in five members of a small nuclear Amish family who lack the common risk alleles at the major AMD risk loci. We identified a variant (P503A) in CFH that is not present in dbSNP or 1000Genomes and is associated with AMD in an Ohio and Indiana cohort (p = 9.27x10-13).
Advisors/Committee Members: Milam A. Brantley (committee member), Chun Li (committee member), Jonathan L. Haines (committee member), David C. Samuels (committee member), Wells%22%29&pagesize-30">
Tricia A.
Thornton-
Wells (Committee Chair).
Subjects/Keywords: AMD; genetics; Age-Related Macular Degeneration; genomics; assocation analysis; linkage analysis
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APA ·
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APA (6th Edition):
Hoffman, J. D. (2015). Modeling Macular Degeneration Using Quantitative Phenotypes. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10680
Chicago Manual of Style (16th Edition):
Hoffman, Joshua David. “Modeling Macular Degeneration Using Quantitative Phenotypes.” 2015. Doctoral Dissertation, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/10680.
MLA Handbook (7th Edition):
Hoffman, Joshua David. “Modeling Macular Degeneration Using Quantitative Phenotypes.” 2015. Web. 28 Feb 2021.
Vancouver:
Hoffman JD. Modeling Macular Degeneration Using Quantitative Phenotypes. [Internet] [Doctoral dissertation]. Vanderbilt University; 2015. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/10680.
Council of Science Editors:
Hoffman JD. Modeling Macular Degeneration Using Quantitative Phenotypes. [Doctoral Dissertation]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/10680
Vanderbilt University
6.
Pryweller, Jennifer Raechelle.
A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model.
Degree: PhD, Interdisciplinary Studies: Human Genetics, 2013, Vanderbilt University
URL: http://hdl.handle.net/1803/14984
► Williams syndrome (WS) is a rare, neurodevelopmental disorder caused by the deletion of 26 genes on chromosome 7q11.23. WS has a well-defined auditory phenotype, characterized…
(more)
▼ Williams syndrome (WS) is a rare, neurodevelopmental disorder caused by the deletion of 26 genes on chromosome 7q11.23. WS has a well-defined auditory phenotype, characterized by a strong attraction and emotional reactivity to music, abnormal sensitivity to sounds (hyperacusis) and an aversion to or avoidance of sounds (phonophobia). Auditory abnormalities reported in WS also affect a wide range of neurodevelopmental, neuropsychiatric and neurological disorders. Little is known about sensory modulation, or the demonstration of maladaptive emotional and behavioral responses to sensory stimuli in WS. This study aims to describe a neural basis for impaired sensory modulation in atypical auditory processing characteristic of the WS phenotype.
To define functional and structural connectivity between brain regions involved in auditory processing, we recruited 18 individuals with WS and 18 controls, ages 16-50, for neuroimaging. In the absence of external stimuli, “resting state” fMRI (rs-fMRI) measures blood oxygenation level dependent (BOLD) signal that reflects baseline neuronal activation in functionally connected networks of brain regions, including the auditory processing network. We used rs-fMRI to identify a network of functional brain regions involved in auditory processing in WS. White matter (WM) integrity was assessed by DTI parameters providing a quantitative measure of water diffusion through axonal membranes. We used DTI to identify structural integrity differences in WM fiber tracts underlying auditory processing in WS. To provide a basis for understanding sensory modulation impairments in WS, 56 caregivers of individuals with WS, ages 5-49, were recruited to quantitatively describe sensory processing patterns, independent of clinical diagnoses. Atypical auditory response patterns, based on a self-report of sensory processing, were correlated with group differences in functional and structural connectivity in WS. This study constitutes the most comprehensive investigation of patterns of sensory processing in WS, spanning a wide age range, and provides a uniquely developmentally-informed basis for understanding behavioral difficulties and the clinical interventions that could address them. Investigating the relationship between patterns of auditory sensory responses and functional and structural connectivity measures elucidates a brain-behavior relationship related to atypical auditory processing.
Advisors/Committee Members: Carissa J. Cascio, Ph.D. (committee member), Elisabeth M. Dykens, Ph.D. (committee member), Baxter P. Rogers, Ph.D. (committee member), Wells%22%29&pagesize-30">
Tricia A.
Thornton-
Wells (committee member),
Ronald L. Cowan, M.D., Ph.D. (Committee Chair).
Subjects/Keywords: Williams sydrome; fMRI; DTI; neuroimaging; functional connectivity; sensory modulation; auditory processing
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APA ·
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MLA ·
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APA (6th Edition):
Pryweller, J. R. (2013). A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14984
Chicago Manual of Style (16th Edition):
Pryweller, Jennifer Raechelle. “A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed February 28, 2021.
http://hdl.handle.net/1803/14984.
MLA Handbook (7th Edition):
Pryweller, Jennifer Raechelle. “A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model.” 2013. Web. 28 Feb 2021.
Vancouver:
Pryweller JR. A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2021 Feb 28].
Available from: http://hdl.handle.net/1803/14984.
Council of Science Editors:
Pryweller JR. A Neural Basis for Atypical Auditory Processing: A Williams Syndrome Model. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/14984
. 
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