subject:(PheWAS)

Vanderbilt University

1. Levinson, Rebecca Terrall. The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record.

Degree: PhD, Human Genetics, 2016, Vanderbilt University

URL: http://hdl.handle.net/1803/14698

► While genetic association studies have been able to elucidate the importance of genetics in human disease outcomes, these studies are limited by the necessity of… (more)

Subjects/Keywords: PheWAS

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Levinson, R. T. (2016). The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14698

Chicago Manual of Style (16^th Edition):

Levinson, Rebecca Terrall. “The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record.” 2016. Doctoral Dissertation, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/14698.

MLA Handbook (7^th Edition):

Levinson, Rebecca Terrall. “The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record.” 2016. Web. 03 Mar 2021.

Vancouver:

Levinson RT. The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record. [Internet] [Doctoral dissertation]. Vanderbilt University; 2016. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/14698.

Council of Science Editors:

Levinson RT. The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record. [Doctoral Dissertation]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/14698

Vanderbilt University

2. Simonti, Corinne Nicole. Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution.

Degree: PhD, Human Genetics, 2017, Vanderbilt University

URL: http://hdl.handle.net/1803/11256

► The genomics era has seen a staggering increase in the number of whole genome sequences. This has bolstered studies of human populations, and revealed regions… (more)

Subjects/Keywords: PheWAS; Biobank; Neanderthal; Evolution

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Simonti, C. N. (2017). Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11256

Chicago Manual of Style (16^th Edition):

Simonti, Corinne Nicole. “Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/11256.

MLA Handbook (7^th Edition):

Simonti, Corinne Nicole. “Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution.” 2017. Web. 03 Mar 2021.

Vancouver:

Simonti CN. Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/11256.

Council of Science Editors:

Simonti CN. Leveraging Biobanks and PheWAS to Uncover the Health Consequences of Recent Human Evolution. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/11256

Penn State University

3. Verma, Anurag. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15007auv13

► In an emerging approach called precision medicine, the primary focus is to utilize an individual’s clinical data along with genetic, environmental, and lifestyle information to… (more)

▼ In an emerging approach called precision medicine, the primary focus is to utilize an individual’s clinical data along with genetic, environmental, and lifestyle information to tailor clinical care. The initial steps toward precision medicine involve enrolling individuals into studies to link their genotype and phenotype data. Patient data can be used to discover clinically relevant genetic associations. The most common methodology to identify genetic associations is called an genome-wide association study (GWAS), in which tests for associations are performed between single-nucleotide polymorphisms (SNPs) across the genome (usually over 500,000 SNPs) and a single disease outcome or trait. There is now a growing amount of evidence to demonstrate the success of some of these genetic associations. However, the impact of GWAS has been limited due to its focus on a single phenotype, and hence, the effect of a given SNP across multiple phenotypes cannot be explored. An alternative approach called a phenome-wide association study (PheWAS) has been successful in simultaneously scanning genome-wide significant variants over hundreds of phenotypes. Using this approach, we can identify genetic variants associated with a wide range of phenotypes, also referred to as cross-phenotype associations. Such findings have the potential to identify pleiotropy (where one variant is affecting two or more independent phenotypes with same underlying biological mechanism) or an underlying genetic architecture of comorbidities. The majority of PheWAS have used data from de-identified electronic health records (EHRs) linked to genotype data, and a few have been performed in large-scale epidemiologic studies and clinical trials. While existing studies have demonstrated the development of PheWAS methodology, the focus has remained on a small set of genome-wide significant SNPs or a genomic region of iv interest. After advances in genotyping and sequencing technologies, as well as in phenotype data collection, it is imperative to apply PheWAS on a genome-wide scale. It will allow us to investigate genetic associations across all SNPs and phenotypes in a study population. However, there can be many challenges with expanding the current PheWAS approach to investigate associations across the genome. In this dissertation, I aim to address following specific challenges regarding large-scale PheWAS analysis. 1) Evaluating heterogeneous groups simultaneously makes precision medicine impossible; stratifying samples based on context such as age, sex, or drugs can help to improve precision in identifying true genetic associations (Chapter 2). 2) The number of association tests raise the statistical threshold of significance in such a way that finding the significant associations is difficult. Also, the impact of factors such as sample size, casecontrol ratio, and minor allele frequency on the statistical power to identify associations have not been explored (Chapter 3). 3) Integrating results from independent PheWAS using different types of data sets (e.g.,… Advisors/Committee Members: Marylyn Ritchie, Dissertation Advisor/Co-Advisor, Moriah Louise Szpara, Committee Chair/Co-Chair, Ross Cameron Hardison, Committee Member, N/A, Committee Member, Yu Zhang, Outside Member.

Subjects/Keywords: PheWAS; EHR; GWAS; Genomics; Biobank

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Verma, A. (2018). PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15007auv13

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Verma, Anurag. “PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.” 2018. Thesis, Penn State University. Accessed March 03, 2021. https://submit-etda.libraries.psu.edu/catalog/15007auv13.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Verma, Anurag. “PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA.” 2018. Web. 03 Mar 2021.

Vancouver:

Verma A. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Mar 03]. Available from: https://submit-etda.libraries.psu.edu/catalog/15007auv13.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Verma A. PheWAS AND BEYOND: APPROACHES TO ADDRESS CHALLENGES FOR IDENTIFYING ROBUST ASSOCIATIONS USING CLINICAL DATA. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15007auv13

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University

4. Fish, Alexandra Elizabeth. Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans.

Degree: PhD, Human Genetics, 2017, Vanderbilt University

URL: http://hdl.handle.net/1803/11250

► Epistasis is a phenomenon wherein the effect of a genetic variant on a phenotype is dependent on the genomic context. Better understanding epistastic relationships between… (more)

▼ Epistasis is a phenomenon wherein the effect of a genetic variant on a phenotype is dependent on the genomic context. Better understanding epistastic relationships between variants, often termed interactions, can shed light on novel genomic loci associated with complex disease, which may improve our understanding of the underlying biological mechanisms. Additionally, capturing epistastic effects in models of disease risk may help improve predictions of at-risk populations, or the prediction of a variant’s deleteriousness in precision medicine initiatives. However, the study of epistasis faces unique methodological challenges, and consequently, evidence for regulatory epistasis remains elusive in humans. In this work, I address two major challenges within the field of regulatory epistasis: the development of statistical best practices, and the investigation of epistasis within haplotypes. In Chapter 2, I illustrate that traditional quality control procedures are insufficient to correct for confounding in studies of epistasis, and develop a set of additional guidelines for future studies. Once these were applied, I found little evidence for epistasis between common, unlinked variants influencing gene expression levels. In Chapter 3, I leverage unique properties of admixed populations to investigate epistasis within ancestral haplotypes disrupted by ancestry-specific recombination events. I find several examples of epistasis with plausible biological support, which serve as a proof of principle for the utility of this approach. Overall, these findings indicate that regulatory epistasis likely has small effects, occurs within haplotypes, or occurs between distant genomic regions; we recommend future studies of epistasis focus on these possibilities. Advisors/Committee Members: William Scott Bush (committee member), John Anthony Capra (committee member), Melinda Aldrich (committee member), Joseph Lee Rodgers (committee member), Douglas P. Mortlock (Committee Chair).

Subjects/Keywords: epistasis; gene expression; admixed populations; eQTL; PheWAS

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Fish, A. E. (2017). Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11250

Chicago Manual of Style (16^th Edition):

Fish, Alexandra Elizabeth. “Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/11250.

MLA Handbook (7^th Edition):

Fish, Alexandra Elizabeth. “Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans.” 2017. Web. 03 Mar 2021.

Vancouver:

Fish AE. Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/11250.

Council of Science Editors:

Fish AE. Leveraging gene expression and local ancestry to investigate regulatory epistasis in humans. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/11250

Vanderbilt University

5. Osterman, Travis John. Extracting Detailed Tobacco Exposure From The Electronic Health Record.

Degree: MS, Biomedical Informatics, 2017, Vanderbilt University

URL: http://hdl.handle.net/1803/13004

► Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Natural language processing (NLP) tools exist to determine smoking status… (more)

▼ Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Natural language processing (NLP) tools exist to determine smoking status (ever-smoker vs. never-smoker) from electronic health record data, but no system to date extracts detailed smoking data needed to assess a patient’s eligibility for lung cancer screening. Here we describe the Smoking History And Pack-year Extraction System (SHAPES), a rules-based, NLP system to quantify tobacco exposure from electronic clinical notes. SHAPES was developed based on 261 patient records with 9,573 clinical notes and validated on 352 randomly selected patient records with 4,040 notes. F-measures for never-smoking status, ever-smoking status, rate of smoking, duration of smoking, quantity of cigarettes, and years quit were 0.86, 0.82, 0.79, 0.62, 0.64, and 0.61, respectively. Sixteen of 22 individuals eligible for lung cancer screening were identified (precision = 0.94, recall = 0.73). SHAPES was compared to a previously validated smoking classification system using a phenome wide association study (PheWAS). SHAPES predicted similar significant associations with 66% less sample size (10,000 vs. 35,788), and detected 411 (268%) more associations in the full dataset than when using just ever/never smoking status. Using smoking data from SHAPES, a smoking genome by environment interaction study found 57 statistically significant interactions between smoking and diseases including previously describes interactions between ischemic heart disease and rs1746537, obesity and rs10871777, and type 2 diabetes and rs2943641. These studies support the use of SHAPES for lung cancer screening and other research requiring quantitative smoking history. External validation needs to be performed prior to implementation at other medical centers. Advisors/Committee Members: Mia Levy, M.D., Ph.D. (committee member), Pierre Massion, M.D. (committee member), Josh Denny, M.D., M.S. (Committee Chair).

Subjects/Keywords: data extraction; lung cancer screening; phewas; gxe; smoking; natural language processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Osterman, T. J. (2017). Extracting Detailed Tobacco Exposure From The Electronic Health Record. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13004

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Osterman, Travis John. “Extracting Detailed Tobacco Exposure From The Electronic Health Record.” 2017. Thesis, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/13004.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Osterman, Travis John. “Extracting Detailed Tobacco Exposure From The Electronic Health Record.” 2017. Web. 03 Mar 2021.

Vancouver:

Osterman TJ. Extracting Detailed Tobacco Exposure From The Electronic Health Record. [Internet] [Thesis]. Vanderbilt University; 2017. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/13004.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Osterman TJ. Extracting Detailed Tobacco Exposure From The Electronic Health Record. [Thesis]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/13004

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Chouchana, Laurent. Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches.

Degree: Docteur es, Pharmacologie, 2014, Université Paris Descartes – Paris V

URL: http://www.theses.fr/2014PA05P616

►

Les thiopurines sont des médicaments cytotoxiques et immunosuppresseurs largement prescrits, notamment dans les maladies inflammatoires chroniques de l’intestin (MICI). Ils représentent l’un des meilleurs exemples… (more)

▼

Les thiopurines sont des médicaments cytotoxiques et immunosuppresseurs largement prescrits, notamment dans les maladies inflammatoires chroniques de l’intestin (MICI). Ils représentent l’un des meilleurs exemples d’application clinique de la pharmacogénétique avec le dépistage du déficit en thiopurine S-méthyltransférase (TPMT), enzyme clé du métabolisme des thiopurines. La variabilité interindividuelle de la réponse à ces médicaments rend nécessaire leur optimisation thérapeutique. Ce travail de thèse a d’une part, analysé les relations entre activité TPMT et concentrations des métabolites thiopuriniques, et d’autre part, recherché des facteurs associés à la résistance aux thiopurines. A l’aide d’une base de données pharmacogénétiques hospitalière et d’une étude « PheWAS » à partir d’un entrepôt de données cliniques, nous avons analysé la distribution et la corrélation génotype-phénotype pour la TPMT, en lien avec les concentrations des métabolites thiopuriniques. Nous avons observé qu’une activité TPMT très élevée (phénotype « ultra-rapide ») était associée à des paramètres clinico-biologiques reflétant une maladie évolutive et un traitement inefficace dans les MICI. De plus, une étude clinique rétrospective dans les MICI pédiatriques a permis d’identifier des facteurs associés à la lymphopénie observée sous thiopurines. Enfin, à partir d’un modèle in vitro fondé sur des lignées cellulaires lymphoblastoïdes (LCL) sélectionnées, nous avons établi une signature transcriptomique, incluant 32 gènes, prédictive de la résistance aux thiopurines. Une analyse fonctionnelle bioinformatique a abouti à l’identification de voies métaboliques liées à la protéine p53 et au cycle cellulaire, ainsi que des mécanismes moléculaires associés à la résistance aux thiopurines. En conclusion, ce travail de thèse, qui a exploré la variabilité de réponse aux thiopurines et tout particulièrement la résistance à ces médicaments, propose des hypothèses pour l’individualisation et l’optimisation thérapeutique des thiopurines.

Thiopurines are cytotoxic and immunosuppressive drugs widely prescribed, mainly in inflammatory bowel disease (IBD). They constitute one of the best success story of pharmacogenetic implementation into clinical practice based on the screening of thiopurine S-methyltransferase (TPMT) deficiency, a key enzyme in thiopurine metabolism. Optimization of thiopurine response is challenging because of its large interindividual variability such as inefficacy and toxicities. This thesis has explored, on one hand, the relationships between TPMT activity and metabolite concentrations, and on the other hand, factors associated with thiopurine inefficacy. Using a primary care pharmacogenetic database, we first analyzed TPMT distribution and genotype-phenotype correlation, in relation with thiopurine metabolites in a large population. Using a PheWAS study based on a clinical data warehouse we then reported that a very high TPMT activity (“ultra-rapid” phenotype) was associated with parameters of active IBD and poor response to…

Advisors/Committee Members: Loriot, Marie-Anne (thesis director).

Subjects/Keywords: Thiopurines; Thiopurine S-méthyltransférase (TPMT); Optimisation thérapeutique; Résistance thérapeutique; Pharmacogénomique; PheWAS; Lignées cellulaires lymphoblastoïdes; Thiopurines; Thiopurine S-methyltransferase (TPMT); Drug optimization; Therapeutic resistance; Pharmacogenomics; PheWAS; Lymphoblastoid cell lines; 615.7

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chouchana, L. (2014). Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches. (Doctoral Dissertation). Université Paris Descartes – Paris V. Retrieved from http://www.theses.fr/2014PA05P616

Chicago Manual of Style (16^th Edition):

Chouchana, Laurent. “Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches.” 2014. Doctoral Dissertation, Université Paris Descartes – Paris V. Accessed March 03, 2021. http://www.theses.fr/2014PA05P616.

MLA Handbook (7^th Edition):

Chouchana, Laurent. “Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches.” 2014. Web. 03 Mar 2021.

Vancouver:

Chouchana L. Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches. [Internet] [Doctoral dissertation]. Université Paris Descartes – Paris V; 2014. [cited 2021 Mar 03]. Available from: http://www.theses.fr/2014PA05P616.

Council of Science Editors:

Chouchana L. Optimisation de la réponse aux thiopurines par la pharmacogénétique : approches in vitro et cliniques : Thiopurine response optimization using pharmacogenomics : in vitro and clinical approaches. [Doctoral Dissertation]. Université Paris Descartes – Paris V; 2014. Available from: http://www.theses.fr/2014PA05P616

Penn State University

7. Hall, Molly Ann. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/26751

► Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with… (more)

▼ Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships among genetic loci and across traits. Methods that embrace pleiotropy (the effect of one locus on more than one trait), gene-environment (GxE) and gene-gene (GxG) interactions will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on an individual’s genotype and environment. Additionally, a richer picture of the genetic and environmental aspects that impact complex disease will inform environmental regulations to protect vulnerable populations. Three key limitations of GWAS lead to an inability to robustly model trait prediction in a manner that reflects biological complexity: 1) GWAS explore traits in isolation, one phenotype at a time, preventing investigators from uncovering relationships that exist among multiple traits; 2) GWAS do not account for the exposome; rather, they simply explore the effect of genetic loci on an outcome; and 3) GWAS do not allow for interactions between genetic loci, despite the complexity that exists in biology. The aims described in this dissertation address these limitations. Methods employed in each aim have the potential to: uncover genetic interactions, unveil complex biology behind phenotype networks, inform public policy decisions concerning environmental exposures, and ultimately assess individual disease-risk. Advisors/Committee Members: Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor, Marylyn Deriggi Ritchie, Committee Chair/Co-Chair, Santhosh Girirajan, Committee Chair/Co-Chair, Scott Brian Selleck, Committee Member, Ross Cameron Hardison, Committee Member, George H Perry, Committee Member, Catherine Mc Carty, Special Member.

Subjects/Keywords: gene-gene interactions; epistasis; PheWAS; phenome; EWAS; exposome; gene-environment interactions; complex traits

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hall, M. A. (2015). Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/26751

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hall, Molly Ann. “Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.” 2015. Thesis, Penn State University. Accessed March 03, 2021. https://submit-etda.libraries.psu.edu/catalog/26751.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hall, Molly Ann. “Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.” 2015. Web. 03 Mar 2021.

Vancouver:

Hall MA. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Mar 03]. Available from: https://submit-etda.libraries.psu.edu/catalog/26751.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hall MA. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/26751

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Michigan Technological University

8. Li, Xueling. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.

Degree: PhD, Department of Mathematical Sciences, 2019, Michigan Technological University

URL: https://digitalcommons.mtu.edu/etdr/813

► Genome-wide association studies (GWAS) have successfully detected tens of thousands of robust SNP-trait associations. Earlier researches have primarily focused on association studies of genetic… (more)

▼ Genome-wide association studies (GWAS) have successfully detected tens of thousands of robust SNP-trait associations. Earlier researches have primarily focused on association studies of genetic variants and some well-defined functions or phenotypic traits. Emerging evidence suggests that pleiotropy, the phenomenon of one genetic variant affects multiple phenotypes, is widespread, especially in complex human diseases. Therefore, individual phenotype analyses may lose statistical power to identify the underlying genetic mechanism. Contrasting with single phenotype analyses, joint analysis of multiple phenotypes exploits the correlations between phenotypes and aggregates multiple weak marginal effects and is therefore likely to provide new insights into the functional consequences of genetic variations. This dissertation includes two papers, corresponding to two primary research projects I have done during my Ph.D. study, with each distributed in one chapter. Chapter 1 proposed an innovative method, which referred to as HC-CLC, for joint analysis of multipole phenotypes using a Hierarchical Clustering (HC) approach followed by a Clustering Linear Combination (CLC) method. The HC step partitions phenotypes into clusters. The CLC method is then used to test the association between the genetic variant and all phenotypes, which is done by combining individual test statistics while taking full advantage of the clustering information in the HC step. Extensive simulations together with the COPDGene data analysis have been used to assess the Type I error rates and the power of our proposed method. Our simulation results demonstrate that the Type I error rates of HC-CLC are effectively controlled in different realistic settings. HC-CLC either outperforms all other methods or has statistical power that is very close to the most powerful alternative method with which it has been compared. In addition, our real data analysis shows that HC-CLC is an appropriate method for GWAS. Chapter 2 redesigned the PheCLC (Phenome-wide association study that uses the CLC method) which was previously developed by our research group. The refined method is then applied on the UKBiobank data, a large cohort study across the United Kingdom, to test the validity and understand the limitations of the proposed method. We have named our new method UKB-PheCLC. The UKB-PheCLC method is an EHR-based PheWAS. In the first step, it classifies the whole phenome into different phenotypic categories according to the UK Biobank ICD codes. In the second step, the CLC method is applied to each phenotypic category to derive a CLC-based p-value for testing the association between the genetic variant of interest and all phenotypes in that category. In the third step, the CLC-based p-values of all categories are combined by using a strategy resemble that of the Adaptive Fisher's Combination (AFC) method. Overall, UKB-PheCLC harnesses the powerful resource of the UK Biobank and considers the possibility that phenotypes can be grouped into different… Advisors/Committee Members: Qiuying Sha.

Subjects/Keywords: Statistical Methodology Development; UK Biobank; PheWAS; Joint Analysis of Multiple Phenotypes; Applied Statistics; Bioinformatics; Biostatistics; Health Information Technology; Respiratory Tract Diseases; Statistical Methodology; Statistics and Probability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, X. (2019). STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/813

Chicago Manual of Style (16^th Edition):

Li, Xueling. “STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.” 2019. Doctoral Dissertation, Michigan Technological University. Accessed March 03, 2021. https://digitalcommons.mtu.edu/etdr/813.

MLA Handbook (7^th Edition):

Li, Xueling. “STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS.” 2019. Web. 03 Mar 2021.

Vancouver:

Li X. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. [Internet] [Doctoral dissertation]. Michigan Technological University; 2019. [cited 2021 Mar 03]. Available from: https://digitalcommons.mtu.edu/etdr/813.

Council of Science Editors:

Li X. STATISTICAL METHODS FOR JOINT ANALYSIS OF MULTIPLE PHENOTYPES AND THEIR APPLICATIONS FOR PHEWAS. [Doctoral Dissertation]. Michigan Technological University; 2019. Available from: https://digitalcommons.mtu.edu/etdr/813

Vanderbilt University

9. -3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.

Degree: PhD, Biostatistics, 2020, Vanderbilt University

URL: http://hdl.handle.net/1803/15924

► When conducting analyses on high dimensional data, one could face statistical difficulties due to large dimensionality and the noisy nature of the data. In this… (more)

▼ When conducting analyses on high dimensional data, one could face statistical difficulties due to large dimensionality and the noisy nature of the data. In this dissertation, we specifically look into potential complexities one might encounter when analyzing electronic medical record (EMR) and functional magnetic resonance imaging (fMRI) data. Phenome-Wide Association study (PheWAS) is a newly proposed method that scans through phenotypes (Phecodes) with a specific genotype of interest using logistic regression. Since the clinical diagnoses in EMR are often inaccurate which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classifying controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased estimates. However, this method could be inefficient and the accuracy of the lists cannot be guaranteed. We propose to estimate relative risk (RR) instead. With simulation and real data application, we show that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to extend PheWAS to larger-scale phenotypes which preserve more disease-related clinical information than Phecodes. The main purpose of task-induced fMRI is to measure neuronal activities related to specific task. fMRI data usually require several preprocessing steps before analysis. Among all, spatial smoothing is a necessary step known to increase signal-to-noise ratios but the choice of degree of smoothing is often arbitrary. One critical statistical issue in fMRI analysis is the balance between Type I and II error rates. We first demonstrate the influence of the degree of smoothing and experimental factors on the trade-off between Type I and II error rates. Next, we propose to use second-generation p-values (SGPV) as an inference tool instead of the traditional p-values for hypothesis testing. By allowing the interval null hypothesis, we have shown that SGPV is able to alleviate the critical statistical issue by controlling Type I error rate more steadily while obtaining enough power. Advisors/Committee Members: Johnson, Robert (advisor), Kang, Hakmook (advisor).

Subjects/Keywords: EMR; PheWAS; fMRI; Statistical analysis; study design; Type I error rate; Type II error rate; p-value; Multiple comparison; Second-generation p-values; SGPV; Interval null; Hypothesis testing

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APA (6^th Edition):

-3660-4570. (2020). Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15924

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Chicago Manual of Style (16^th Edition):

-3660-4570. “Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.” 2020. Doctoral Dissertation, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/15924.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-3660-4570. “Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.” 2020. Web. 03 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2020. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/15924.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. [Doctoral Dissertation]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/15924

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

10. Pandey, Ashutosh Kumar. Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.

Degree: PhD, Biomedical Sciences, 2015, University of Tennessee Health Science Center

URL: https://dc.uthsc.edu/dissertations/359

► Reverse genetics methods, particularly the production of gene knockouts and knockins, have revolutionized the understanding of gene function. High throughput sequencing now makes it… (more)

Subjects/Keywords: BXD; complex traits; DBA/2J; nextgeneration sequencing; PheWAS; reverse genetics; Genetic Phenomena; Genetic Processes; Medical Genetics; Medical Sciences; Medicine and Health Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pandey, A. K. (2015). Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes. (Doctoral Dissertation). University of Tennessee Health Science Center. Retrieved from https://dc.uthsc.edu/dissertations/359

Chicago Manual of Style (16^th Edition):

Pandey, Ashutosh Kumar. “Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.” 2015. Doctoral Dissertation, University of Tennessee Health Science Center. Accessed March 03, 2021. https://dc.uthsc.edu/dissertations/359.

MLA Handbook (7^th Edition):

Pandey, Ashutosh Kumar. “Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.” 2015. Web. 03 Mar 2021.

Vancouver:

Pandey AK. Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes. [Internet] [Doctoral dissertation]. University of Tennessee Health Science Center; 2015. [cited 2021 Mar 03]. Available from: https://dc.uthsc.edu/dissertations/359.

Council of Science Editors:

Pandey AK. Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes. [Doctoral Dissertation]. University of Tennessee Health Science Center; 2015. Available from: https://dc.uthsc.edu/dissertations/359

Vanderbilt University

11. Bloodworth, Melissa Harintho. Regulation of Immune Responses during Airway Inflammation.

Degree: PhD, Microbiology and Immunology, 2017, Vanderbilt University

URL: http://hdl.handle.net/1803/11246

► Allergic asthma is refractory to corticosteroid treatment in up to 10% of patients and often leads to hospital admissions caused by respiratory viral and/ or… (more)

Subjects/Keywords: type 2 immunity (Th2); gamma-delta 17 (γδ17) cells; STAT6; Klebsiella pneumoniae; glucagon-like peptide 1 (GLP-1); respiratory syncytial virus (RSV); phenome-wide association study (PheWAS); regulatory T cells (Tregs); prostacyclin (PGI2)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bloodworth, M. H. (2017). Regulation of Immune Responses during Airway Inflammation. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11246

Chicago Manual of Style (16^th Edition):

Bloodworth, Melissa Harintho. “Regulation of Immune Responses during Airway Inflammation.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed March 03, 2021. http://hdl.handle.net/1803/11246.

MLA Handbook (7^th Edition):

Bloodworth, Melissa Harintho. “Regulation of Immune Responses during Airway Inflammation.” 2017. Web. 03 Mar 2021.

Vancouver:

Bloodworth MH. Regulation of Immune Responses during Airway Inflammation. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/1803/11246.

Council of Science Editors:

Bloodworth MH. Regulation of Immune Responses during Airway Inflammation. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/11246

Open Access Theses and Dissertations