Genome-wide association studies (GWAS) and next generation sequencing (NGS) studies are powerful high-throughput methods of scanning the human genome that have dramatically increased our ability to identify disease-causing genetic variants and estimate the magnitude of their effects. Leveraging the power of these technologies requires statistical methods tailored to the real world complexities of the data from these studies. Statistical methods developed during the era of small candidate gene studies fail to account for the extended scope of genome-wide studies, which encompasses: (1) discovery of disease-associated regions; (2) localization of associations to individual risk variants; and (3) estimation of effect size. In addition, high-throughput sequencing used for large samples differs from traditional Sanger sequencing in that genotyping error varies substantially over a region, which can distort evidence used to identify the disease-associated variant. In this thesis, I model these factors in order to increase accuracy of genetic effect estimation and accuracy of identification of disease-causing variants within disease-associated regions. I address these factors in three related settings: (1) GWAS study used alone to both discover and estimate the size of genetic effect at disease-associated variants; (2) GWAS study followed with sequencing to both discover an associated region via GWAS SNPs and estimate the size of genetic effect using the sequencing data; and (3) GWAS study with sequencing or imputation used jointly to identify candidate causal variants and estimate the corresponding effect sizes within an associated region. I develop novel statistical methods to address the specific localization and estimation problems encountered in each setting. Extensive simulation studies are used to explore the nature of these problems and to compare the performance of the new methods with the standard methods. Application to the Welcome Trust Case Control Consortium Type 1 Diabetes dataset and National Cancer Institute BPC3 aggressive prostate cancer study demonstrates the difference the methods make in the interpretation of evidence in these high-throughput studies.

Genome-wide association studies (GWAS) and next generation sequencing (NGS) studies are powerful high-throughput methods of scanning the human genome that have dramatically increased our ability to identify disease-causing genetic variants and estimate the magnitude of their effects. Leveraging the power of these technologies requires statistical methods tailored to the real world complexities of the data from these studies. Statistical methods developed during the era of small candidate gene studies fail to account for the extended scope of genome-wide studies, which encompasses: (1) discovery of disease-associated regions; (2) localization of associations to individual risk variants; and (3) estimation of effect size. In addition, high-throughput sequencing used for large samples differs from traditional Sanger sequencing in that genotyping error varies substantially over a region, which can distort evidence used to identify the disease-associated variant. In this thesis, I model these factors in order to increase accuracy of genetic effect estimation and accuracy of identification of disease-causing variants within disease-associated regions. I address these factors in three related settings: (1) GWAS study used alone to both discover and estimate the size of genetic effect at disease-associated variants; (2) GWAS study followed with sequencing to both discover an associated region via GWAS SNPs and estimate the size of genetic effect using the sequencing data; and (3) GWAS study with sequencing or imputation used jointly to identify candidate causal variants and estimate the corresponding effect sizes within an associated region. I develop novel statistical methods to address the specific localization and estimation problems encountered in each setting. Extensive simulation studies are used to explore the nature of these problems and to compare the performance of the new methods with the standard methods. Application to the Welcome Trust Case Control Consortium Type 1 Diabetes dataset and National Cancer Institute BPC3 aggressive prostate cancer study demonstrates the difference the methods make in the interpretation of evidence in these high-throughput studies.

Split-mouth trials are a design of randomized controlled trial in dentistry in which divisions of the mouth are the units of randomization. Since there is more than one tooth in each mouth division, the structure of the data is complex, which can create difficulties in the statistical analysis. The aim of this study was to determine what is the most appropriate method to analyze split-mouth trials with continuous outcomes, with regards to the treatment effect estimates, power, type-I error, confidence interval coverage and confidence interval width. A superiority split-mouth trial in the field of periodontology was simulated, using two mouth divisions and varying underlying study characteristics such as correlation among teeth, treatment effects and sample size. Twenty-four statistical methods were compared across 315 scenarios. The performance of the statistical methods depended mainly on the correlation among the data, and a paired t-test performed the best across the different scenarios.

Split-mouth trials are a design of randomized controlled trial in dentistry in which divisions of the mouth are the units of randomization. Since there is more than one tooth in each mouth division, the structure of the data is complex, which can create difficulties in the statistical analysis. The aim of this study was to determine what is the most appropriate method to analyze split-mouth trials with continuous outcomes, with regards to the treatment effect estimates, power, type-I error, confidence interval coverage and confidence interval width. A superiority split-mouth trial in the field of periodontology was simulated, using two mouth divisions and varying underlying study characteristics such as correlation among teeth, treatment effects and sample size. Twenty-four statistical methods were compared across 315 scenarios. The performance of the statistical methods depended mainly on the correlation among the data, and a paired t-test performed the best across the different scenarios.

Capture-recapture methods take their origins from animal abundance estimation, where they were used to estimate the unknown size of the animal population under study. In the late 1940s and again in the late 1960s and early 1970s these same capture-recapture methods were modified and applied to epidemiological list data. Since then through their continued use, in particular in the 1990s, these methods have become popular for the estimation of the completeness of disease registries and for the estimation of the unknown total size of human disease populations. In this thesis we investigate new methods for the analysis of epidemiological list data using capture-recapture methods. In particular we compare two standard methods used to estimate the unknown total population size, and examine new methods which incorporate list mismatch errors and model-selection uncertainty into the process for the estimation of the unknown total population size and its associated confidence interval. We study the use of modified tag loss methods from animal abundance estimation to allow for list mismatch errors in the epidemio-logical list data. We also explore the use of a weighted average method, the use of Bootstrap methods, and the use of a Bayesian model averaging method for incorporating model-selection uncertainty into the estimate of the unknown total population size and its associated confidence interval. In addition we use two previously unanalysed Diabetes studies to illustrate the methods examined and a well-known Spina Bifida Study for simulation purposes. This thesis finds that ignoring list mismatch errors will lead to biased estimates of the unknown total population size and that the list mismatch methods considered here result in a useful adjustment. The adjustment also approximately agrees with the results obtained using a complex matching algorithm. As for the incorporation of model-selection uncertainty, we find that confidence intervals which incorporate model-selection uncertainty are wider and more appropriate than confidence intervals that do not. Hence we recommend the use of tag loss methods to adjust for list mismatch errors and the use of methods that incorporate model-selection uncertainty into both point and interval estimates of the unknown total population size. / Subscription resource available via Digital Dissertations only.

Capture-recapture methods take their origins from animal abundance estimation, where they were used to estimate the unknown size of the animal population under study. In the late 1940s and again in the late 1960s and early 1970s these same capture-recapture methods were modified and applied to epidemiological list data. Since then through their continued use, in particular in the 1990s, these methods have become popular for the estimation of the completeness of disease registries and for the estimation of the unknown total size of human disease populations. In this thesis we investigate new methods for the analysis of epidemiological list data using capture-recapture methods. In particular we compare two standard methods used to estimate the unknown total population size, and examine new methods which incorporate list mismatch errors and model-selection uncertainty into the process for the estimation of the unknown total population size and its associated confidence interval. We study the use of modified tag loss methods from animal abundance estimation to allow for list mismatch errors in the epidemio-logical list data. We also explore the use of a weighted average method, the use of Bootstrap methods, and the use of a Bayesian model averaging method for incorporating model-selection uncertainty into the estimate of the unknown total population size and its associated confidence interval. In addition we use two previously unanalysed Diabetes studies to illustrate the methods examined and a well-known Spina Bifida Study for simulation purposes. This thesis finds that ignoring list mismatch errors will lead to biased estimates of the unknown total population size and that the list mismatch methods considered here result in a useful adjustment. The adjustment also approximately agrees with the results obtained using a complex matching algorithm. As for the incorporation of model-selection uncertainty, we find that confidence intervals which incorporate model-selection uncertainty are wider and more appropriate than confidence intervals that do not. Hence we recommend the use of tag loss methods to adjust for list mismatch errors and the use of methods that incorporate model-selection uncertainty into both point and interval estimates of the unknown total population size. / Subscription resource available via Digital Dissertations only.

Capture-recapture methods take their origins from animal abundance estimation, where they were used to estimate the unknown size of the animal population under study. In the late 1940s and again in the late 1960s and early 1970s these same capture-recapture methods were modified and applied to epidemiological list data. Since then through their continued use, in particular in the 1990s, these methods have become popular for the estimation of the completeness of disease registries and for the estimation of the unknown total size of human disease populations. In this thesis we investigate new methods for the analysis of epidemiological list data using capture-recapture methods. In particular we compare two standard methods used to estimate the unknown total population size, and examine new methods which incorporate list mismatch errors and model-selection uncertainty into the process for the estimation of the unknown total population size and its associated confidence interval. We study the use of modified tag loss methods from animal abundance estimation to allow for list mismatch errors in the epidemio-logical list data. We also explore the use of a weighted average method, the use of Bootstrap methods, and the use of a Bayesian model averaging method for incorporating model-selection uncertainty into the estimate of the unknown total population size and its associated confidence interval. In addition we use two previously unanalysed Diabetes studies to illustrate the methods examined and a well-known Spina Bifida Study for simulation purposes. This thesis finds that ignoring list mismatch errors will lead to biased estimates of the unknown total population size and that the list mismatch methods considered here result in a useful adjustment. The adjustment also approximately agrees with the results obtained using a complex matching algorithm. As for the incorporation of model-selection uncertainty, we find that confidence intervals which incorporate model-selection uncertainty are wider and more appropriate than confidence intervals that do not. Hence we recommend the use of tag loss methods to adjust for list mismatch errors and the use of methods that incorporate model-selection uncertainty into both point and interval estimates of the unknown total population size. / Subscription resource available via Digital Dissertations only.

Capture-recapture methods take their origins from animal abundance estimation, where they were used to estimate the unknown size of the animal population under study. In the late 1940s and again in the late 1960s and early 1970s these same capture-recapture methods were modified and applied to epidemiological list data. Since then through their continued use, in particular in the 1990s, these methods have become popular for the estimation of the completeness of disease registries and for the estimation of the unknown total size of human disease populations. In this thesis we investigate new methods for the analysis of epidemiological list data using capture-recapture methods. In particular we compare two standard methods used to estimate the unknown total population size, and examine new methods which incorporate list mismatch errors and model-selection uncertainty into the process for the estimation of the unknown total population size and its associated confidence interval. We study the use of modified tag loss methods from animal abundance estimation to allow for list mismatch errors in the epidemio-logical list data. We also explore the use of a weighted average method, the use of Bootstrap methods, and the use of a Bayesian model averaging method for incorporating model-selection uncertainty into the estimate of the unknown total population size and its associated confidence interval. In addition we use two previously unanalysed Diabetes studies to illustrate the methods examined and a well-known Spina Bifida Study for simulation purposes. This thesis finds that ignoring list mismatch errors will lead to biased estimates of the unknown total population size and that the list mismatch methods considered here result in a useful adjustment. The adjustment also approximately agrees with the results obtained using a complex matching algorithm. As for the incorporation of model-selection uncertainty, we find that confidence intervals which incorporate model-selection uncertainty are wider and more appropriate than confidence intervals that do not. Hence we recommend the use of tag loss methods to adjust for list mismatch errors and the use of methods that incorporate model-selection uncertainty into both point and interval estimates of the unknown total population size. / Subscription resource available via Digital Dissertations only.

Capture-recapture methods take their origins from animal abundance estimation, where they were used to estimate the unknown size of the animal population under study. In the late 1940s and again in the late 1960s and early 1970s these same capture-recapture methods were modified and applied to epidemiological list data. Since then through their continued use, in particular in the 1990s, these methods have become popular for the estimation of the completeness of disease registries and for the estimation of the unknown total size of human disease populations. In this thesis we investigate new methods for the analysis of epidemiological list data using capture-recapture methods. In particular we compare two standard methods used to estimate the unknown total population size, and examine new methods which incorporate list mismatch errors and model-selection uncertainty into the process for the estimation of the unknown total population size and its associated confidence interval. We study the use of modified tag loss methods from animal abundance estimation to allow for list mismatch errors in the epidemio-logical list data. We also explore the use of a weighted average method, the use of Bootstrap methods, and the use of a Bayesian model averaging method for incorporating model-selection uncertainty into the estimate of the unknown total population size and its associated confidence interval. In addition we use two previously unanalysed Diabetes studies to illustrate the methods examined and a well-known Spina Bifida Study for simulation purposes. This thesis finds that ignoring list mismatch errors will lead to biased estimates of the unknown total population size and that the list mismatch methods considered here result in a useful adjustment. The adjustment also approximately agrees with the results obtained using a complex matching algorithm. As for the incorporation of model-selection uncertainty, we find that confidence intervals which incorporate model-selection uncertainty are wider and more appropriate than confidence intervals that do not. Hence we recommend the use of tag loss methods to adjust for list mismatch errors and the use of methods that incorporate model-selection uncertainty into both point and interval estimates of the unknown total population size. / Subscription resource available via Digital Dissertations only.

Master of Science / Department of Statistics / Gary L. Gadbury / Discerning the metabolic or enzymatic role of a particular gene product, in the absence of information indicating sequence homology to known gene products, is a difficult task. One approach is to compare the levels of metabolites in a wild-type organism to those in an organism with a mutation that causes loss of function of the gene. The goal of this project was to develop an approach to analyze metabolite data on wild-type and mutant organisms for the purpose of identifying the function of a mutated gene. To develop and test statistical approaches to analysis of metabolite data for identification of gene function, levels of 141 lipid metabolites were measured in leaves of wild-type Arabidopsis thaliana plants and in leaves of Arabidopsis thaliana plants with known mutations in genes involved in lipid metabolism. The mutations were primarily in fatty acid desaturases, which are enzymes that catalyze reactions in which double bonds are added to fatty acids. When these enzymes are mutated, leaf lipid composition is altered, and the altered levels of specific lipid metabolites can be detected by a mass spectrometry. A randomization P-Value and other metrics were calculated for all potential reactant product pairs, which included all lipid metabolite pairs. An algorithm was developed to combine these data and rank the results for each pair as to likelihood of being the actual reactant-product pair. This method was designed and tested on data collected on mutants in genes with known functions, fad2 (Okuley et al., 1994), fad3 (Arondel et al., 1992), fad4, fad5 (Mekhedov et al., 2000), fad6 (Falcone et al., 1994), and fad7 (Iba et al., 1993 and Gibson et al., 1994). Application of the method to three additional genes produced by random mutagenesis, sfd1, sfd2, and sfd3, indicated that the significant pairs for fad6 and sfd3 were similar. Consistent with this, genetic evidence has indicated that sfd3 is a mutation in the FAD6 gene. The methods provide a list of putative reactions for an enzyme encoded by an unknown mutant gene. The output lists for unknown genes and known genes can be compared to provide evidence for similar biochemical activities. However, the strength of the current method is that the list of candidate chemical reactions for an enzyme encoded by a mutant gene can be produced without data other than the metabolite profile of the wild-type and mutant organisms, i.e., known gene analysis is not a requirement to obtain the candidate reaction list.