Physical Sciences and Mathematics | Open Access Articles

General Approaches For Combining Multiple Rare Variant Associate Tests Provide Improved Power Across A Wider Range Of Genetic Architecture, Nathan L. Tintle, Brian Greco, Allison Hainline, Keli Liu, Jaron Arbet, Alejandra Benitez, Kelsey Grinde

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In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by …

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Evaluating The Concordance Between Sequencing, Imputation And Microarray Genotype Calls In The Gaw18 Data, Ally Rogers, Andrew Beck, Nathan L. Tintle

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Genotype errors are well known to increase type I errors and/or decrease power in related tests of genotypephenotype association, depending on whether the genotype error mechanism is associated with the phenotype. These relationships hold for both single and multimarker tests of genotype-phenotype association. To assess the potential for genotype errors in Genetic Analysis Workshop 18 (GAW18) data, where no gold standard genotype calls are available, we explored concordance rates between sequencing, imputation, and microarray genotype calls. Our analysis shows that missing data rates for sequenced individuals are high and that there is a modest amount of called genotype discordance between …

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Genetic Analysis Workshop 18: Methods And Strategies For Analyzing Human Sequence And Phenotype Data In Members Of Extended Pedigrees, Heike Bickeboller, Julia N. Bailey, Joseph Beyene, Rita M. Cantor, Heather J. Cordell, Robert C. Culverhouse, Corinne D. Engelman, David W. Fardo, Saurabh Ghosh, Inke R. Konig, Justo Lorenzo Bermejo, Phillip E. Melton, Stephanie A. Santorico, Glen A. Satten, Lei Sun, Nathan L. Tintle, Andreas Ziegler, Jean W. Maccluer, Laura Almasy

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Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted …

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Full-Text Articles in Physical Sciences and Mathematics

General Approaches For Combining Multiple Rare Variant Associate Tests Provide Improved Power Across A Wider Range Of Genetic Architecture, Nathan L. Tintle, Brian Greco, Allison Hainline, Keli Liu, Jaron Arbet, Alejandra Benitez, Kelsey Grinde

Faculty Work Comprehensive List

Evaluating The Concordance Between Sequencing, Imputation And Microarray Genotype Calls In The Gaw18 Data, Ally Rogers, Andrew Beck, Nathan L. Tintle

Faculty Work Comprehensive List

Faculty Work Comprehensive List