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Full-Text Articles in Genetics and Genomics
Improvements To Bayesian Gene Activity State Estimation From Genome-Wide Transcriptomics Data, Craig Disselkoen, Nathan Hekman, Brian Gilbert, Sydney Benson, Matthew Anderson, Matthew Dejongh, Aaron Best, Nathan L. Tintle
Improvements To Bayesian Gene Activity State Estimation From Genome-Wide Transcriptomics Data, Craig Disselkoen, Nathan Hekman, Brian Gilbert, Sydney Benson, Matthew Anderson, Matthew Dejongh, Aaron Best, Nathan L. Tintle
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An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with …
Illustrating, Quantifying, And Correcting For Bias In Post-Hoc Analysis Of Gene-Based Rare Variant Tests Of Association, Kelsey E. Grinde, Jaron Arbet, Alden Green, Michael O'Connell, Alessandra Valcarcel, Jason Westra, Nathan L. Tintle
Illustrating, Quantifying, And Correcting For Bias In Post-Hoc Analysis Of Gene-Based Rare Variant Tests Of Association, Kelsey E. Grinde, Jaron Arbet, Alden Green, Michael O'Connell, Alessandra Valcarcel, Jason Westra, Nathan L. Tintle
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To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias …
Powerful Method For Including Genotype Uncertainty In Tests Of Hardy-Weinberg Equilibrium, Andrew Beck, Alexander Luedtke, Keli Liu, Nathan L. Tintle
Powerful Method For Including Genotype Uncertainty In Tests Of Hardy-Weinberg Equilibrium, Andrew Beck, Alexander Luedtke, Keli Liu, Nathan L. Tintle
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The use of posterior probabilities to summarize genotype uncertainty is pervasive across genotype, sequencing and imputation platforms. Prior work in many contexts has shown the utility of incorporating genotype uncertainty (posterior probabilities) in downstream statistical tests. Typical approaches to incorporating genotype uncertainty when testing Hardy-Weinberg equilibrium tend to lack calibration in the type I error rate, especially as genotype uncertainty increases. We propose a new approach in the spirit of genomic control that properly calibrates the type I error rate, while yielding improved power to detect deviations from Hardy-Weinberg Equilibrium. We demonstrate the improved performance of our method on both …
Improved Performance Of Gene Set Analysis On Genome-Wide Transcriptomics Data When Using Gene Activity State Estimates, Thomas Kamp, Micah Adams, Craig Disselkoen, Nathan L. Tintle
Improved Performance Of Gene Set Analysis On Genome-Wide Transcriptomics Data When Using Gene Activity State Estimates, Thomas Kamp, Micah Adams, Craig Disselkoen, Nathan L. Tintle
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Gene set analysis methods continue to be a popular and powerful method of evaluating genome-wide transcriptomics data. These approach require a priori grouping of genes into biologically meaningful sets, and then conducting downstream analyses at the set (instead of gene) level of analysis. Gene set analysis methods have been shown to yield more powerful statistical conclusions than single-gene analyses due to both reduced multiple testing penalties and potentially larger observed effects due to the aggregation of effects across multiple genes in the set. Traditionally, gene set analysis methods have been applied directly to normalized, log-transformed, transcriptomics data. Recently, efforts have …