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Full-Text Articles in Genetics and Genomics
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 …
General Approach For Combining Diverse Rare Variant Association Tests Provides Improved Robustness Across A Wider Range Of Genetic Architectures, Brian Greco, Allison Hainline, Jaron Arbet, Kelsey Grinde, Alejandra Benitez, Nathan L. Tintle
General Approach For Combining Diverse Rare Variant Association Tests Provides Improved Robustness Across A Wider Range Of Genetic Architectures, Brian Greco, Allison Hainline, Jaron Arbet, Kelsey Grinde, Alejandra Benitez, Nathan L. Tintle
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The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we …
Assessing The Impact Of Non-Differential Genotyping Errors On Rare Variant Tests Of Association, Scott Powers, Shyam Gopalakrishnan, Nathan L. Tintle
Assessing The Impact Of Non-Differential Genotyping Errors On Rare Variant Tests Of Association, Scott Powers, Shyam Gopalakrishnan, Nathan L. Tintle
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Background/Aims: We aim to quantify the effect of non-differential genotyping errors on the power of rare variant tests and identify those situations when genotyping errors are most harmful. Methods: We simulated genotype and phenotype data for a range of sample sizes, minor allele frequencies, disease relative risks and numbers of rare variants. Genotype errors were then simulated using five different error models covering a wide range of error rates. Results: Even at very low error rates, misclassifying a common homozygote as a heterozygote translates into a substantial loss of power, a result that is exacerbated even further as the minor …