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Full-Text Articles in Bioinformatics
Assessing Methods For Assigning Snps To Genes In Gene-Based Tests Of Association Using Common Variants, Ashley Petersen, Carolina Alvarez, Scott Declaire, Nathan L. Tintle
Assessing Methods For Assigning Snps To Genes In Gene-Based Tests Of Association Using Common Variants, Ashley Petersen, Carolina Alvarez, Scott Declaire, Nathan L. Tintle
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Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal …
Geometric Framework For Evaluating Rare Variant Tests Of Association, Keli Liu, Shannon Fast, Matthew Zawistowski, Nathan L. Tintle
Geometric Framework For Evaluating Rare Variant Tests Of Association, Keli Liu, Shannon Fast, Matthew Zawistowski, Nathan L. Tintle
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The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare …
Optimal Methods For Using Posterior Probabilities In Association Testing, Keli Liu, Alexander Luedtke, Nathan L. Tintle
Optimal Methods For Using Posterior Probabilities In Association Testing, Keli Liu, Alexander Luedtke, Nathan L. Tintle
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Objective: The use of haplotypes to impute the genotypes of unmeasured single nucleotide variants continues to rise in popularity. Simulation results suggest that the use of the dosage as a one-dimensional summary statistic of imputation posterior probabilities may be optimal both in terms of statistical power and computational efficiency; however, little theoretical understanding is available to explain and unify these simulation results. In our analysis, we provide a theoretical foundation for the use of the dosage as a one-dimensional summary statistic of genotype posterior probabilities from any technology. Methods: We analytically evaluate the dosage, mode and the more general set …
Assessing The Impact Of Differential Genotyping Errors On Rare Variant Tests Of Association, Morgan Mayer-Jochimsen, Shannon Fast, Nathan L. Tintle
Assessing The Impact Of Differential Genotyping Errors On Rare Variant Tests Of Association, Morgan Mayer-Jochimsen, Shannon Fast, Nathan L. Tintle
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Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In …