Increasing Power for Tests of Genetic Association in the Presence of Phenotype and/or Genotype Error by Use of Double-Sampling
-
Derek Gordon
Phenotype and/or genotype misclassification can: significantly increase type II error probabilities for genetic case/control association, causing decrease in statistical power; and produce inaccurate estimates of population frequency parameters. We present a method, the likelihood ratio test allowing for errors (LRTae) that incorporates double-sample information for phenotypes and/or genotypes on a sub-sample of cases/controls. Population frequency parameters and misclassification probabilities are determined using a double-sample procedure as implemented in the Expectation-Maximization (EM) method. We perform null simulations assuming a SNP marker or a 4-allele (multi-allele) marker locus. To compare our method with the standard method that makes no adjustment for errors (LRTstd), we perform power simulations using a 2^k factorial design with high and low settings of: case/control samples, phenotype/genotype costs, double-sampled phenotypes/genotypes costs, phenotype/genotype error, and proportions of double-sampled individuals. All power simulations are performed fixing equal costs for the LRTstd and LRTae methods. We also consider case/control ApoE genotype data for an actual Alzheimer's study.The LRTae method maintains correct type I error proportions for all null simulations and all significance level thresholds (10%, 5%, 1%). LRTae average estimates of population frequencies and misclassification probabilities are equal to the true values, with variances of 10e-7 to 10e-8. For power simulations, the median power difference LRTae-LRTstd at the 5% significance level is 0.06 for multi-allele data and 0.01 for SNP data. For the ApoE data example, the LRTae and LRTstd p-values are 5.8 x 10e-5 and 1.6 x 10e-3, respectively. The increase in significance is due to adjustment in the LRTae for misclassification of the most commonly reported risk allele. We have developed freely available software that performs our LRTae statistic.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- Using Alpha Wisely: Improving Power to Detect Multiple QTL
- Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
- Asymptotic Optimality of Likelihood-Based Cross-Validation
- Using Importance Sampling to Improve Simulation in Linkage Analysis
- Model-Based Assignment and Inference of Protein Backbone Nuclear Magnetic Resonances
- Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?
- Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma
- Saturation and Quantization Reduction in Microarray Experiments using Two Scans at Different Sensitivities
- Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection
- Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates
- Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate
- Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives
- Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
- A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects
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- Classifying Gene Expression Profiles from Pairwise mRNA Comparisons
- Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
- A Mixed Model Approach to Identify Yeast Transcriptional Regulatory Motifs via Microarray Experiments
- Mammalian Genomes Ease Location of Human DNA Functional Segments but Not Their Description
- On the Dependence Structure of Sequence Alignment Scores Calculated with Multiple Scoring Matrices
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- A Method for Evaluating the Impact of Individual Haplotypes on Disease Incidence in Molecular Epidemiology Studies
- Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment
- MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data
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- Maximum Likelihood for Genome Phylogeny on Gene Content
- Confidence Levels for the Comparison of Microarray Experiments
- PLS Dimension Reduction for Classification with Microarray Data
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- Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays
- Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
- A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks
- Validation and Discovery in Markov Models of Genetics Data
- Making Sense of High-Throughput Protein-Protein Interaction Data
- Reader's Reaction
- Reader Reaction
- Response to Foulkes and De Gruttola
- Software Communication
- BayesMendel: an R Environment for Mendelian Risk Prediction
- Letter to the Editor
- Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors
