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Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates
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Nathan L Tintle
Veröffentlicht/Copyright:
5. Februar 2007
Although researchers use duplicate genotyped data to calculate an inconsistency rate, there is no power analysis to assess the value of the duplicate data. In this paper, we present a model in which the genotyping error rate is related to the inconsistency rate. We extend the g genotype by h phenotype chi-squared test to incorporate the duplicate genotyped data. When a subject is inconsistently genotyped (that is, has two observed genotypes), our procedure is to allocate 0.5 units to each of the two genotypes. We specify the multivariate analysis of variance (MANOVA) test comparing these extended counts. We provide freely available software for this test and also for a permutation test used on small samples. A simulation study shows that the asymptotic null distribution of the MANOVA test holds when the total number of subjects, N, is at least 300. We also document with a simulation study that the asymptotic distribution of this test under various alternative hypotheses is a satisfactory approximation to the simulated power. In all cases, the power of the MANOVA test using the duplicate genotyped data is greater than the power of the chi-squared test ignoring the duplicate data. Power increases ranged from 0.776% to 4.652% for 80% powered tests and 0.292% to 2.028% for 95% powered tests. Researchers now can compute the value of the duplicate genotyped data as part of the design of the study.
Keywords: genotype error; misclassification; test of association; case-control; re-genotype; inconsistency rate; duplicate genotype; whole genome association; genome wide association
Published Online: 2007-2-5
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Schlagwörter für diesen Artikel
genotype error;
misclassification;
test of association;
case-control;
re-genotype;
inconsistency rate;
duplicate genotype;
whole genome association;
genome wide association
Artikel in diesem Heft
- Article
- Accounting for Dependence in Similarity Data from DNA Fingerprinting
- Normalization of Dye Bias in Microarray Data Using the Mixture of Splines Model
- A Generalized Sidak-Holm Procedure and Control of Generalized Error Rates under Independence
- Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates
- Likelihood-Based Inference for Multi-Color Optical Mapping
- Sparse Logistic Regression with Lp Penalty for Biomarker Identification
- Super Learning: An Application to the Prediction of HIV-1 Drug Resistance
- Supervised Detection of Conserved Motifs in DNA Sequences with Cosmo
- Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
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- Case-Control Inference of Interaction between Genetic and Nongenetic Risk Factors under Assumptions on Their Distribution
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- Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge
- Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression
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- Approximating the Variance of the Conditional Probability of the State of a Hidden Markov Model
- Using Linear Mixed Models for Normalization of cDNA Microarrays
- Experimental Design for Two-Color Microarrays Applied in a Pre-Existing Split-Plot Experiment
- The Cyclohedron Test for Finding Periodic Genes in Time Course Expression Studies
- H-Tuple Approach to Evaluate Statistical Significance of Biological Sequence Comparison with Gaps
- Multiple Testing Issues in Discriminating Compound-Related Peaks and Chromatograms from High Frequency Noise, Spikes and Solvent-Based Noise in LC - MS Data Sets
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- Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference
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- A Comparison of Methods to Control Type I Errors in Microarray Studies
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- T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data
- Population Structure and Covariate Analysis Based on Pairwise Microsatellite Allele Matching Frequencies
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