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Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies
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Jeremie J Lebrec
Veröffentlicht/Copyright:
13. Januar 2010
In Genomewide association (GWA) studies investigating thousands of SNPs, large sample sizes are needed to obtain a reasonable power after correction for multiple testing. To obtain the necessary sample sizes, data from different populations/cohorts are combined. The problem of pooling evidence across cohorts bears some resemblance with meta-analysis of clinical trials, and in fact classical meta-analytic methodologies from that field are typically used in GWAs. However, in genetics, it can be expected that the cohorts show some amount of heterogeneity in the association measures that are used for significance testing. In this paper, we demonstrate how it is possible to exploit this heterogeneity to improve our ability to detect influential genetic variants. We also discuss how pathway analysis based on summary data can help resolve heterogeneity. The current standard method for testing SNPs across cohorts in GWAs will miss heterogeneous but important genetic variants affecting complex diseases. Our new testing strategy has the potential to detect them while maintaining sensitivity to variants with homogeneous effects.
Keywords: complex disease; fixed effects model; gene; global test; GWA; meta-analysis; pathway; random effects model
Published Online: 2010-1-13
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Predicting Patient Survival from Longitudinal Gene Expression
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Schlagwörter für diesen Artikel
complex disease;
fixed effects model;
gene;
global test;
GWA;
meta-analysis;
pathway;
random effects model
Artikel in diesem Heft
- Article
- Epistatic Interactions
- Testing for Gene-Gene Interaction with AMMI Models
- A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data
- Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach
- Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium
- Optimisation of HMM Topologies Enhances DNA and Protein Sequence Modelling
- The Apportionment of Total Genetic Variation by Categorical Analysis of Variance
- Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies
- An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
- Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment
- Asymptotic Distribution of the "Orthogonal" Quantitative Transmission Disequilibrium Test in a Structured Population: Exact Formula
- Comparing Spatial Maps of Human Population-Genetic Variation Using Procrustes Analysis
- An Internal Calibration Method for Protein-Array Studies
- Weighted-LASSO for Structured Network Inference from Time Course Data
- Trilocus Disequilibrium Analysis of Multiallelic Markers in Outcrossing Populations
- Sparse Partial Least Squares Classification for High Dimensional Data
- Reconstructability Analysis as a Tool for Identifying Gene-Gene Interactions in Studies of Human Diseases
- Sub-Modular Resolution Analysis by Network Mixture Models
- Space Oriented Rank-Based Data Integration
- The Generalized Odds Ratio as a Measure of Genetic Risk Effect in the Analysis and Meta-Analysis of Association Studies
- Network Enrichment Analysis in Complex Experiments
- Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression
- Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
- A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number
- Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression
- Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA
- Confidently Estimating the Number of DNA Replication Origins
- Generalizing Moving Averages for Tiling Arrays Using Combined P-Value Statistics
- Lasso Logistic Regression, GSoft and the Cyclic Coordinate Descent Algorithm: Application to Gene Expression Data
- Granger Causality Analysis of Human Cell-Cycle Gene Expression Profiles
- Mapping Quantitative Trait Loci in a Non-Equilibrium Population
- On the Optimal Design of Genetic Variant Discovery Studies
- On Optimal Selection of Summary Statistics for Approximate Bayesian Computation
- Assessment of LD Matrix Measures for the Analysis of Biological Pathway Association
- Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis
- The Detection of Blur in Affymetrix GeneChips
- Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model
- Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing
- Predicting Patient Survival from Longitudinal Gene Expression
- Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression
- Reader's Reaction
- An Alternative Model of Type A Dependence in a Gene Set of Correlated Genes
- Letter to the Editor
- Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn
