GENOVA: Gene Overlap Analysis of GWAS Results
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Clara S. Tang
In many published genome-wide association studies (GWAS), the top few strongly associated variants are often located in or near known genes. This observation raises the more general hypothesis that variants nominally associated with a phenotype are more likely to overlap genes than those not associated with a phenotype. We developed a simple approach – named GENe OVerlap Analysis (GENOVA) – to formally test this hypothesis. This approach includes two steps. First, we define largely independent groups of highly correlated SNPs (or “clumps”) and classify each clump as intersecting a gene or not. Second, we determine how strongly associated each clump is with the phenotype and use logistic regression to formally test the hypothesis that clumps associated with the phenotype are more likely to intersect genes. Simulations suggest that the power of GENOVA is affected by at least three factors: GWAS sample size, the gene boundaries used to define gene-intersecting clumps and the P-value threshold used to define phenotype-associated clumps. We applied GENOVA to results from three recent GWAS meta-analyses of height, body mass index (BMI) and waist-hip ratio (WHR) conducted by the GIANT consortium. SNPs associated with variation in height were 1.44-fold more likely to be in or near genes than SNPs not associated with height (P = 5x10-28). A weaker association was observed for BMI (1.09-fold, P = 0.008) and WHR (1.09-fold, P = 0.014). GENOVA is implemented in C++ and is freely available at https://genepi.qimr.edu.au/staff/manuelF/genova/main.html.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
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- Exploring Multicollinearity Using a Random Matrix Theory Approach
- The Beta-Binomial SGoF method for multiple dependent tests
- Detecting Sample Misidentifications in Genetic Association Studies
- Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis
- Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies
- The practical effect of batch on genomic prediction
- Normalization, bias correction, and peak calling for ChIP-seq
- Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model
- Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals
- Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set
- Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes
- Graph Selection with GGMselect
- Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry
- A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification
- Software Communication
- GENOVA: Gene Overlap Analysis of GWAS Results
Artikel in diesem Heft
- Article
- Exploring Multicollinearity Using a Random Matrix Theory Approach
- The Beta-Binomial SGoF method for multiple dependent tests
- Detecting Sample Misidentifications in Genetic Association Studies
- Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis
- Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies
- The practical effect of batch on genomic prediction
- Normalization, bias correction, and peak calling for ChIP-seq
- Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model
- Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals
- Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set
- Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes
- Graph Selection with GGMselect
- Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry
- A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification
- Software Communication
- GENOVA: Gene Overlap Analysis of GWAS Results
