Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes
-
Benjamin French
The general availability of reliable and affordable genotyping technology has enabled genetic association studies to move beyond small case-control studies to large prospective studies. For prospective studies, genetic information can be integrated into the analysis via haplotypes, with focus on their association with a censored survival outcome. We develop non-iterative, regression-based methods to estimate associations between common haplotypes and a censored survival outcome in large cohort studies. Our non-iterative methods—weighted estimation and weighted haplotype combination—are both based on the Cox regression model, but differ in how the imputed haplotypes are integrated into the model. Our approaches enable haplotype imputation to be performed once as a simple data-processing step, and thus avoid implementation based on sophisticated algorithms that iterate between haplotype imputation and risk estimation. We show that non-iterative weighted estimation and weighted haplotype combination provide valid tests for genetic associations and reliable estimates of moderate associations between common haplotypes and a censored survival outcome, and are straightforward to implement in standard statistical software. We apply the methods to an analysis of HSPB7-CLCNKA haplotypes and risk of adverse outcomes in a prospective cohort study of outpatients with chronic heart failure.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- 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
Articles in the same Issue
- 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
