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Statistical Applications in Genetics and Molecular Biology

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Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes

Benjamin French1 / Thomas Lumley2 / Thomas P. Cappola3 / Nandita Mitra4

1University of Pennsylvania

2University of Auckland

3University of Pennsylvania

4University of Pennsylvania

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 3, ISSN (Online) 1544-6115, DOI: 10.1515/1544-6115.1764, February 2012

Publication History

Published Online:
2012-02-15

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.

Keywords: Cox regression; phase ambiguity; prospective study; unphased genotypes

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