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PubMed 22499703


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Automatically associated channels: ClvC4



Title: Non-iterative, regression-based estimation of haplotype associations with censored survival outcomes.

Authors: Benjamin French, Thomas Lumley, Thomas P Cappola, Nandita Mitra

Journal, date & volume: Stat Appl Genet Mol Biol, 2012 , 11, Article 4

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/22499703


Abstract
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.