{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["French B"],"funding":["NHLBI NIH HHS"],"pagination":["Article 4"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3395231"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(3)"],"pubmed_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."],"journal":["Statistical applications in genetics and molecular biology"],"pubmed_title":["Non-iterative, regression-based estimation of haplotype associations with censored survival outcomes."],"pmcid":["PMC3395231"],"funding_grant_id":["R01 HL088577"],"pubmed_authors":["Mitra N","French B","Lumley T","Cappola TP"],"additional_accession":[]},"is_claimable":false,"name":"Non-iterative, regression-based estimation of haplotype associations with censored survival outcomes.","description":"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.","dates":{"release":"2012-01-01T00:00:00Z","publication":"2012 Feb","modification":"2024-12-04T02:21:09.491Z","creation":"2019-03-27T00:55:31Z"},"accession":"S-EPMC3395231","cross_references":{"pubmed":["22499703"],"doi":["10.1515/1544-6115.1764"]}}