Kernel score statistic for dependent data

The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects....

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Bibliographic Details
Published in:BMC proceedings 2014-06, Vol.8 (Suppl 1), p.S41-S41, Article S41
Main Authors: Malzahn, Dörthe, Friedrichs, Stefanie, Rosenberger, Albert, Bickeböller, Heike
Format: Article
Language:English
Subjects:
Biomedical research
Blood pressure
Diabetes
Economic models
Estimates
Gallbladder
Genomes
Genomics
Grants
Hypotheses
Proceedings
Statistical analysis
Statistical methods
Studies
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Summary:The kernel score statistic is a global covariance component test over a set of genetic markers. It provides a flexible modeling framework and does not collapse marker information. We generalize the kernel score statistic to allow for familial dependencies and to adjust for random confounder effects. With this extension, we adjust our analysis of real and simulated baseline systolic blood pressure for polygenic familial background. We find that the kernel score test gains appreciably in power through the use of sequencing compared to tag-single-nucleotide polymorphisms for very rare single nucleotide polymorphisms with
ISSN:1753-6561
1753-6561
DOI:10.1186/1753-6561-8-S1-S41