title: Kernel score statistic for dependent data.
abstract: 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 <1% minor allele frequency.
authors.affiliation: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
authors.forename: Dörthe
authors.initials: D
authors.lastname: Malzahn
authors.affiliation: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
authors.forename: Stefanie
authors.initials: S
authors.lastname: Friedrichs
authors.affiliation: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
authors.forename: Albert
authors.initials: A
authors.lastname: Rosenberger
authors.affiliation: Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
authors.forename: Heike
authors.initials: H
authors.lastname: Bickeböller