title: Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis.
abstract: Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.
authors.affiliation: Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark.
authors.forename: Qihua
authors.initials: Q
authors.lastname: Tan
authors.affiliation: Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark.
authors.forename: Jacob V
authors.initials: JV
authors.lastname: B Hjelmborg
authors.affiliation: Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark.
authors.forename: Mads
authors.initials: M
authors.lastname: Thomassen
authors.affiliation: Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark.
authors.forename: Andreas Kryger
authors.initials: AK
authors.lastname: Jensen
authors.affiliation: Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark.
authors.forename: Lene
authors.initials: L
authors.lastname: Christiansen
authors.affiliation: Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark.
authors.forename: Kaare
authors.initials: K
authors.lastname: Christensen
authors.affiliation: MRC Epidemiology Unit and Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
authors.forename: Jing Hua
authors.initials: JH
authors.lastname: Zhao
authors.affiliation: Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark.
authors.forename: Torben A
authors.initials: TA
authors.lastname: Kruse