Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis

Research output: Contribution to journalConference articleResearchpeer-review

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.

Original languageEnglish
Article numberS82
JournalB M C Proceedings
Volume8
Issue numberSupplement 1
Number of pages6
ISSN1753-6561
DOIs
Publication statusPublished - 2014
Event18th Genetic Analysis Workshop: Human sequence data in extended pedigrees - Stevenson, WA, United States
Duration: 13. Oct 201217. Oct 2012

Conference

Conference18th Genetic Analysis Workshop
CountryUnited States
CityStevenson, WA
Period13/10/201217/10/2012

Fingerprint

Pedigree
Blood pressure
Chromosomes
Data acquisition
Genes
Trajectories
Chromosomes, Human, Pair 3
Genome-Wide Association Study
Genetic Models
Economic Inflation
Genetic Association Studies
Cluster Analysis
Linear Models
Education

Cite this

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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.",
author = "Qihua Tan and {B Hjelmborg}, {Jacob V} and Mads Thomassen and Jensen, {Andreas Kryger} and Lene Christiansen and Kaare Christensen and Zhao, {Jing Hua} and Kruse, {Torben A}",
note = "Edited by H Bickeb{\"o}ller, JN Bailey, J Beyene, RM Cantor, HJ Cordell, RC Culverhouse, CD Engelman, DW Fardo, S Ghosh, IR K{\"o}nig, J Lorenzo Bermejo, PE Melton, SA Santorico, GA Satten, L Sun, NL Tintle, A Ziegler, JW MacCluer and L Almasy",
year = "2014",
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language = "English",
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journal = "B M C Proceedings",
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Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis. / Tan, Qihua; B Hjelmborg, Jacob V; Thomassen, Mads; Jensen, Andreas Kryger; Christiansen, Lene; Christensen, Kaare; Zhao, Jing Hua; Kruse, Torben A.

In: B M C Proceedings, Vol. 8, No. Supplement 1 , S82, 2014.

Research output: Contribution to journalConference articleResearchpeer-review

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T1 - Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis

AU - Tan, Qihua

AU - B Hjelmborg, Jacob V

AU - Thomassen, Mads

AU - Jensen, Andreas Kryger

AU - Christiansen, Lene

AU - Christensen, Kaare

AU - Zhao, Jing Hua

AU - Kruse, Torben A

N1 - Edited by H Bickeböller, JN Bailey, J Beyene, RM Cantor, HJ Cordell, RC Culverhouse, CD Engelman, DW Fardo, S Ghosh, IR König, J Lorenzo Bermejo, PE Melton, SA Santorico, GA Satten, L Sun, NL Tintle, A Ziegler, JW MacCluer and L Almasy

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N2 - 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.

AB - 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.

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