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Three Approaches to Modeling Gene‐Environment Interactions in Longitudinal Family Data: Gene‐Smoking Interactions in Blood Pressure

Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promis... Full description

Journal Title: Genetic Epidemiology January 2016, Vol.40(1), pp.73-80
Main Author: Basson, Jacob
Other Authors: Sung, Yun Ju , De Las Fuentes, Lisa , Schwander, Karen L. , Vazquez, Ana , Rao, Dabeeru C.
Format: Electronic Article Electronic Article
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ID: ISSN: 0741-0395 ; E-ISSN: 1098-2272 ; DOI: 10.1002/gepi.21941
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recordid: wj10.1002/gepi.21941
title: Three Approaches to Modeling Gene‐Environment Interactions in Longitudinal Family Data: Gene‐Smoking Interactions in Blood Pressure
format: Article
creator:
  • Basson, Jacob
  • Sung, Yun Ju
  • De Las Fuentes, Lisa
  • Schwander, Karen L.
  • Vazquez, Ana
  • Rao, Dabeeru C.
subjects:
  • Longitudinal
  • Family
  • Interactions
  • Gene‐Environment
  • Blood Pressure
ispartof: Genetic Epidemiology, January 2016, Vol.40(1), pp.73-80
description: Blood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene‐smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree‐based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single‐visit analyses. The pedigree‐based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene‐smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency
language:
source:
identifier: ISSN: 0741-0395 ; E-ISSN: 1098-2272 ; DOI: 10.1002/gepi.21941
fulltext: fulltext
issn:
  • 0741-0395
  • 07410395
  • 1098-2272
  • 10982272
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titleThree Approaches to Modeling Gene‐Environment Interactions in Longitudinal Family Data: Gene‐Smoking Interactions in Blood Pressure
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subjectLongitudinal ; Family ; Interactions ; Gene‐Environment ; Blood Pressure
descriptionBlood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene‐smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree‐based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single‐visit analyses. The pedigree‐based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene‐smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data.
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descriptionBlood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene‐smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree‐based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single‐visit analyses. The pedigree‐based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene‐smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data.
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abstractBlood pressure (BP) has been shown to be substantially heritable, yet identified genetic variants explain only a small fraction of the heritability. Gene‐smoking interactions have detected novel BP loci in cross‐sectional family data. Longitudinal family data are available and have additional promise to identify BP loci. However, this type of data presents unique analysis challenges. Although several methods for analyzing longitudinal family data are available, which method is the most appropriate and under what conditions has not been fully studied. Using data from three clinic visits from the Framingham Heart Study, we performed association analysis accounting for gene‐smoking interactions in BP at 31,203 markers on chromosome 22. We evaluated three different modeling frameworks: generalized estimating equations (GEE), hierarchical linear modeling, and pedigree‐based mixed modeling. The three models performed somewhat comparably, with multiple overlaps in the most strongly associated loci from each model. Loci with the greatest significance were more strongly supported in the longitudinal analyses than in any of the component single‐visit analyses. The pedigree‐based mixed model was more conservative, with less inflation in the variant main effect and greater deflation in the gene‐smoking interactions. The GEE, but not the other two models, resulted in substantial inflation in the tail of the distribution when variants with minor allele frequency <1% were included in the analysis. The choice of analysis method should depend on the model and the structure and complexity of the familial and longitudinal data.
doi10.1002/gepi.21941
pages73-80
date2016-01