Use of longitudinal data in genetic studies in the genome-wide association studies era: Summary of group 14

Genetic Epidemiology

Volumn 33, Issue SUPPL. 1, 2009, Pages

  Kerner, Berit ^a   North, Kari E ^b   Fallin, M Daniele ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b The University of North Carolina at Chapel Hill   (United States)

^c JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Growth modeling; Phenotype definition; Risk estimation; Sampling of controls; Trends

Indexed keywords

CARDIOVASCULAR RISK; CONFERENCE PAPER; CONTROLLED STUDY; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC HETEROGENEITY; GENETIC RISK; GENETIC VARIABILITY; GENOTYPE; HIGH RISK POPULATION; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; LONGITUDINAL STUDY; METABOLIC DISORDER; PHENOTYPE; QUANTITATIVE TRAIT; SINGLE NUCLEOTIDE POLYMORPHISM;

CARDIOVASCULAR DISEASES; CROSS-SECTIONAL STUDIES; FEMALE; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; LINEAR MODELS; LOGISTIC MODELS; MALE; MOLECULAR EPIDEMIOLOGY; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; PROPORTIONAL HAZARDS MODELS; RISK FACTORS; SUPERIOR SAGITTAL SINUS;

EID: 71249152405     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.20479     Document Type: Conference Paper

References (17)

1. Chang S-W, Choi SH, Li K, Saint Fleur R, Huang C, Shen T, Ahn K, Gordon D, Kim W, Wu R, Mendell NR, Finch SJ. 2009. Growth mixture modeling as an exploratory analysis tool in longitudinal quantitative trait loci analysis. BMC Proc 3:S112.
2. Cupples LA, Arruda HT, Benjamin EJ, D'Agostino Sr RB, Demissie S, DeStefano AL, Dupuis J, Falls KM, Fox CS, Gottlieb DJ, Govindaraju DR, Guo CY, Heard-Costa NL, Hwang SJ, Kathiresan S, Kiel DP, Laramie JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey M, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf PA, Yang Q, Atwood LD. 2007. The Framingham Heart Study 100K SNP genome-wide association study resource: Overview of 17 phenotype working group reports. BMC Med Genet 8:S1. (Pubitemid 351935124)
3. Duncan TE, Duncan SC, Strycker LA, Li F, Alpert A. 1999. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah: Lawrence Erlbaum.
4. Fradin DD, Fallin MD. 2009. Influence of control selection in genome-wide association studies: The example of diabetes in the Framingham Heart Study. BMC Proc 3:S113.
5. Gauderman WJ, Macgregor S, Briollais L, Scurrah K, Tobin M, Park T, Wang D, Rao S, John S, Bull S. 2003. Longitudinal data analysis in pedigree studies. Genet Epidemiol 25:S18-S28. (Pubitemid 37490331)
6. Kerner B, Muthén BO. 2009. Growth mixture modeling in families of the Framingham Heart Study. BMC Proc 3:S114.
7. Luan J, Kerner B, Zhao JH, Loos RJF, Sharp SJ, Muthén BO, Wareham NJ. 2009. A multilevel linear mixed model of the association between candidate genes and weight and body mass index using the Framingham longitudinal family data. BMC Proc 3:S115.
8. McLean RA, Sanders WL, Stroup WW. 1991. A unified approach to mixed linear models. Am Stat 45:54-64.
9. Muthén B. 2004. Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In: Kaplan EL, editor. Handbook of quantitative methodology for the social sciences. Newbury Park: Sage Publications. p 345-368.
10. Muthén B, Asparouhov T. 2008. Growth mixture modeling: Analysis with non-Gaussian random effects. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, editors. Longitudinal Data Analysis. Boca Raton: Chapman & Hall/CRC Press. p 143-165.
11. Park YM, Province MA, Gao X, Feitosa M, Wu J, Ma D, Rao DC, Kraja AT. 2009. Longitudinal trends in the association of metabolic syndrome with 550 k single-nucleotide polymorphisms in the Framingham Heart Study. BMC Proc 3:S116.
12. Roslin NM, Hamid JS, Paterson AD, Beyene J. 2009. Genome-wide association analysis of cardiovascular-related quantitative traits in the Framingham Heart Study. BMC Proc 3:S117.
13. Yan Y, Hu Y, North KE, Franceschini N, Lin D. 2009. Evaluation of population impact of candidate polymorphisms for coronary heart disease in the Framingham Heart Study Offspring Cohort. BMC Proc 3:S118.
14. Zeger SL, Liang KY. 1986. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42:121-130.
15. Zhang HP. 1997. Multivariate adaptive splines for analysis of longitudinal data. J Comput Graph Stat 6:74-91. (Pubitemid 127054885)
16. Zhang HP. 2004. Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data. Stat Methods Med Res 13:63-82. (Pubitemid 38094367)
17. Zhu W, Cho K, Chen X, Zhang M, Wang M, Zhang H. 2009. A genome-wide association analysis of Framingham Heart Study longitudinal data using multivariate adaptive splines. BMC Proc 3:S119.