Semiparametric variance components models for genetic studies with longitudinal phenotypes

Biostatistics

Volumn 13, Issue 3, 2012, Pages 482-496

  Wang, Yuanjia ^a,b   Huang, Chiahui ^a,b  

^a Howard Hughes Medical Institute   (United States)

^b INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

Author keywords

Genome wide linkage study; Multivariate longitudinal data; Penalized splines; Quantitative trait locus

Indexed keywords

ADULT; ARTICLE; BIOLOGICAL MODEL; BLOOD PRESSURE; CHROMOSOME 17; CHROMOSOME MAP; COMPUTER SIMULATION; FAMILY; FEMALE; GENETICS; HUMAN; LONGITUDINAL STUDY; MALE; METHODOLOGY; MIDDLE AGED; PHENOTYPE; QUANTITATIVE TRAIT; QUANTITATIVE TRAIT LOCUS;

ADULT; BLOOD PRESSURE; CHROMOSOME MAPPING; CHROMOSOMES, HUMAN, PAIR 17; COMPUTER SIMULATION; FAMILY; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; MIDDLE AGED; MODELS, GENETIC; PHENOTYPE; QUANTITATIVE TRAIT LOCI; QUANTITATIVE TRAIT, HERITABLE;

EID: 84863604899     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxr027     Document Type: Article

References (34)

1. ALMASY, L. AND BLANGERO, J. (1998). American Journal of Human Genetics 62, 1198-1211.
2. AMOS, C. I. (1994). Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54, 535-543. (Pubitemid 24068235)
3. ASTON, J. A. D., CHIOU, J.-M. AND EVANS, J. P. (2010). Linguistic pitch analysis using functional principal component mixed effect models. Journal of the Royal Statistical Society Series C 59, 297-317.
4. ATKINSON, B. AND THERNEAU, T. (2009). kinship: mixed-effects Cox models, sparse matrices, and modeling data from large pedigrees. R package version 1.1.0-23. http://CRAN.R-project.org/package=kinship.
5. ATWOOD, L., HEARD-COSTA, N., CUPPLES, L., JAQUISH, C., WILSON, P. AND D'AGOSTINE, R. (2002). Genome-wide linkage analysis of body mass index across 28 years of the Framingham Heart Study. American Journal of Human Genetics 71, 1044-1050. (Pubitemid 35305224)
6. BALADANDAYUTHAPANI, V., MALLICK, B. K., HONG, M. Y., LUPTON, J. R., TURNER, N. D. AND CARROLL, R. J. (2008). Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics 64, 64-73. (Pubitemid 351316876)
7. BRUMBACK, B. AND RICE, J. (1998). Smoothing spline models for the analysis of nested and crossed samples of curves (with discussion). Journal of the American Statistical Association 93, 961-994.
8. CHEN, H. AND WANG, Y. (2011). A penalized spline approach to functional mixed effects model analysis. Biometrics (in press). PMC2948587. doi: 10.1111/j.1541-0420.2010.01524.x
9. CRAINICEANU, C. AND RUPPERT, D. (2004). Likelihood ratio tests in linear mixed models with one variance component. Journal of the Royal Statistical Society, Series B 65, 165-185. (Pubitemid 38203127)
10. CRAINICEANU, C., RUPPERT, D., CLAESKENS, G. AND WAND, P. (2005). Exact likelihood ratio tests for penalised splines. Biometrika 92, 91-103. (Pubitemid 40427394)
11. DAWBER, T. R., MEADORS, G. F. AND MOORE, F. E. J. (1951). Epidemiological approaches to heart disease: the Framingham Study. American Journal of Public Health and the Nation's Health 41, 279-286.
12. DEANDRADE, M., GÚEGUEN, G., VISVIKIS, S., SASS, S., SIEST, G. AND AMOS, C. I. (2002). Extension of variance components approach to incorporate temporal trends and longitudinal pedigree data analysis. Genetic Epidemiology 22, 221-232. (Pubitemid 34246662)
13. DI, C., CRAINICEANU, C. M., CAFFO, B. S. AND PUNJABI, N. M. (2009). Multilevel functional principal component analysis. Annals of Applied Statistics 3, 458-488.
14. DUTILLEUL, P. AND PINEL-ALLOUL, B. (1996). A doubly multivariate model for statistical analysis of spatiotemporal environmental data. Environmetrics 7, 551-566.
15. EILERS, P. AND MARX, B. (1996). Flexible smoothing with B-splines. Statistical Science 11, 89-121.
16. GREVEN, G., CRAINICEANU, C., CAFFO, B. AND REICH, D. (2010). Longitudinal functional principal component analysis. Electronic Journal of Statistics 4, 1022-1054.
17. GUO, W. (2002). Functional mixed effects models. Biometrics 58, 121-128. (Pubitemid 34194495)
18. KHOURY, M., BEATY, H. AND COHEN, B. (1993). Fundamentals of Genetic Epidemiology. New York: Oxford University Press.
19. LASKY-SU, J., LYON, H. N., EMILSSON, V. and others (2008). On the replication of genetic associations: timing can be everything!, American Journal of Human Genetics 82, 849-858.
20. LEDERMAN, M. M., CONNICK, E. and others (1998). Immunological responses associated with 12 weeks of combination antiretroviral therapy consisting of zidovudine, lamivudine and ritonavir: results of AIDS Clinical Trials Group Protocol 315. Journal of Infectious Diseases 178, 70-79. (Pubitemid 28328078)
21. LEVY, D., DESTEFANO, A. L., LARSON, M. G., O'DONNELL, C. J., LIFTON, R. P., GAVRAS, H., CUPPLES, L. A. AND MYERS, R. H. (2000). Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension 36, 477-483.
22. MEYER, K. (1998). Estimating covariance functions for longitudinal data using a random regression model. Genetics Selection Evolution 30, 221-240. (Pubitemid 128509015)
23. O'BRIEN, L. M. AND FITZMAURICE, G. M. (2005). Regression models for the analysis of longitudinal Gaussian data from multiple sources. Statistics in Medicine 24, 1725-1744. (Pubitemid 40754091)
24. O'SULLIVAN, F. (1986). A statistical perspective on ill-posed inverse problems. Statistical Science 1, 502-518.
25. RUPPERT, D., WAND, M. P. AND CARROLL, R. J. (2003). Semiparametric Regression. New York: Cambridge University Press.
26. SHI, G. AND RAO, D. C. (2008). Ignoring temporal trends in genetic effects substantially reduces power of quantitative trait linkage analysis. Genetic Epidemiology 32, 61-72.
27. STAICU, A., CRAINICEANU, C. AND CARROLL, C. (2010). Fast methods for spatially correlated multilevel functional data. Biostatistics 11, 177-194.
28. STRAUCH, J., GOLLA, A., WILCOX, M. A. AND BAUR, M. P. (2003). Genetic analysis of phenotypes derived from longitudinal data: presentation group 1 of Genetic Analysis Workshop 13. Genet Epidemiol 25 (suppl 1), S5-S17. (Pubitemid 37490330)
29. WANG, Y. (2011). Flexible estimation of covariance function by penalized spline with application to longitudinal family data. Statistics in Medicine (in press). doi:10.1002/sim.4236.
30. WU, H. AND ZHANG, J. (2006). Nonparametric Regression Methods for Longitudinal Data Analysis Mixed-Effects Modeling Approaches. New York: Wiley.
31. YANG, Q., CHAZARO, I., CUI, J., GUO, C. Y., DEMISSIE, S., LARSON, M., ATWOOD, D. L., CUPPLES, L. A. AND DESTEFANO, A. L. (2003). Genetic analysis of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach. BMC Genetics 4 (Suppl 1), S29.
32. ZHANG, H. AND ZHONG, X. (2006). Linkage analysis of longitudinal data and design consideration, BMC Genetics 7, 37.
33. ZHAO, W. AND WU, R. (2008). Wavelet-based nonparametric functional mapping of longitudinal curves. Journal of the American Statistical Association 103, 714-725.
34. ZHOU, L., HUANG, J. AND CARROLL, R. (2008). Joint modeling of paired sparse functional data using principal components. Biometrika 95, 601-619.