Table of Contents Author Guidelines Submit a Manuscript
Journal of Probability and Statistics
Volume 2012, Article ID 485174, 16 pages
http://dx.doi.org/10.1155/2012/485174
Review Article

Mixed Modeling with Whole Genome Data

MRC Epidemiology Unit & Institute of Metabolic Science, Addrenbrooke's Hospital, Box 285, Hills Road, Cambridge CB2 0QQ, UK

Received 2 March 2012; Accepted 20 April 2012

Academic Editor: Yongzhao Shao

Copyright © 2012 Jing Hua Zhao and Jian'an Luan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. C. Thomas and W. J. Gauderman, “Gibbs sampling methods in genetics,” in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richard, and D. J. Spiegelhalter, Eds., pp. 419–440, Chapman & Hall/CRC, London, UK, 1996. View at Google Scholar
  2. D. C. Thomas, Statistical Methods in Genetic Epidemiology, University Press, Oxford, UK, 2004.
  3. J. Yang, B. Benyamin, B. P. McEvoy et al., “Common SNPs explain a large proportion of the heritability for human height,” Nature Genetics, vol. 42, no. 7, pp. 565–569, 2010. View at Publisher · View at Google Scholar
  4. R. A. Fisher, “The correlation between relatives on the supposition of mendelian inheritance,” Transactions of the Royal Society of Edinburgh, vol. 52, pp. 399–433, 1918. View at Google Scholar
  5. H. L. Allen, K. Estrada, G. Lettre et al., “Hundreds of variants clustered in genomic loci and biological pathways affect human height,” Nature, vol. 467, no. 7317, pp. 832–838, 2010. View at Google Scholar
  6. G. B. Ehret, P. B. Munroe, K. M. Rice et al., “Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk,” Nature, vol. 478, no. 7367, pp. 103–109, 2011. View at Google Scholar
  7. T. M. Teslovich, K. Musunuru, A. V. Smith et al., “Biological, clinical and population relevance of 95 loci for blood lipids,” Nature, vol. 466, no. 7307, pp. 707–713, 2010. View at Google Scholar
  8. E. K. Speliotes, C. J. Willer, S. I. Berndt et al., “Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index,” Nature Genetics, vol. 42, no. 11, pp. 937–948, 2010. View at Google Scholar
  9. R. Plomin, C. M. A. Haworth, and O. S. P. Davis, “Common disorders are quantitative traits,” Nature Reviews Genetics, vol. 10, no. 12, pp. 872–878, 2009. View at Publisher · View at Google Scholar
  10. C. E. McCulloch and S. R. Searle, Generalized, Linear, and Mixed Models, Wiley Series in Probability and Statistics, Wiley-Interscience, New York, NY, USA, 2001.
  11. SAS Institute, SAS/STAT 9.3 User's Guide, SAS Publishing, Cary, NC, USA, 2011.
  12. C. I. Amos, “Robust variance-components approach for assessing genetic linkage in pedigrees,” American Journal of Human Genetics, vol. 54, no. 3, pp. 535–543, 1994. View at Google Scholar
  13. J. Blangero, J. T. Williams, and L. Almasy, “Variance component methods for detecting complex trait loci,” Advances in Genetics, vol. 42, pp. 151–181, 2001. View at Google Scholar
  14. T. M. Frayling, N. J. Timpson, M. N. Weedon et al., “A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity,” Science, vol. 316, no. 5826, pp. 889–894, 2007. View at Publisher · View at Google Scholar
  15. N. E. Morton and C. J. MacLean, “Analysis of family resem blance. III. complex segregation of quantitative traits,” American Journal of Human Genetics, vol. 26, pp. 489–503, 1974. View at Google Scholar
  16. J. L. Hopper and J. D. Mathews, “Extensions to multivariate normal models for pedigree analysis,” Annals of Human Genetics, vol. 46, no. 4, pp. 373–383, 1982. View at Google Scholar
  17. K. Lange and M. Boehnke, “Extensions to pedigree analysis. IV. Covariance components models for multivariate traits,” American Journal of Medical Genetics, vol. 14, no. 3, pp. 513–524, 1983. View at Google Scholar
  18. S. J. Hasstedt, “A mixed-model likelihood approximation on large pedigrees,” Computers and Biomedical Research, vol. 15, no. 3, pp. 295–307, 1982. View at Publisher · View at Google Scholar
  19. M. P. Epstein, J. E. Hunter, E. G. Allen, S. L. Sherman, X. Lin, and M. Boehnke, “A variance-component framework for pedigree analysis of continuous and categorical outcomes,” Statistics in BioSciences, vol. 1, no. 2, pp. 181–198, 2009. View at Google Scholar
  20. A. M. Saxton, Ed., Genetic Analysis of Complex Traits Using SAS, SAS Publishing, 2004.
  21. A. I. Vazquez, D. M. Bates, G. J. M. Rosa, D. Gianola, and K. A. Weigel, “Technical note: an R package for fitting generalized linear mixed models in animal breeding,” Journal of Animal Science, vol. 88, no. 2, pp. 497–504, 2010. View at Publisher · View at Google Scholar
  22. V. S. Pankratz, M. de Andrade, and T. M. Therneau, “Random-effects cox proportional hazards model: general variance components methods for time-to-event data,” Genetic Epidemiology, vol. 28, no. 2, pp. 97–109, 2005. View at Google Scholar
  23. V. Ducrocq and G. Casella, “A bayesian analysis of mixed survival models,” Genetics Selection Evolution, vol. 28, no. 6, pp. 505–529, 1996. View at Google Scholar
  24. D. Sorensen and D. Gianola, Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics, Springer, New York, NY, USA, 2002.
  25. P. Waldmann, “Easy and flexible Bayesian inference of quantitative genetic parameters,” Evolution, vol. 63, no. 6, pp. 1640–1643, 2009. View at Publisher · View at Google Scholar
  26. P. R. Burton, K. J. Scurrah, M. D. Tobin, and L. J. Palmer, “Covariance components models for longitudinal family data,” International Journal of Epidemiology, vol. 34, no. 5, pp. 1063–1079, 2005. View at Publisher · View at Google Scholar
  27. J. M. Lachin, Biostatistical Methods: The Assessment of Relative Risks, Wiley Series in Probability and Statistics, John Wiley & Sons, Hoboken, NJ, USA, 2nd edition, 2011.
  28. A. Skrondal and S. Rabe-Hesketh, Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models, Interdisciplinary Statistics, Chapman & Hall/CRC, Boca Raton, Fla, USA, 2004. View at Publisher · View at Google Scholar
  29. J. Whitehead, “Fitting Cox's regression model to survival data using GLIM,” Journal of the Royal Statistical Society, vol. 29, no. 3, pp. 268–275, 1980. View at Publisher · View at Google Scholar
  30. G. Verbeke and G. Molenberghs, Linear Mixed Models for Longitudinal Data, Springer, New York, NY, USA, 2000.
  31. J. Pinheiro and D. M. Bates, Mixed Effects Models in S and S-PLUS, Springer, 2000.
  32. R. B. Bapat, Linear Algebra and Linear Models, Universitext, Springer, London, UK, 3rd edition, 2012. View at Publisher · View at Google Scholar
  33. P. J. Diggle, P. J. Heagerty, K.-Y. Liang, and S. L. Zeger, Analysis of Longitudinal Data, vol. 25, Oxford University Press, Oxford, UK, 2nd edition, 2002.
  34. J. P. Klein and M. L. Moeschberger, Survival Analysis-Techniques for Censored and Truncated Data, Springer, 2nd edition, 2003.
  35. R. D. Riley, P. C. Lambert, and G. Abo-Zaid, “Meta-analysis of individual participant data: rationale, conduct, and reporting,” British Medical Journal, vol. 340, p. c221, 2010. View at Publisher · View at Google Scholar
  36. J. H. Zhao, J. Luan, R. J. F. Loos, and N. Wareham, “On genotype-phenotype association using SAS,” in Proceedings of the 2nd International Conference on Computational Bioscience, pp. 428–433, Cambridge, Mass, USA, 2011. View at Publisher · View at Google Scholar
  37. T. D. Pigott, Advances in Meta-Analysis, Springer, 2012.
  38. J. Neyman and E. L. Scott, “Consistent estimates based on partially consistent observations,” Econometrica, vol. 16, pp. 1–32, 1948. View at Google Scholar
  39. P. Hall, J. S. Marron, and A. Neeman, “Geometric representation of high dimension, low sample size data,” Journal of the Royal Statistical Society, vol. 67, no. 3, pp. 427–444, 2005. View at Publisher · View at Google Scholar
  40. N. J. Schork, “Extended multipoint identity-by-descent analysis of human quantitative traits: efficiency, power, and modeling considerations,” American Journal of Human Genetics, vol. 53, no. 6, pp. 1306–1319, 1993. View at Google Scholar
  41. J. H. Zhao and Q. Tan, “Integrated analysis of genetic data with R,” Human Genomics, vol. 2, no. 4, pp. 258–265, 2006. View at Google Scholar
  42. L. Almasy, T. D. Dyer, J. M. Peralta et al., “Genetic Analysis Workshop 17 mini-exome simulation,” BMC Proceedings, vol. 5, article S2, supplement 9, Article ID S2, 2011. View at Publisher · View at Google Scholar
  43. J. Luan, B. Kerner, J. H. Zhao et al., “A multilevel linear mixed model of the association between candidate genes and weight and body mass index using the framingham longitudinal family data,” BMC Proceedings, vol. 3, article S115, supplement 7, 2009. View at Google Scholar
  44. S. Purcell, B. Neale, K. Todd-Brown et al., “PLINK: a tool set for whole-genome association and population-based linkage analyses,” American Journal of Human Genetics, vol. 81, no. 3, pp. 559–575, 2007. View at Publisher · View at Google Scholar
  45. A. Sanchez, J. Ocaña, and F. Utzet, “Sampling theory, estimation, and significance testing for Prevosti's estimate of genetic distance,” Biometrics, vol. 51, no. 4, pp. 1216–1235, 1995. View at Publisher · View at Google Scholar
  46. M. de Andrade, E. Atkinson, E. Lunde, C. I. Amos, and J. Chen, “Estimating genetic components of variance for quantitative traits in family studies using the multic,” Tech. Rep., Mayo Clinic, 2006. View at Google Scholar
  47. E. F. Vonesh and V. M. Chinchilli, Linear and Nonlinear Models for the Analysis of Repeated Measurements, vol. 154 of Statistics: Textbooks and Monographs, Marcel Dekker, NewYork, NY, USA, 1997.
  48. G. Yin, “Bayesian generalized method of moments,” Bayesian Analysis, vol. 4, no. 2, pp. 191–208, 2009. View at Google Scholar
  49. T. Moger, O. O. Aalen, K. Heimdal, and H. K. Gjessing, “Analysis of testicular cancer data using a frailty model with familial dependence,” Statistics in Medicine, vol. 23, no. 4, pp. 617–632, 2004. View at Publisher · View at Google Scholar
  50. O. O. Aalen, O. Borgan, and H. K. Gjessing, Survival and Event History Analysis: A Process Point of View, Statistics for Biology and Health, Springer, NewYork, NY, USA, 2008. View at Publisher · View at Google Scholar
  51. Y. Wang, C. Huang, Y. Fang, Q. Yang, and R. Li, “Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing,” Journal of the Royal Statistical Society, vol. 61, no. 1, pp. 1–24, 2012. View at Publisher · View at Google Scholar
  52. J. Yu, G. Pressoir, W. H. Briggs et al., “A unified mixed-model method for association mapping that accounts for multiple levels of relatedness,” Nature Genetics, vol. 38, no. 2, pp. 203–208, 2006. View at Google Scholar
  53. A. G. Day-Williams, J. Blangero, T. D. Dyer, K. Lange, and E. M. Sobel, “Linkage analysis without defined pedigrees,” Genetic Epidemiology, vol. 35, no. 5, pp. 360–370, 2011. View at Google Scholar
  54. L. Han and M. Abney, “Identity by descent estimation with dense genome-wide genotype data,” Genetic Epidemiology, vol. 35, no. 6, pp. 557–567, 2011. View at Publisher · View at Google Scholar
  55. J. R. Lupski, J. G. Reid, C. Gonzaga-Jauregui et al., “Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy,” The New England Journal of Medicine, vol. 362, no. 13, pp. 1181–1191, 2010. View at Publisher · View at Google Scholar
  56. R. J. F. Loos, C. M. Lindgren, S. Li et al., “Common variants near MC4R are associated with fat mass, weight and risk of obesity,” Nature Genetics, vol. 40, no. 6, pp. 768–775, 2008. View at Publisher · View at Google Scholar
  57. S. Bandyopadhyay, B. Ganguli, and A. Chatterjee, “A review of multivariate longitudinal data analysis,” Statistical Methods in Medical Research, vol. 20, no. 4, pp. 299–330, 2011. View at Publisher · View at Google Scholar
  58. B. C. Sutradhar, Dynamic Mixed Models for Familial Longitudinal Data, Springer Series in Statistics, Springer, NewYork, NY, USA, 2011. View at Publisher · View at Google Scholar
  59. J. M. Soler and J. Blangero, “Longitudinal familial analysis of blood pressure involving parametric (co)variance functions,” BMC Genetics, vol. 4, article S87, supplement 1, 2003. View at Google Scholar
  60. R. Hardy, A. K. Wills, A. Wong et al., “Life course variations in the associations between FTO and MC4R gene variants and body size,” Human Molecular Genetics, vol. 19, no. 3, pp. 545–552, 2010. View at Publisher · View at Google Scholar