Dissecting high-dimensional phenotypes with Bayesian sparse factor analysis of genetic covariance matrices

Genetics

Volumn 194, Issue 3, 2013, Pages 753-767

  Runcie, Daniel E ^a   Mukherjee, Sayan ^a  

^a DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; BAYES THEOREM; BAYESIAN GENETIC SPARSE FACTOR ANALYSIS; CONTROLLED STUDY; DROSOPHILA MELANOGASTER; FACTORIAL ANALYSIS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC COVARIANCE MATRIX; GENETIC MODEL; GENETIC TRAIT; GENETIC VARIABILITY; GENOTYPE; HERITABILITY; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; QUANTITATIVE GENETICS; SAMPLING ERROR; SIMULATION;

ANIMAL MODEL; BAYESIAN INFERENCE; FACTOR MODEL; G MATRIX; SPARSITY;

ANIMALS; BAYES THEOREM; BIOLOGICAL EVOLUTION; DROSOPHILA MELANOGASTER; GENE EXPRESSION REGULATION; MODELS, THEORETICAL; PHENOTYPE;

DROSOPHILA MELANOGASTER;

EID: 84879742168     PISSN: 00166731     EISSN: 19432631     Source Type: Journal    
DOI: 10.1534/genetics.113.151217     Document Type: Article

References (64)

1. Armagan, A., D. Dunson, and M. Clyde, 2011 Generalized beta mixtures of Gaussians, pp. 523-531 in Advances in Neural In-formation Processing Systems 24, edited by J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger Available at: http://books.nips.cc/nips24.html.
2. Ayroles, J. F., M. A. Carbone, E. A. Stone, K. W. Jordan, R. F. Lyman et al., 2009 Systems genetics of complex traits in Drosophila melanogaster. Nat. Genet. 41(3): 299-307.
3. Bhattacharya, A., and D. B. Dunson, 2011 Sparse Bayesian infinite factor models. Biometrika 98(2): 291-306.
4. Bickel, P. J., and E. Levina, 2008a Covariance regularization by thresholding. Ann. Stat. 36: 2577-2604.
5. Bickel, P. J., and E. Levina, 2008b Regularized estimation of large covariance matrices. Ann. Stat. 36: 199-227.
6. Blows, M. W., S. F. Chenoweth, and E. Hine, 2004 Orientation of the genetic variance-covariance matrix and the fitness surface for multiple male sexually selected traits. Am. Nat. 163(3): 329-340.
7. Cantor, R. M., K. Lange, and J. S. Sinsheimer, 2010 Prioritizing GWAS results: a review of statistical methods and recommen-dations for their application. Am. J. Hum. Genet. 86(1): 6-22.
8. Carvalho, C. M., J. Chang, J. E. Lucas, J. R. Nevins, Q. Wang et al., 2008 High-dimensional sparse factor modeling: applications in gene expression genomics.J.Am.Stat.Assoc.103(484):1438-1456.
9. Cheverud, J. M., 1996 Developmental integration and the evolu-tion of pleiotropy. Integr. Comp. Biol. 36(1): 44-50.
10. Davidson, E., and M. Levine, 2008 Properties of developmental gene regulatory networks. Proc. Natl. Acad. Sci. USA 105(51): 20063-20066.
11. Dawid, A. P., 1981 Some matrix-variate distribution theory: no-tational considerations and a Bayesian application. Biometrika 68(1): 265-274.
12. de la Cruz, O., X. Wen, B. Ke, M. Song, and D. L. Nicolae, 2010 Gene, region and pathway level analyses in whole-ge-nome studies. Genet. Epidemiol. 34: 222-231.
13. de Los Campos, G., and D. Gianola, 2007 Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation. Genet. Sel. Evol. 39(5): 481-494.
14. el Karoui, N., 2008 Operator norm consistent estimation of large dimensional sparse covariance matrices. Ann. Stat. 36: 2717-2756.
15. Engelhardt, B. E., and M. Stephens, 2010 Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis. PLoS Genet. 6(9): e1001117.
16. Fan, J., J. Lv, and L. Qi, 2011 Sparse high dimensional models in economics. Annu. Rev. Econom. 3: 291-317.
17. Gelman, A., 2006 Prior distributions for variance parameters in hierarchical models. Bayesian Anal. 1(3): 515-533.
18. Gibson, G., and B. Weir, 2005 The quantitative genetics of tran-scription. Trends Genet. 21(11): 616-623.
19. Goldstein, H., 2010 Multilevel Factor Analysis, Structural Equation and Mixture Models, pp. 189-200. Wiley, New York.
20. Hahn, P. R., C. M. Carvalho, and S. Mukherjee, 2013 Partial factor modeling: predictor-dependent shrinkage for linear regression. J. Am. Stat. Assoc. (in press).
21. Hansen, T. F., and D. Houle, 2008 Measuring and comparing evolvability and constraint in multivariate characters. J. Evol. Biol. 21(5): 1201-1219.
22. Hartl, D. L., and H. Jungen, 1979 Estimation of average fitness of populations of Drosophila melanogaster and the evolution of fitness in experimental populations. Evolution 33(1): 371-380.
23. Hastie, T., R. Tibshirani, and J. H. Friedman, 2003 The Elements of Statistical Learning. Springer, New York.
24. Hayes, J. F., and W. G. Hill, 1981 Modification of estimates of parameters in the construction of genetic selection indices ('bending'). Biometrics 37(3): 483-493.
25. Henderson, C. R., 1984 Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, Ontario, Canada.
26. Hine, E., and M. W. Blows, 2006 Determining the effective di-mensionality of the genetic variance-covariance matrix. Genetics 173: 1135-1144.
27. Houle, D., 2010 Colloquium papers: numbering the hairs on our heads: the shared challenge and promise of phenomics. Proc. Natl. Acad. Sci. USA 107(Suppl. 1): 1793-1799.
28. Huang, D.W., B.T. Sherman, and R.A. Lempicki, 2009a Bioinformatics enrichmenttools: paths towardthecomprehensive functional analysis of large gene lists. Nucleic Acids Res. 37(1): 1-13.
29. Huang, D. W., B. T. Sherman, and R. A. Lempicki, 2009b Systematic and integrative analysis of large gene lists using DAVID bioinfor-matics resources. Nat. Protoc. 4(1): 44-57.
30. Jaffrezic, F., M. S. White, R. Thompson, and P. M. Visscher, 2002 Contrasting models for lactation curve analysis. J. Dairy Sci. 85: 968-975.
31. Kirkpatrick, M., 2009 Patterns of quantitative genetic variation in multiple dimensions. Genetica 136(2): 271-284.
32. Kirkpatrick, M., and N. Heckman, 1989 A quantitative genetic model for growth, shape, reaction norms, and other infinite-di-mensional characters. J. Math. Biol. 27(4): 429-450.
33. Kirkpatrick, M., and K. Meyer, 2004 Direct estimation of genetic principal components: simplified analysis of complex pheno-types. Genetics 168: 2295-2306.
34. Knight, G. R., and A. Robertson 1957 Fitness as a measurable character in Drosophila. Genetics 42: 524.
35. Kruuk, L. E. B., 2004 Estimating genetic parameters in natural populations using the'animal model'. Philos. Trans. R. Soc. B 359(1446): 873-890.
36. Krzanowski, W. J., 1979 Between-groups comparison of principal components. J. Am. Stat. Assoc. 74(367): 703-707.
37. Lande, R., 1979 Quantitative genetic-analysis of multivariate evolu-tion, Applied tobrain-body size allometry.Evolution 33(1):402-416.
38. Lucas, J., C. Carvalho, Q. Wang, A. Bild, J. Nevins et al., 2006 Sparse statistical modelling in gene expression genomics, pp. 155-173 in Bayesian Inference for Gene Expression and Proteomics, edited by K.-A. Do, P. Muller, and M. Vannucci. Cambridge University Press, Cambridge, UK.
39. Lynch, M., and B. Walsh, 1998 Genetics and Analysis of Quantita-tive Traits, Ed. 1. Sinauer Associates, Sunderland, MA.
40. McGraw, E. A., Y. H. Ye, B. Foley, S. F. Chenoweth, M. Higgie et al., 2011 High-dimensional variance partitioning reveals the modular genetic basis of adaptive divergence in gene expression during re-productive character displacement. Evolution 65(11): 3126-3137.
41. McGuigan, K., and M. W. Blows, 2007 The phenotypic and genetic covariance structure of drosphilid wings. Evolution 61(4): 902-911.
42. Meyer, K., 2005 Advances in methodology for random regression analyses. Aust. J. Exp. Agric. 45: 847-858.
43. Meyer, K., 2009 Factor-analytic models for genotype · environ-ment type problems and structured covariance matrices. Genet. Sel. Evol. 41: 21.
44. Meyer, K., and M. Kirkpatrick, 2007 A note on bias in reduced rank estimates of covariance matrices. Proc. Assoc. Adv. Anim. Breed. Genet 17: 154-157.
45. Meyer, K., and M. Kirkpatrick, 2008 Perils of parsimony: proper-ties of reduced-rank estimates of genetic covariance matrices. Genetics 180: 1153-1166.
46. Meyer, K., and M. Kirkpatrick, 2010 Better estimates of genetic covariance matrices by "bending" using penalized maximum likelihood. Genetics 185(3): 1097-1110.
47. Mezey, J., and D. Houle, 2005 The dimensionality of genetic var-iation for wing shape in Drosophila melanogaster. Evolution 59 (5): 1027-1038.
48. Neal, R. M., 1996 Bayesian Learning for Neural Networks. Springer-Verlag, New York, Secaucus, NJ.
49. Parts, L., O. Stegle, J. Winn, and R. Durbin, 2011 Joint genetic analysis of gene expression data with inferred cellular pheno-types. PLoS Genet. 7(1): e1001276.
50. Park, T., and G. Casella, 2008 The Bayesian Lasso. J. Am. Stat. Assoc. 103: 681-686.
51. Pletcher, S. D., and C. J. Geyer, 1999 The genetic analysis of age-dependent traits: modeling the character process. Genetics 153: 825-835.
52. Poggio, T., and S. Smale, 2003 The mathematics of learning: dealing with data. Not. Am. Math. Soc. 50: 2003.
53. Rausher, M. D., 1992 The measurement of selection on quantita-tive traits-biases due to environmental covariances between traits and fitness. Evolution 46(3): 616-626.
54. Schluter, D., 1996 Adaptive radiation along genetic lines of least resistance. Evolution 50(5): 1766-1774.
55. Sorensen, D., and D. Gianola, 2010 Likelihood, Bayesian and MCMC Methods in Quantitative Genetics: Statistics for Biology and Health. Springer-Verlag, Berlin.
56. Stegle, O., L. Parts, R. Durbin, and J. Winn, 2010 A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLOS Comput. Biol. 6(5): e1000770.
57. Stone, E. A., and J. F. Ayroles, 2009 Modulated modularity clus-tering as an exploratory tool for functional genomic inference. PLoS Genet. 5(5): e1000479.
58. Tipping, M. E., 2001 Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1: 211-244.
59. van Dyk, D. A., and T. Park, 2011 Partially collapsed Gibbs sam-pling and path-adaptive Metropolis-Hastings in high-energy as-trophysics, pp. 383-397 in Handbook of Markov Chain Monte Carlo, edited by S. Brooks, A. Gelman, G. Jones, and X.-L. Meng. Chapman & Hall/CRC Handbooks of Modern Statistical Methods, New York, NY.
60. Wagner, G., and L. Altenberg, 1996 Perspective: complex adap-tations and the evolution of evolvability. Evolution 50: 967-976.
61. Walsh, B., and M. W. Blows, 2009 Abundant genetic variation plus strong selection = multivariate genetic constraints: a geo-metric view of adaptation. Annu. Rev. Ecol. Evol. Syst. 40: 41-59.
62. West, M., 2003 Bayesian factor regression models in the "large p, small n" paradigm, pp. 723-732 in Bayesian Statistics. Oxford University Press, Oxford.
63. Xiong, Q., N. Ancona, E. R. Hauser, S. Mukherjee, and T. S. Furey, 2012 Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 22: 386-397.
64. Zhou, X., and M. Stephens, 2012 Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44: 821-824.