Functional Mapping of Expression Quantitative Trait Loci that Regulate Oscillatory Gene Expression

Methods in Molecular Biology

Volumn 734, Issue , 2011, Pages 241-255

  Berg, Arthur ^b   Li, Ning ^b   Tong, Chunfa ^b   Wang, Zhong ^b   Berceli, Scott A ^b   Wu, Rongling ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NONE

Author keywords

Akaike information criterion; Bayesian information criterion; Cell cycle; Fourier series; Quantitative trait locus; Single nucleotide polymorphism

Indexed keywords

CELL CYCLE; COVARIANCE; EXPECTATION-MAXIMIZATION ALGORITHM; EXPRESSION QUANTITATIVE TRAIT LOCUS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENOTYPE; KINETICS; OSCILLATION; PROTEIN EXPRESSION; QUANTITATIVE TRAIT LOCUS; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; CHROMOSOME MAP; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; GENETICS; HAPLOTYPE; METHODOLOGY; SAMPLE SIZE;

ALGORITHMS; ANIMALS; CHROMOSOME MAPPING; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; HAPLOTYPES; MODELS, GENETIC; QUANTITATIVE TRAIT LOCI; SAMPLE SIZE;

EID: 79960151267     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-61779-086-7_12     Document Type: Chapter

References (30)

1. Brem, R., Yvert, G., Clinton, R., and Kruglyak, L. (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755.
2. Cheung, V. and Spielman, R. (2002) The genetics of variation in gene expression. Nature Genetics 32, 522–525.
3. Cheung, V. and Spielman, R. (2009) Genetics of human gene expression: mapping DNA variants that influence gene expression. Nature Reviews Genetics 10, 595–604.
4. Cheung, V., Conlin, L., Weber, T., Arcaro, M., Jen, K., Morley, M., and Spielman, R. (2003) Natural variation in human gene expression assessed in lymphoblastoid cells. Nature Genetics 33, 422–425.
5. Cookson, W., Liang, L., Abecasis, G., Moffatt, M., and Lathrop, M. (2009) Mapping complex disease traits with global gene expression. Nature Reviews Genetics 10, 184–194.
6. Schadt, E., Monks, S., Drake, T., Lusis, A., Che, N., Colinayo, V., Ruff, T., Milligan, S., Lamb, J., Cavet, G., et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302.
7. Jansen, R. and Nap, J. (2001) Genetical genomics: the added value from segregation. Trends in Genetics 17, 388–391.
8. Goldbeter, A. (2002) Computational approaches to cellular rhythms. Nature 420, 238–245.
9. Rustici, G., Mata, J., Kivinen, K., Lió, P., Penkett, C., Burns, G., Hayles, J., Brazma, A., Nurse, P., and Bahler, J. (2004) Periodic gene expression program of the fission yeast cell cycle. Nature Genetics 36,809–817.
10. Swinburne, I., Miguez, D., Landgraf, D., and Silver, P. (2008) Intron length increases oscillatory periods of gene expression in animal cells. Genes & Development 22, 2342–2346.
11. Holter, N., Maritan, A., Cieplak, M., Fedoroff, N., and Banavar, J. (2001) Dynamic modeling of gene expression data. Proceedings of the National Academy of Sciences of the United States of America 98, 1693–1698.
12. Qian, J., Dolled-Filhart, M., Lin, J., Yu, H., and Gerstein, M. (2001) Beyond synexpression relationships: local clustering of timeshifted and inverted gene expression profiles identifies new, biologically relevant interactions. Journal of Molecular Biology 314, 1053–1066.
13. Bar-Joseph, Z., Gerber, G., Gifford, D., Jaakkola, T., and Simon, I. (2003) Continuous representations of time-series gene expression data. Journal of Computational Biology 10, 341–356.
14. Luan, Y. and Li, H. (2003) Clustering of timecourse gene expression data using a mixedeffects model with B-splines. Bioinformatics 19,474–482.
15. Park, T., Yi, S., Lee, S., Lee, S., Yoo, D., Ahn, J., and Lee, Y. (2003) Statistical tests for identifying differentially expressed genes in timecourse microarray experiments. Bioinformatics 19,694–703.
16. Wakefield, J., Zhou, C., and Self, S. (2003) Modelling gene expression over time: curve clustering with informative prior distributions. Bayesian Statistics 7, 721–732.
17. Ernst, J., Nau, G., and Bar-Joseph, Z. (2005) Clustering short time series gene expression data. Bioinformatics 21, 159–168.
18. Storey, J., Xiao, W., Leek, J., Tompkins, R., and Davis, R. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 102, 12837–12842.
19. Ma, P., Castillo-Davis, C., Zhong, W., and Liu, J. (2006) A data-driven clustering method for time course gene expression data. Nucleic Acids Research 34, 1261–1269.
20. Ng, S., McLachlan, G., Wang, K., Ben Tovim Jones, L., and Ng, S. (2006) A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22, 1745–1752.
21. Inoue, L., Neira, M., Nelson, C., Gleave, M., and Etzioni, R. (2007) Cluster-based network model for time-course gene expression data. Biostatistics 8, 507–525.
22. Kim, B., Zhang, L., Berg, A., Fan, J., and Wu, R. (2008) A Computational Approach to the Functional Clustering of Periodic Gene Expression Profiles. Genetics 180, 821–834.
23. Wang, Z. and Wu, R. (2004) A statistical model for high-resolution mapping of quantitative trait loci determining human HIV-1 dynamics. Statistics in Medicine 23, 3033–3051.
24. Ma, C., Casella, G., and Wu, R. (2002) Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. Genetics 161, 1751–1762.
25. Li, N., McMurry, T., Berg, A., Zhong, W., Berceli, S., and Wu. (2010 (accepted)) Functional clustering of periodic transcriptional profiles through arma(p,q). PloS ONE 5, e9894.
26. Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., and Futcher, B. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular biology of the cell 9, 3273–3297.
27. Brockwell, P. and Davis, R. (1991) Time series: theory and methods. (Springer).
28. Haddad, J. (2004) On the closed form of the covariance matrix and its inverse of the causal ARMA process. Journal of Time Series Analysis 25, 443–448.
29. Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.
30. Schwarz, G. (1978) Estimating the dimension of a model. The Annals of Statistics 6, 461–464.