Abstract
A complex disease is usually characterized by a few relevant disease phenotypes which are dictated by complex genetical factors through different biological pathways. These pathways are very likely to overlap and interact with one another leading to a more intricate network. Identification of genes that are associated with these phenotypes will help understand the mechanism of the disease development in a comprehensive manner. However, no analytical model has been reported to deal with multiple phenotypes simultaneously in gene-phenotype association study. Typically, a phenotype is inquired at one time. The conclusion is then made simply by fusing the results from individual analysis based on single phenotype. We believe that the certain information among phenotypes may be lost by not analyzing the phenotypes jointly. In current study, we proposed to investigate the associations between expressed genes and multiple phenotypes with a single statistics model. The relationship between gene expression level and phenotypes is described by a multiple linear regression equation. Each regression coefficient, representing gene-phenotype(s) association strength, is assumed to be sampled from a mixture of two normal distributions. The two normal components are used to model the behaviors of phenotype(s)-relevant genes and phenotype(s)-irrelevant genes, respectively. The conclusive classification of coefficients determines the association status between genes and phenotypes. The new method is demonstrated by simulated study as well as a real data analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blalock, E.M., Geddes, J.W., Chen, K.C., Porter, N.M., Markesbery, W.R., Landfield, P.W.: Incipient alzheimer’s disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proceedings of the National Academy of Sciences of the United States of America 101, 2173–2178 (2004)
Lan, H., Chen, M., Flowers, J.B., Yandell, B.S., Stapleton, D.S., Mata, C.M., Mui, E.T., Flowers, M.T., Schueler, K.L., Manly, K.F., Williams, R.W., Kendziorski, C., Attie, A.D.: Combined expression trait correlations and expression quantitative trait locus mapping. Plos Genetics 2, e6 (2006)
van Bakel, H., Strengman, E., Wijmenga, C., Holstege, F.C.P.: Gene expression profiling and phenotype analyses of s. cerevisiae in response to changing copper reveals six genes with new roles in copper and iron metabolism. Physiol. Genomics 22, 356–367 (2005)
Jia, Z., Xu, S.: Clustering expressed genes on the basis of their association with a quantitative phenotype. Genetical Research 86, 193–207 (2005)
Qu, Y., Xu, S.H.: Quantitative trait associated microarray gene expression data analysis. Molecular Biology and Evolution 23, 1558–1573 (2006)
Jia, Z., Tang, S., Mercola, D., Xu, S.: Detection of quantitative trait associated genes using cluster analysis. In: Marchiori, E., Moore, J.H. (eds.) EvoBIO 2008. LNCS, vol. 4973, pp. 83–94. Springer, Heidelberg (2008)
Mitchell, T.J., Beauchamp, J.J.: Bayesian variable selection in linear regression. Journal of the American Statistical Association 83, 1023–1036 (1988)
George, E.I., Mcculloch, R.E.: Variable selection via gibbs sampling. Journal of the American Statistical Association 88, 881–889 (1993)
Raftery, A.E., Lewis, S.M.: One long run with diagnostics: Implementation strategies for markov chain monte carlo. Statistical Science 7, 493–497 (1992)
Jia, Z., Xu, S.: Mapping quantitative trait loci for expression abundance. Genetics 176, 611–623 (2007)
Ghazalpour, A., Doss, S., Zhang, B., Wang, S., Plaisier, C., Castellanos, R., Brozell, A., Schadt, E.E., Drake, T.A., Lusis, A.J., Horvath, S.: Integrating genetic and network analysis to characterize genes related to mouse weight. Plos Genetics 2, 1182–1192 (2006)
Lander, E.S., Botstein, D.: Mapping mendelian factors underlying quantitative traits using rflp linkage maps. Genetics 121, 185–199 (1989)
Jiang, C.J., Zeng, Z.B.: Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101, 47–58 (1997)
Xu, S.Z., Yi, N.J.: Mixed model analysis of quantitative trait loci. Proceedings of the National Academy of Sciences of the United States of America 97, 14542–14547 (2000)
Broman, K.W., Speed, T.R.: A model selection approach for the identification of quantitative trait loci in experimental crosses. Journal of the Royal Statistical Society Series B-Statistical Methodology 64, 641–656 (2002)
Yi, N.J., Xu, S.Z.: Mapping quantitative trait loci with epistatic effects. Genetical Research 79, 185–198 (2002)
Yi, N.J., Xu, S.Z., Allison, D.B.: Bayesian model choice and search strategies for mapping interacting quantitative trait loci. Genetics 165, 867–883 (2003)
Rzhetsky, A., Wajngurt, D., Park, N., Zheng, T.: Probing genetic overlap among complex human phenotypes. Proceedings of the National Academy of Sciences of the United States of America 104, 11694–11699 (2007)
Oti, M., Huynen, M.A., Brunner, H.G.: Phenome connections. Trends in Genetics 24, 103–106 (2008)
Banerjee, S., Yandell, B.S., Yi, N.: Bayesian quantitative trait loci mapping for multiple traits. Genetics 179, 2275–2289 (2008)
Schadt, E.E., Monks, S.A., Drake, T.A., Lusis, A.J., Che, N., Colinayo, V., Ruff, T.G., Milligan, S.B., Lamb, J.R., Cavet, G., Linsley, P.S., Mao, M., Stoughton, R.B., Friend, S.H.: Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)
Hubner, N., Wallace, C.A., Zimdahl, H., Petretto, E., Schulz, H., Maciver, F., Mueller, M., Hummel, O., Monti, J., Zidek, V., Musilova, A., Kren, V., Causton, H., Game, L., Born, G., Schmidt, S., Muller, A., Cook, S.A., Kurtz, T.W., Whittaker, J., Pravenec, M., Aitman, T.J.: Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genetics 37, 243–253 (2005)
Jansen, R.C., Nap, J.P.: Genetical genomics: the added value from segregation. Trends in Genetics 17, 388–391 (2001)
Kendziorski, C.M., Chen, M., Yuan, M., Lan, H., Attie, A.D.: Statistical methods for expression quantitative trait loci (eqtl) mapping. Biometrics 62(1), 19–27 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jia, Z. et al. (2009). Association Study between Gene Expression and Multiple Relevant Phenotypes with Cluster Analysis. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009. Lecture Notes in Computer Science, vol 5483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01184-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-01184-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01183-2
Online ISBN: 978-3-642-01184-9
eBook Packages: Computer ScienceComputer Science (R0)