Abstract
Many efforts have been involved in association study of quantitative phenotypes and expressed genes. The key issue is how to efficiently identify phenotype-associated genes using appropriate methods. The limitations for the existing approaches are discussed. We propose a hierarchical mixture model in which the relationship between gene expressions and phenotypic values is described using orthogonal polynomials. Gene specific coefficient, which reflects the strength of association, is assumed to be sampled from a mixture of two normal distributions. The association status for a gene is determined based on which distribution the gene specific coefficient is sampled from. The statistical inferences are made via the posterior mean drawn from a Markov Chain Monte Carlo sample. The new method outperforms the existing methods in simulated study as well as the analysis of a mice data generated for obesity research.
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References
Dudoit, S., Yang, Y.H., Callow, M.J., Speed, T.P.: Statistical methods for identifying differentially expressed genes in replicated cdna microarray experiments. Stat. Sinic. 12, 111–139 (2002)
Saban, M.R., Hellmich, H., Nguyen, N.B., Winston, J., Hammond, T.G., Saban, R.: Time course of lps- induced gene expression in a mouse model of genitourinary inflammation. Physiological Genomics 5, 147–160 (2001)
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)
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 98, 5116–5121 (2001)
Efron, B., Tibshirani, R., Storey, J.D., Tusher, V.: Empirical bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 96, 1151–1160 (2001)
Newton, M.A., Noueiry, A., Sarkar, D., Ahlquist, P.: Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155–176 (2004)
Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences of the United States of America 97, 262–267 (2000)
Herrero, J., Valencia, A., Dopazo, J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17, 126–136 (2001)
Yeung, K.Y., Fraley, C., Murua, A., Raftery, A.E., Ruzzo, W.L.: Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977–987 (2001)
Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Statistica Sinica 12, 61–86 (2002)
Huang, D.S., Pan, W.: Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data. Bioinformatics 22, 1259–1268 (2006)
Jia, Z., Xu, S.: Clustering expressed genes on the basis of their association with a quantitative phenotype. Genetical Research 86, 193–207 (2005)
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)
Qu, Y., Xu, S.H.: Quantitative trait associated microarray gene expression data analysis. Molecular Biology and Evolution 23, 1558–1573 (2006)
Schwartz, G.: Estimating the dimensions of a model. Ann. Stat. 6, 461–464 (1978)
Hayes, J.G.: Numerical methods for curve and surface fitting. J. Inst. Math. Appl. 10, 144–152 (1974)
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)
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)
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)
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Jia, Z., Tang, S., Mercola, D., Xu, S. (2008). Detection of Quantitative Trait Associated Genes Using Cluster Analysis. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_8
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DOI: https://doi.org/10.1007/978-3-540-78757-0_8
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