Influence of Number of Gene Loci on Prediction of Animal Phenotype Value Using Back-Propagation Artificial Neural Network

Abstract:

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Vast amount of bioinformation immerged in the past, HapMap Project had genotyped more than 3.1 million Single Nucleotide Polymorphisms (SNPs) information by 2007, a prediction equation based on SNPs was derived to calculate genomic breeding values. However, the simple mathematical function could not reflect the complex relation between genome and phenotypes. Unlike the methods of regression, artificial neural networks could perform well for optimization in complex non-linear systems; artificial neural networks have not been used to calculate genomic breeding values. In this paper, back-propagation neural network is used to simulate and predict the genomic breeding values or polygenic genotype value, and the different numbers of gene loci and hidden neurons were used to discuss the influence of the learning rate on estimating the polygenic genotype value. The result showed normalization was very important for training prediction model. After phenotype value normalized, optimum neural network for estimating the animal phenotype could be established without considering the gene number, but the optimum neural network should be selected from amount of neuronal networks with different hidden neuron number. No matter what the gene number is, as well as the number of hidden neurons is right, BP networks could be used to predict the animal phenotypes.

Info:

Periodical:

Key Engineering Materials (Volumes 460-461)

Edited by:

Yanwen Wu

Pages:

329-334

DOI:

10.4028/www.scientific.net/KEM.460-461.329

Citation:

X. B. Li et al., "Influence of Number of Gene Loci on Prediction of Animal Phenotype Value Using Back-Propagation Artificial Neural Network", Key Engineering Materials, Vols. 460-461, pp. 329-334, 2011

Online since:

January 2011

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$35.00

[1] M.B. Miller, G. R. Lind, N Li, S Y Jang. BMC Proceedings. Vol. 1 (Suppl 1) (2007), p. S4.

[2] D. Altshuler, M. J. Daly, E S Lander. Science. Vol. 322 (2008), pp.881-888.

[3] J. Murvai, K. Vlahoviček, C. Szepesvári, and S. Pongor. Genome research. Vol. 11(2009), p.1410–1417.

[4] D. Huesken, J. Lange, C. Mickanin, J. Weiler, F. Asselbergs, J. Warner, et al. Nature Biotechnology. Vol. 23 (2005), pp.995-1001.

[5] J. Khan, J.S. Wei, M. Ringnér, L.H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C.R. Antonescu, C. Peterson, P.S. Meltzer. Nature Medicine. Vol. 7 (2001), pp.673-679.

DOI: 10.1038/89044

[6] M. Milik, D. Sauer, A. P. Brunmark, L. Yuan, A. Vitiello, M.R. Jackson, P.A. Peterson, J. Skolnick, C.A. Nature Biotechnology. Vol. 16 (1998), pp.753-756.

DOI: 10.1038/nbt0898-753

[7] X. B. Li, X. L. Yu. 2009 International symposium on computational intelligence and design. Vol. 2 (2009), pp.270-273.

[8] X. B. Li, X. L. Yu. The International Conference on computational intelligence and software engineering (CiSE 2009). 2009, 12.

[9] N. Nikolic, N. Nikolic1, Y. S Park2, M. Sancristobal1, S. Lek, C. Chevalet. Genet. Res. Vol. 91 (2009), p.121–132.

[10] H. A. Lewin. It's a Bull's Market. Science. Vol. 324 (2009), pp.478-479.

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