Abstract
The analysis of haplotype data of human populations has received much attention recently. For instance, problems such as Haplotype Reconstruction are important intermediate steps in gene association studies, which seek to uncover the genetic basis of complex diseases. In this chapter, we explore the application of probabilistic logic learning techniques to haplotype data. More specifically, a new haplotype reconstrcution technique based on Logical Hidden Markov Models is presented and experimentally compared against other state-of-the-art haplotyping systems. Furthermore, we explore approaches for combining haplotype reconstructions from different sources, which can increase accuracy and robustness of reconstruction estimates. Finally, techniques for discovering the structure in haplotype data at the level of haplotypes and population are discussed.
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References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press (1996)
Daly, M.J., Rioux, J.D., Schaffner, S.F., Hudson, T.J., Lander, E.S.: High-Resolution Haplotype Structure in the Human Genome. Nature Genetics 29, 229–232 (2001)
Eronen, L., Geerts, F., Toivonen, H.: A Markov Chain Approach to Reconstruction of Long Haplotypes. In: Altman, R.B., Dunker, A.K., Hunter, L., Jung, T.A., Klein, T.E. (eds.) Biocomputing 2004, Proceedings of the Pacific Symposium, Hawaii, USA, 6-10 January 2004, pp. 104–115. World Scientific, Singapore (2004)
Eronen, L., Geerts, F., Toivonen, H.: HaploRec: efficient and accurate large-scale reconstruction of haplotypes. BMC Bioinformatics 7, 542 (2006)
Flum, J., Grohe, M.: Parameterized Complexity Theory. In: EATCS Texts in Theoretical Computer Science, Springer, Heidelberg (2006)
Gionis, A., Mannila, H., Terzi, E.: Clustered segmentations. In: 3rd Workshop on Mining Temporal and Sequential Data (TDM) (2004)
Gabriel, S.B., Schaffner, S.F., Nguyen, H., Moore, J.M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., Liu-Cordero, S.N., Rotimi, C., Adeyemo, A., Cooper, R., Ward, R., Lander, E.S., Daly, M.J., Altshuler, D.: The structure of haplotype blocks in the human genome. Science 296(5576), 2225–2229 (2002)
Halldórsson, B.V., Bafna, V., Edwards, N., Lippert, R., Yooseph, S., Istrail, S.: A survey of computational methods for determining haplotypes. In: Istrail, S., Waterman, M.S., Clark, A. (eds.) DIMACS/RECOMB Satellite Workshop 2002. LNCS (LNBI), vol. 2983, pp. 26–47. Springer, Heidelberg (2004)
Higasa, K., Miyatake, K., Kukita, Y., Tahira, T., Hayashi, K.: D-HaploDB: A database of definitive haplotypes determined by genotyping complete hydatidiform mole samples. Nucleic Acids Research 35, D685–D689 (2007)
Hudson, R.R.: Generating samples under a wright-fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002)
Kersting, K., De Raedt, L., Raiko, T.: Logical hidden markov models. Journal for Artificial Intelligence Research 25, 425–456 (2006)
Koivisto, M., Kivioja, T., Mannila, H., Rastas, P., Ukkonen, E.: Hidden markov modelling techniques for haplotype analysis. In: Ben-David, S., Case, J., Maruoka, A. (eds.) ALT 2004. LNCS (LNAI), vol. 3244, pp. 37–52. Springer, Heidelberg (2004)
Kääriäinen, M., Landwehr, N.: Sampsa Lappalainen, and Taneli Mielikäinen. Combining haplotypers. Technical Report C-2007-57, Department of Computer Science, University of Helsinki (2007)
Kimmel, G., Shamir, R.: A Block-Free Hidden Markov Model for Genotypes and Its Applications to Disease Association. Journal of Computational Biology 12(10), 1243–1259 (2005)
Landwehr, N., Mielikäinen, T., Eronen, L., Toivonen, H., Mannila, H.: Constrained hidden markov models for population-based haplotyping. BMC Bioinformatics (to appear, 2007)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Rastas, P., Koivisto, M., Mannila, H., Ukkonen, E.: A hidden markov technique for haplotype reconstruction. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS (LNBI), vol. 3692, pp. 140–151. Springer, Heidelberg (2005)
Savage, C.: A survey of combinatorial gray codes. SIAM Review 39(4), 605–629 (1997)
Stephens, M., Scheet, P.: Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation. The American Journal of Human Genetics 76, 449–462 (2005)
Scheet, P., Stephens, M.: A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase. The American Journal of Human Genetics 78, 629–644 (2006)
Salem, R., Wessel, J., Schork, N.: A comprehensive literature review of haplotyping software and methods for use with unrelated individuals. Human Genomics 2, 39–66 (2005)
The International HapMap Consortium. A Haplotype Map of the Human Genome. Nature, 437, 1299–1320 (2005)
Thompson Jr., J.N., Hellack, J.J., Braver, G., Durica, D.S.: Primer of Genetic Analysis: A Problems Approach, 2nd edn. Cambridge University Press, Cambridge (1997)
Wang, W.Y.S., Barratt, B.J., Clayton, D.G., Todd, J.A.: Genome-wide association studies: Theoretical and practical concerns. Nature Reviews Genetics 6, 109–118 (2005)
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Landwehr, N., Mielikäinen, T. (2008). Probabilistic Logic Learning from Haplotype Data. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds) Probabilistic Inductive Logic Programming. Lecture Notes in Computer Science(), vol 4911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78652-8_10
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DOI: https://doi.org/10.1007/978-3-540-78652-8_10
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