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BioMed Research International
Volume 2013 (2013), Article ID 574735, 11 pages
Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression
1Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
2The Genome Institute, Washington University, St. Louis, MO 63108, USA
3College of Medicine and Forensics, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi 710061, China
4Key Laboratory of the Ministry of Health for Forensic Sciences, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
5Key Laboratory of the Ministry of Education for Environment and Genes Related to Diseases, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
6Center for Translational Medicine, The First Affiliated Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi 710061, China
Received 2 April 2013; Revised 8 June 2013; Accepted 2 July 2013
Academic Editor: Eugénio Ferreira
Copyright © 2013 Xuanping Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- J. N. Hirschhorn and M. J. Daly, “Genome-wide association studies for common diseases and complex traits,” Nature Reviews Genetics, vol. 6, no. 2, pp. 95–108, 2005.
- L. A. Hindorff, P. Sethupathy, H. A. Junkins et al., “Potential etiologic and functional implications of genome-wide association loci for human diseases and traits,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 23, pp. 9362–9367, 2009.
- G. Gibson, “Rare and common variants: twenty arguments,” Nature Reviews Genetics, vol. 13, no. 2, pp. 135–145, 2012.
- P. M. Visscher, W. G. Hill, and N. R. Wray, “Heritability in the genomics era—concepts and misconceptions,” Nature Reviews Genetics, vol. 9, no. 4, pp. 255–266, 2008.
- E. E. Eichler, J. Flint, G. Gibson et al., “Missing heritability and strategies for finding the underlying causes of complex disease,” Nature Reviews Genetics, vol. 11, no. 6, pp. 446–450, 2010.
- J. He, K. Wang, A. C. Edmondson, D. J. Rader, C. Li, and M. Li, “Gene-based interaction analysis by incorporating external linkage disequilibrium information,” European Journal of Human Genetics, vol. 19, no. 2, pp. 164–172, 2011.
- A. A. Motsinger, S. L. Lee, G. Mellick, and M. D. Ritchie, “GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease,” BMC Bioinformatics, vol. 7, article S39, 2006.
- A. A. Motsinger-Reif, S. M. Dudek, L. W. Hahn, and M. D. Ritchie, “Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology,” Genetic Epidemiology, vol. 32, no. 4, pp. 325–340, 2008.
- Z. Li, T. Zheng, A. Califano, et al., “Pattern-based mining strategy to detect multi-locus association and gene times environment interaction,” BMC Proceedings, vol. 1, supplement 1, article S16, 2007.
- Q. Long, Q. Zhang, and J. Ott, “Detecting disease-associated genotype patterns,” BMC Bioinformatics, vol. 10, supplement 1, article S75, 2009.
- I. Ruczinski, C. Kooperberg, and M. Leblanc, “Logic regression,” Journal of Computational and Graphical Statistics, vol. 12, no. 3, pp. 475–511, 2003.
- C. Kooperberg and I. Ruczinski, “Identifying interacting SNPs using Monte Carlo logic regression,” Genetic Epidemiology, vol. 28, no. 2, pp. 157–170, 2005.
- A. Fritsch and K. Ickstadt, “Comparing logic regression based methods for identifying SNP interactions,” in Proceedings of the 1st International Conference on Bioinformatics Research and Development (BIRD '07), pp. 90–103, March 2007.
- H. Schwender and K. Ickstadt, “Identification of SNP interactions using logic regression,” Biostatistics, vol. 9, no. 1, pp. 187–198, 2008.
- H. Schwender, I. Ruczinski, and K. Ickstadt, “Testing SNPs and sets of SNPs for importance in association studies,” Biostatistics, vol. 12, no. 1, pp. 18–32, 2011.
- H. Janes, M. Pepe, C. Kooperberg, and P. Newcomb, “Identifying target populations for screening or not screening using logic regression,” Statistics in Medicine, vol. 24, no. 9, pp. 1321–1338, 2005.
- J. Wang, J. Zhang, and Y. Wu, “Identifying interacting SNPs with parallel fish-agent based logic regression,” in Proceedings of the 1st IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS '11), pp. 171–177, February 2011.
- X. Li, Z. Shao, and J. Qian, “Optimizing method based on autonomous animats: fish-swarm algorithm,” System Engineering, vol. 22, no. 11, pp. 32–38, 2002.
- X. Li, A new intelligent optimization—artificial fish swarm algorithm [Ph.D. thesis], Zhejiang University, Zhejiang, China, 2003.
- M. Neshat, G. Sepidnam, M. Sargolzaei, and A. N. Toosi, “Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications,” Artificial Intelligence Review, 2012.
- Q. Hou and S. Li, “Potential association of DRD2 and DAT1 genetic variation with heroin dependence,” Neuroscience Letters, vol. 464, no. 2, pp. 127–130, 2009.
- J. H. Lai, Y. S. Zhu, Z. H. Huo et al., “Association study of polymorphisms in the promoter region of DRD4 with schizophrenia, depression, and heroin addiction,” Brain Research, vol. 1359, pp. 227–232, 2010.
- K. Xu, D. Lichtermann, R. H. Lipsky et al., “Association of specific haplotypes of D2 dopamine receptor gene with vulnerability to heroin dependence in 2 distinct populations,” Archives of General Psychiatry, vol. 61, no. 6, pp. 597–606, 2004.
- A. Szilagyi, K. Boor, A. Szekely, et al., “Combined effect of promoter polymorphisms in the dopamine D4 receptor and the serotonin transporter genes in heroin dependence,” Neuropsychopharmacol Hung, vol. 7, pp. 28–33, 2005.
- Y. Li, C. Shao, D. Zhang et al., “The effect of dopamine D2, D5 receptor and transporter (SLC6A3) polymorphisms on the cue-elicited heroin craving in Chinese,” American Journal of Medical GeneticsB, vol. 141, no. 3, pp. 269–273, 2006.
- W. Huang, J. Z. Ma, T. J. Payne, J. Beuten, R. T. Dupont, and M. D. Li, “Significant association of DRD1 with nicotine dependence,” Human Genetics, vol. 123, no. 2, pp. 133–140, 2008.
- W. Huang and M. D. Li, “Differential allelic expression of dopamine D1 receptor gene (DRD1) is modulated by microRNA miR-504,” Biological Psychiatry, vol. 65, no. 8, pp. 702–705, 2009.
- D. Kim, B. L. Park, S. Yoon et al., “5′ UTR polymorphism of dopamine receptor D1 (DRD1) associated with severity and temperament of alcoholism,” Biochemical and Biophysical Research Communications, vol. 357, no. 4, pp. 1135–1141, 2007.
- D. S. D. Lobo, H. P. Vallada, J. Knight et al., “Dopamine genes and pathological gambling in discordant Sib-Pairs,” Journal of Gambling Studies, vol. 23, no. 4, pp. 421–433, 2007.
- G. Hellenthal and M. Stephens, “msHOT: modifying Hudson's ms simulator to incorporate crossover and gene conversion hotspots,” Bioinformatics, vol. 23, no. 4, pp. 520–521, 2007.