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Abstract

Genome-wide association study (GWAS) has become an essential method to reveal the genetic mechanism of complex diseases. In the past decade, the research on GWAS methods has gradually advanced from the initial single-locus, single-trait analysis to multi-locus, multi-trait association analysis, but the results can only explain a small portion of the genetic power. Therefore, the methodological study of GWAS is of great importance.

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References

  1. Li Muzi. A gene mutation makes humans more susceptible to cancer [N]. China Science News, 2002-5-20(10).

    Google Scholar 

  2. SUNG B, PRASAD S, YADAV V R, et al. Cancer cell signaling pathways targeted by spice-derived nutraceuticals [J]. Nutrition and Cancer, 2012, 64(2):173–197.

    Article  Google Scholar 

  3. GANINI C, AMELIO I, BERTOLO R, et al. Global mapping of cancers: The Cancer Genome Atlas and beyond[J]. Molecular Oncology, 2021, 15(11):2823–2840.

    Article  Google Scholar 

  4. FORBES S A, TANG G, BINDAL N, et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer[J]. Nucleic Acids Research, 2010, 38(suppl_1):D652–D657.

    Google Scholar 

  5. STARK C, BREITKREUTZ B, REGULY T, et al. BioGRID: a general repository for interaction datasets[J]. Nucleic Acids Research, 2006, 34(suppl_1):D535–D539.

    Google Scholar 

  6. KANEHISA M, GOTO S. KEGG: Kyoto encyclopedia of genes and genomes[J]. Nucleic Acids Research, 2000, 28(1):27–30.

    Article  Google Scholar 

  7. SCHAEFER C F, ANTHONY K, KRUPA S, et al. PID: the pathway interaction database[J]. Nucleic Acids Research 2009, 37(suppl_1):D674–D679.

    Google Scholar 

  8. HONDO F, WERCELENS P, SILVA W D, et al. Data provenance management for bioinformatics workflows using NoSQL database systems in a cloud computing environment[C] //2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017:1929–1934.

    Google Scholar 

  9. YU JSSOIR G, BRIGGS W H, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness[J]. Nature genetics, 2006, 38(2):203–208.

    Google Scholar 

  10. KANG H M, ZAITLEN N A, WADE C M, et al. Efficient control of population structure in model organism association mapping[J]. Genetics, 2008, 178(3):1709–1723.

    Article  Google Scholar 

  11. KANG H M, SUL J H, SERVICE S K, et al. Variance component model to account for sample structure in genome-wide association studies[J]. Nature genetics, 2010, 42(4):348–354.

    Article  Google Scholar 

  12. LIPPERT C, LISTGARTEN J, LIU Y, et al. FaST linear mixed models for genome-wide association studies[J]. Nature methods, 2011, 8(10):833–835.

    Article  Google Scholar 

  13. ZHOU X, STEPHENS M. Genome-wide efficient mixed-model analysis for association studies[J]. Nature genetics, 2012, 44(7):821–824.

    Article  Google Scholar 

  14. YANG J, LEE S H, GODDARD M E, et al. GCTA: a tool for genome-wide complex trait analysis[J]. The American Journal of Human Genetics, 2011, 88(1):76–82.

    Article  Google Scholar 

  15. OSBORNE M R, PRESNELL B, and TURLACH B A. On the lasso and its dual[J]. Journal of Computational and Graphical statistics, 2000, 9(2): 319–337.

    Article  MathSciNet  Google Scholar 

  16. Zou H, Hastie T. Regularization and variable selection via the elastic net[J]. Journal of the royal statistical society: series B (statistical methodology), 2005, 67(2): 301–320.

    Article  MathSciNet  Google Scholar 

  17. ALGAMAL Z Y, and LEE M H. High dimensional logistic regression model using adjusted elastic net penalty[J]. Pakistan Journal of Statistics and Operation Research (2015): 667–676.

    Google Scholar 

  18. Zou H. The adaptive lasso and its oracle properties[J]. Journal of the American statistical association, 2006, 101(476): 1418–1429.

    Article  MathSciNet  Google Scholar 

  19. Casella G, Ghosh M, Gill J, et al. Penalized regression, standard errors, and Bayesian lassos[J]. Bayesian analysis, 2010, 5(2): 369–411.

    Article  MathSciNet  Google Scholar 

  20. Wu T T, Chen Y F, Hastie T, et al. Genome-wide association analysis by lasso penalized logistic regression[J]. Bioinformatics, 2009, 25(6): 714–721.

    Article  Google Scholar 

  21. Cho S, Kim H, Oh S, et al. Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis[C]//BMC proceedings. BioMed Central, 2009, 3(7): 1–6.

    Google Scholar 

  22. Xu S. An expectation-maximization algorithm for the Lasso estimation of quantitative trait locus effects[J]. Heredity, 2010, 105(5): 483–494.

    Article  Google Scholar 

  23. Segura V, Vilhjálmsson B J, Platt A, et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations[J]. Nature genetics, 2012, 44(7):825–830.

    Article  Google Scholar 

  24. Klasen J R, Barbez E, Meier L, et al. A multi-marker association method for genome-wide association studies without the need for population structure correction[J]. Nature communications, 2016, 7(1):1–8.

    Article  Google Scholar 

  25. Li J, Das K, Fu G, et al. The Bayesian lasso for genome-wide association studies[J]. Bioinformatics, 2011, 27(4):516–523.

    Article  Google Scholar 

  26. Korte A, Vilhjálmsson B J, Segura V, et al. A mixed-model approach for genome-wide association studies of correlated traits in structured populations[J]. Nature genetics, 2012, 44(9):1066–1071.

    Article  Google Scholar 

  27. Lee S H, Van der Werf J H J. MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information[J]. Bioinformatics, 2016, 32(9):1420–1422.

    Article  Google Scholar 

  28. Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies[J]. Nature methods, 2014, 11(4):407–409.

    Article  Google Scholar 

  29. Lippert C, Casale F P, Rakitsch B, et al. LIMIX: genetic analysis of multiple traits[J]. BioRxiv, 2014:003905.

    Google Scholar 

  30. Casale F P, Rakitsch B, Lippert C, et al. Efficient set tests for the genetic analysis of correlated traits[J]. Nature methods, 2015, 12(8):755–758.

    Article  Google Scholar 

  31. Meyer H V, Casale F P, Stegle O, et al. LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies[J]. BioRxiv, 2018:255497.

    Google Scholar 

  32. DRAGHICI, SORIN. Pathway Analysis of High Throughput Experiments[J]. CRC Press, 2014.

    Google Scholar 

  33. TARCA A L, DRAGHICI S, KHATRI P, et al. A novel signaling pathway impact analysis[J]. Bioinformatics, 2009, 25(1):75–82.

    Article  Google Scholar 

  34. VASKE C J, BENZ S C, SANBORN J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM[J]. Bioinformatics, 2010, 26(12):i237–i245.

    Article  Google Scholar 

  35. SOL E, SCHAEFER C F, BUETOW K H, et al. Identification of Key Processes Underlying Cancer Phenotypes Using Biologic Pathway Analysis[J]. Plos One, 2007, 2(5):e425.

    Article  Google Scholar 

  36. IQBAL S, HALIM Z. Orienting conflicted graph edges using genetic algorithms to discover pathways in protein-protein interaction networks[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 18(5):1970–1985.

    Article  Google Scholar 

  37. CIRIELLO G, CERAMI E, SANDER C, et al. Mutual exclusivity analysis identifies oncogenic network modules[J]. Genome Research, 2011, 22(2):398–406

    Article  Google Scholar 

  38. BABUR Ö, GÖNEN M, AKSOY B A, et al. Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations[J]. Genome Biology, 2015, 16(1):1–10.

    Article  Google Scholar 

  39. HOU J P, MA J. DawnRank: discovering personalized driver genes in cancer[J]. Genome medicine, 2014, 6(7):1–16.

    Article  Google Scholar 

  40. ZHAO J F, ZHANG S H, et al. Efficient methods for identifying mutated driver pathways in cancer[J]. Bioinformatics (Oxford, England), 28.22 (2012):2940–2947.

    Google Scholar 

  41. ZHANG J H, et al. Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data[J]. BMC systems biology, 2013, 7(2):1–12.

    Google Scholar 

  42. ZHENG C H, YANG W, CHONG Y W, et al. Identification of mutated driver pathways in cancer using a multi-objective optimization model[J]. Computers in Biology and Medicine 2016, 72:22–29.

    Article  Google Scholar 

  43. WU J L, CAI Q R, et al. Identifying mutated driver pathways in cancer by integrating multi-omics data[J]. Computational Biology and Chemistry, 2019, 80:159–167.

    Article  Google Scholar 

  44. LIN J, CHEN H, LI S, et al. Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier[J]. Artificial Intelligence in Medicine, 2019, 98:35–47.

    Google Scholar 

  45. STORN R, PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4):341–359.

    Article  MathSciNet  Google Scholar 

  46. NERI F, TIRRONEN V. Scale factor local search in differential evolution[J]. Memetic Computing, 2009, 1(2):153–171.

    Article  Google Scholar 

  47. JI J, XIAO H, YANG C. HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks[J]. Applied Intelligence, 2021, 51(2):1118–1132.

    Google Scholar 

  48. ALATAS B, AKIN E, KARCI A. MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules[J]. Applied Soft Computing, 2008, 8(1):646–656.

    Article  Google Scholar 

  49. DAO P, KIM Y A, WOJTOWIZ D, et al. BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions[J]. Plos Computational Biology, 2017, 13(10):e1005695.

    Article  Google Scholar 

  50. Shi J, Walker M G. Gene set enrichment analysis (GSEA) for interpreting gene expression profiles[J]. Current Bioinformatics, 2007, 2(2):133–137.

    Article  Google Scholar 

  51. PERNEGER T V. What’s wrong with Bonferroni adjustments[J]. British Medical Journal, 1998, 316(7139), 1236–1238.

    Article  Google Scholar 

  52. BENJAMINI Y. Discovering the false discovery rate[J]. Journal of the Royal Statistical Society:series B (statistical methodology), 2010, 72(4):405–416.

    Article  MathSciNet  Google Scholar 

  53. Fisher E A, Ginsberg H N. Complexity in the Secretory Pathway: The Assembly and Secretion of Apolipoprotein B-containing Lipoproteins[J]. Journal of Biological Chemistry, 2002, 277(20):17377–17380.

    Article  Google Scholar 

  54. KLEINKAUF R, HOUWAART T, BACKOFEN R, et al. antaRNA-Multi-objective inverse folding of pseudoknot RNA using ant-colony optimization[J]. BMC Bioinformatics, 2015, 16(1):1–7.

    Article  Google Scholar 

  55. LUSTIG B, BEHRENS J. The Wnt signaling pathway and its role in tumor development[J]. Journal of cancer research and clinical oncology, 2003, 129(4):199–221.

    Article  Google Scholar 

  56. Mármol I, Sánchez-de-Diego C, et al. Colorectal carcinoma: a general overview and future perspectives in colorectal cancer[J]. International journal of molecular sciences, 2017, 18(1):197.

    Article  Google Scholar 

  57. CHEN X, YAN C C, LUO C, et al. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity[J]. Scientific reports, 2015, 5(1):1–12.

    Google Scholar 

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Chen, Q. (2024). Biological Pathway Identification. In: Association Analysis Techniques and Applications in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-99-8251-6_9

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  • DOI: https://doi.org/10.1007/978-981-99-8251-6_9

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