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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li Muzi. A gene mutation makes humans more susceptible to cancer [N]. China Science News, 2002-5-20(10).
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.
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.
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.
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.
KANEHISA M, GOTO S. KEGG: Kyoto encyclopedia of genes and genomes[J]. Nucleic Acids Research, 2000, 28(1):27–30.
SCHAEFER C F, ANTHONY K, KRUPA S, et al. PID: the pathway interaction database[J]. Nucleic Acids Research 2009, 37(suppl_1):D674–D679.
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.
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.
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.
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.
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.
ZHOU X, STEPHENS M. Genome-wide efficient mixed-model analysis for association studies[J]. Nature genetics, 2012, 44(7):821–824.
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.
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.
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.
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.
Zou H. The adaptive lasso and its oracle properties[J]. Journal of the American statistical association, 2006, 101(476): 1418–1429.
Casella G, Ghosh M, Gill J, et al. Penalized regression, standard errors, and Bayesian lassos[J]. Bayesian analysis, 2010, 5(2): 369–411.
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.
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.
Xu S. An expectation-maximization algorithm for the Lasso estimation of quantitative trait locus effects[J]. Heredity, 2010, 105(5): 483–494.
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.
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.
Li J, Das K, Fu G, et al. The Bayesian lasso for genome-wide association studies[J]. Bioinformatics, 2011, 27(4):516–523.
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.
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.
Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies[J]. Nature methods, 2014, 11(4):407–409.
Lippert C, Casale F P, Rakitsch B, et al. LIMIX: genetic analysis of multiple traits[J]. BioRxiv, 2014:003905.
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.
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.
DRAGHICI, SORIN. Pathway Analysis of High Throughput Experiments[J]. CRC Press, 2014.
TARCA A L, DRAGHICI S, KHATRI P, et al. A novel signaling pathway impact analysis[J]. Bioinformatics, 2009, 25(1):75–82.
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.
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.
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.
CIRIELLO G, CERAMI E, SANDER C, et al. Mutual exclusivity analysis identifies oncogenic network modules[J]. Genome Research, 2011, 22(2):398–406
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.
HOU J P, MA J. DawnRank: discovering personalized driver genes in cancer[J]. Genome medicine, 2014, 6(7):1–16.
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.
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.
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.
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.
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.
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.
NERI F, TIRRONEN V. Scale factor local search in differential evolution[J]. Memetic Computing, 2009, 1(2):153–171.
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.
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.
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.
Shi J, Walker M G. Gene set enrichment analysis (GSEA) for interpreting gene expression profiles[J]. Current Bioinformatics, 2007, 2(2):133–137.
PERNEGER T V. What’s wrong with Bonferroni adjustments[J]. British Medical Journal, 1998, 316(7139), 1236–1238.
BENJAMINI Y. Discovering the false discovery rate[J]. Journal of the Royal Statistical Society:series B (statistical methodology), 2010, 72(4):405–416.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2024 Guangxi Education Publishing House
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-981-99-8251-6_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8250-9
Online ISBN: 978-981-99-8251-6
eBook Packages: Computer ScienceComputer Science (R0)