[1]
Sniekers S, Stringer S, Watanabe K, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet 2017; 49(7): 1107-12.
[2]
Savage JE, Jansen PR, Stringer S, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 2018; 50(7): 912-9.
[3]
Brant SR, Okou DT, Simpson CL, et al. Genome-Wide Association Study Identifies African-Specific Susceptibility Loci in African Americans With Inflammatory Bowel Disease. Gastroenterology 2017; 152(1): 206-217.e2.
[4]
Tian C, Hromatka BS, Kiefer AK, et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat Commun 2017; 8(1): 599.
[5]
Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer 2017; 17(11): 692-704.
[6]
Wang Z, McGlynn KA, Rajpert-De Meyts E, et al. Testicular Cancer Consortium.Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor. Nat Genet 2017; 49(7): 1141-7.
[7]
Chang D, Nalls MA, Hallgrímsdóttir IB, et al. International Parkinson’s Disease Genomics Consortium.23andMe Research Team. A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat Genet 2017; 49(10): 1511-6.
[8]
Erdmann J, Kessler T, Munoz Venegas L, Schunkert H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res 2018; 114(9): 1241-57.
[9]
Maguire LH, Handelman SK, Du X, Chen Y, Pers TH, Speliotes EK. Genome-wide association analyses identify 39 new susceptibility loci for diverticular disease. Nat Genet 2018; 50(10): 1359-65.
[10]
Giacomini KM, Yee SW, Mushiroda T, et al. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nat Rev Drug Discov 2017; 16(1): 1.
[11]
Elliott LT, Sharp K, Alfaro-Almagro F, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 2018; 562(7726): 210-6.
[12]
Pulit SL, Stoneman C, Morris AP, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019; 28(1): 166-74.
[13]
Yengo L, Sidorenko J, Kemper KE, et al. GIANT Consortium.Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet 2018; 27(20): 3641-9.
[14]
Collins A, Lonjou C, Morton NE. Genetic epidemiology of single-nucleotide polymorphisms. Proc Natl Acad Sci USA 1999; 96(26): 15173-7.
[15]
Schork NJ, Fallin D, Lanchbury JS. Single nucleotide polymorphisms and the future of genetic epidemiology. Clin Genet 2000; 58(4): 250-64.
[16]
Cordell HJ. Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 2002; 11(20): 2463-8.
[17]
Ivanova-Stoevska M, Penchev M, Stoyanova V, et al. Investigation of candidate genes reveals significant statistical epistasis between DISC1 and TPH2 in Bulgarian affective disorder patients. Biotechnol Biotechnol Equip 2017; 31(6): 1178-83.
[18]
Meng S, Liu G, Su L, et al. Functional clusters analysis and research based on differential coexpression networks. Biotechnol Biotechnol Equip 2018; 32(1): 171-82.
[19]
Ivanova N, Postadzhiyan A, Apostolova MD. An Application of Logistic Regression and Multifactor Dimensionality Reduction Analyses for Detecting Genotype-Phenotype Interactions Associated with Developing of Atherosclerosis in Bulgarian Cohort. Biotechnol Biotechnol Equip 2012; 26(Suppl. 1): 191-9.
[20]
Wei WH, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet 2014; 15(11): 722-33.
[21]
Mackay TF, Moore JH. Why epistasis is important for tackling complex human disease genetics. Genome Med 2014; 6(6): 124.
[22]
Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005; 6(2): 95-108.
[23]
Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010; 363(2): 166-76.
[24]
Schork NJ, Murray SS, Frazer KA, Topol EJ. Common vs. rare allele hypotheses for complex diseases. Curr Opin Genet Dev 2009; 19(3): 212-9.
[25]
Altmüller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 2001; 69(5): 936-50.
[26]
Ritchie MD, Hahn LW, Roodi N, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001; 69(1): 138-47.
[27]
Gyenesei A, Moody J, Semple CAM, Haley CS, Wei W-H. High-throughput analysis of epistasis in genome-wide association studies with BiForce. Bioinformatics 2012; 28(15): 1957-64.
[28]
Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81(3): 559-75.
[29]
Zhu Z, Tong X, Zhu Z, et al. Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes. PLoS One 2013; 8(4)e61943
[30]
Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007; 39(9): 1167-73.
[31]
Wan X, Yang C, Yang Q, et al. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010; 87(3): 325-40.
[32]
Zhang X, Zou FEI, Wang WEI. In:Biocomputing 2009 FASTCHI: An Efficient Algorithm For Analyzing gene-gene interactions. World scientific 2008; pp. 528-39.
[33]
Wu TT, Chen YF, Hastie T, Sobel E, Lange K. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 2009; 25(6): 714-21.
[34]
Schwarz DF, König IR, Ziegler A. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics 2010; 26(14): 1752-8.
[35]
Nguyen TT, Huang J, Wu Q, Nguyen T, Li M. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests. BMC Genomics 2015; 16(S2)(Suppl. 2): S5.
[36]
Li J, Horstman B, Chen Y. Detecting epistatic effects in association studies at a genomic level based on an ensemble approach. Bioinformatics 2011; 27(13): i222-9.
[37]
Meng YA, Yu Y, Cupples LA, Farrer LA, Lunetta KL. Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinformatics 2009; 10(1): 78.
[38]
Wu Q, Ye Y, Liu Y, Ng MK. SNP selection and classification of genome-wide SNP data using stratified sampling random forests. IEEE Trans Nanobioscience 2012; 11(3): 216-27.
[39]
Jing P-J, Shen H-B. MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics 2015; 31(5): 634-41.
[40]
Wang Y, Liu X, Robbins K, Rekaya R. AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res Notes 2010; 3(1): 117.
[41]
Aflakparast M, Salimi H, Gerami A, Dubé MP, Visweswaran S, Masoudi-Nejad A. Cuckoo search epistasis: a new method for exploring significant genetic interactions. Heredity 2014; 112(6): 666-74.
[42]
Sun Y, Shang J, Liu JX, Li S, Zheng CH. epiACO - a method for identifying epistasis based on ant Colony optimization algorithm. BioData Min 2017; 10(1): 23.
[43]
Yuan L, Yuan CA, Huang DS. FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis. Complexity 2017; 2017(1): 1-10.
[44]
Tuo S, Zhang J, Yuan X, Zhang Y, Liu Z. FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm. PLoS One 2016; 11(3)e0150669
[45]
Tuo S, Zhang J, Yuan X, He Z, Liu Y, Liu Z. Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations. Sci Rep 2017; 7(1): 11529.
[46]
Yang C, He Z, Wan X, Yang Q, Xue H, Yu W. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics 2009; 25(4): 504-11.
[47]
Yang C-H, Chuang L-Y, Lin Y-D. CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies. Bioinformatics 2017; 33(15): 2354-62.
[48]
Manjarres D, Landa-Torres I, Gil-Lopez S, et al. A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 2013; 26(8): 1818-31.
[49]
Breiman LI, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees (CART) In: Encyclopedia of Ecology. 1998; 40: 582-8.
[50]
Visweswaran S, Wong AKI, Barmada MM. A Bayesian Method for Identifying Genetic Interactions. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium 2009; 2009: 673.
[51]
Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992; 9(4): 309-47.
[52]
Hoey J. The Two-Way Likelihood Ratio (G) Test and Comparison
to Two-Way Chi Squared Test. arXiv e-prints 2012. 6
[53]
Burton PR, Clayton DG, Cardon LR, et al. Wellcome Trust Case Control Consortium.Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447(7145): 661-78.
