Price AL, Zaitlen NA, Reich D, Patterson N. New approaches to population stratification in genome-wide association studies. Nat Rev Genet. 2010;11:459–63. 10.1038/nrg2813. DOI: 10.1038/nrg2813
Spielman RS, Ewens WJ. The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet. 1996;59:983–9.
Horvath S, Xu X, Laird NM. The family based association test method: strategies for studying general genotype--phenotype associations. Eur J Hum Genet EJHG. 2001;9:301–6. 10.1038/sj.ejhg.5200625. DOI: 10.1038/sj.ejhg.5200625
Simpson EH. The interpretation of interaction in contingency tables. J R Stat Soc Ser B Methodol. 1951;13:238–41.
Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet. 2004;36:512–7. 10.1038/ng1337. DOI: 10.1038/ng1337
Thomas DC, Witte JS. Point: population stratification: a problem for case-control studies of candidate-gene associations? Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2002;11:505–12.
Wacholder S, Rothman N, Caporaso N. Counterpoint: Bias from population stratification is not a major threat to the validity of conclusions from epidemiological studies of common polymorphisms and Cancer. Cancer Epidemiol Prev Biomark. 2002;11:513–20.
Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NLS, et al. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet. 2010;87:325–40. 10.1016/j.ajhg.2010.07.021. DOI: 10.1016/j.ajhg.2010.07.021
Cattaert T, Calle ML, Dudek SM, Mahachie John JM, Van Lishout F, Urrea V, et al. Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise. Ann Hum Genet. 2011;75:78–89. 10.1111/j.1469-1809.2010.00604.x. DOI: 10.1111/j.1469-1809.2010.00604.x
Lishout FV, Gadaleta F, Moore JH, Wehenkel L, Steen KV. gammaMAXT: a fast multiple-testing correction algorithm. BioData Min. 2015;8. https://doi.org/10.1186/s13040-015-0069-x.
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 2001;69:138–47. 10.1086/321276. DOI: 10.1086/321276
Zhao Y, Chen F, Zhai R, Lin X, Wang Z, Su L, et al. Correction for population stratification in random forest analysis. Int J Epidemiol. 2012:1798–806. https://doi.org/10.1093/ije/dys183.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75. DOI: 10.1086/519795
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;28:1957–64. 10.1093/bioinformatics/bts304. DOI: 10.1093/bioinformatics/bts304
Zhang BY, Zhang J, Liu JS. Block-based Bayesian epistasis association mapping with Appliation to WTCCC type 1 diabetes data. Ann Appl Stat. 2011;5:2052–77. 10.1214/11-AOAS469. DOI: 10.1214/11-AOAS469
Shang J, Zhang J, Sun Y, Liu D, Ye D, Yin Y. Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics. 2011;12:475. 10.1186/1471-2105-12-475. DOI: 10.1186/1471-2105-12-475
Li M, Lou X-Y, Lu Q. On epistasis: a methodological review for detecting gene-gene interactions underlying various types of phenotypic traits. Recent Pat Biotechnol. 2012;6:230–6. DOI: 10.2174/1872208311206030230
Gusareva ES, Van Steen K. Practical aspects of genome-wide association interaction analysis. Hum Genet. 2014;133:1343–58. 10.1007/s00439-014-1480-y. DOI: 10.1007/s00439-014-1480-y
Wei W-H, Hemani G, Haley CS. Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15:722–33. 10.1038/nrg3747. DOI: 10.1038/nrg3747
Gola D, John MMJ, van Steen K, König IR. A roadmap to multifactor dimensionality reduction methods. Brief Bioinform. 2016;17:293–308. 10.1093/bib/bbv038. DOI: 10.1093/bib/bbv038
Fouladi R, Bessonov K, Van Lishout F, Van Steen K. Model-based multifactor dimensionality reduction for rare variant association analysis. Hum Hered. 2015;79:157–67. 10.1159/000381286. DOI: 10.1159/000381286
Niu A, Zhang S, Sha Q. A novel method to detect gene–gene interactions in structured populations: MDR-SP. Ann Hum Genet. 2011;75:742–54. 10.1111/j.1469-1809.2011.00681.x. DOI: 10.1111/j.1469-1809.2011.00681.x
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9. 10.1038/ng1847. DOI: 10.1038/ng1847
Cattaert T, Calle ML, Dudek SM, Mahachie John JM, Van Lishout F, Urrea V, et al. A detailed view on model-based multifactor dimensionality reduction for detecting gene-gene interactions in case-control data in the absence and presence of noise. Ann Hum Genet. 2011;75:78–89. 10.1111/j.1469-1809.2010.00604.x. DOI: 10.1111/j.1469-1809.2010.00604.x
Mahachie John JM, Van Lishout F, Van Steen K. Model-based multifactor dimensionality reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. Eur J Hum Genet. 2011;19:696–703. 10.1038/ejhg.2011.17. DOI: 10.1038/ejhg.2011.17
Abegaz F, Chaichoompu K, Génin E, Fardo DW, König IR, Mahachie John JM, et al. Principals about principal components in statistical genetics. Brief Bioinform. 2019;20:2200–16. 10.1093/bib/bby081. DOI: 10.1093/bib/bby081
Alanis-Lobato G, Cannistraci CV, Eriksson A, Manica A, Ravasi T. Highlighting nonlinear patterns in population genetics datasets. Sci Rep. 2015;5:8140. 10.1038/srep08140. DOI: 10.1038/srep08140
Novembre J, Stephens M. Interpreting principal component analyses of spatial population genetic variation. Nat Genet. 2008;40:646–9. 10.1038/ng.139. DOI: 10.1038/ng.139
Abegaz F, Van Lishout F, Mahachie John JM, Chiachoompu K, Bhardwaj A, Gusareva ES, et al. Epistasis detection in genome-wide screening for complex human diseases in structured populations. Syst Med. 2019;2:19–27. 10.1089/sysm.2019.0003. DOI: 10.1089/sysm.2019.0003
Abegaz F, Lishout FV, John JMM, Chiachoompu K, Bhardwaj A, Gusareva ES, et al. Epistasis Detection using Model Based Multifactor Dimensionality Reduction in Structured Populations. bioRxiv. 2019:541946. https://doi.org/10.1101/541946.
Astle W, Balding DJ. Population structure and cryptic relatedness in genetic association studies. Stat Sci. 2009;24:451–71. 10.1214/09-STS307. DOI: 10.1214/09-STS307
Chen H, Wang C, Conomos MP, Stilp AM, Li Z, Sofer T, et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am J Hum Genet. 2016;98:653–66. 10.1016/j.ajhg.2016.02.012. DOI: 10.1016/j.ajhg.2016.02.012
Hoffman GE. Correcting for population structure and kinship using the linear mixed model: theory and extensions. PLoS One. 2013;8:e75707. 10.1371/journal.pone.0075707. DOI: 10.1371/journal.pone.0075707
Eu-ahsunthornwattana J, Miller EN, Fakiola M, Jeronimo SMB, Blackwell JM, Cordell HJ. Comparison of methods to account for relatedness in genome-wide association studies with family-based data. PLoS Genet. 2014;10:e1004445. 10.1371/journal.pgen.1004445. DOI: 10.1371/journal.pgen.1004445
Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. DOI: 10.1111/j.0006-341X.1999.00997.x
Wang K. Testing for genetic association in the presence of population stratification in genome-wide association studies. Genet Epidemiol. 2009;33:637–45. 10.1002/gepi.20415. DOI: 10.1002/gepi.20415
Ritchie MD, Hahn LW, Moore JH. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol. 2003;24:150–7. 10.1002/gepi.10218. DOI: 10.1002/gepi.10218
Martin E, Ritchie M, Hahn L, Kang S, Moore J. A novel method to identify gene–gene effects in nuclear families: the MDR-PDT. Genet Epidemiol. 2006;30:111–23. 10.1002/gepi.20128. DOI: 10.1002/gepi.20128
Balding DJ, Nichols RA. A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity. Genetica. 1995;96:3–12. 10.1007/BF01441146. DOI: 10.1007/BF01441146
Cattaert T, Urrea V, Naj AC, Lobel LD, Wit VD, Fu M, et al. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS One. 2010;5:e10304. 10.1371/journal.pone.0010304. DOI: 10.1371/journal.pone.0010304
Li W, Reich J. A complete enumeration and classification of two-locus disease models. Hum Hered. 2000;50:334–49 doi:22939. DOI: 10.1159/000022939
Pritchard JK, Stephens M, Rosenberg NA, Donnelly P. Association mapping in structured populations. Am J Hum Genet. 2000;67:170–81. DOI: 10.1086/302959
Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.
Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, et al. Genetic structure of human populations. Science. 2002;298:2381–5. 10.1126/science.1078311. DOI: 10.1126/science.1078311
Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–64. 10.1101/gr.094052.109. DOI: 10.1101/gr.094052.109
Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42:355–60. 10.1038/ng.546. DOI: 10.1038/ng.546
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, et al. Efficient control of population structure in model organism association mapping. Genetics. 2008;178:1709–23. 10.1534/genetics.107.080101. DOI: 10.1534/genetics.107.080101
Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010;42:348–54. 10.1038/ng.548. DOI: 10.1038/ng.548
Zhou X, Stephens M. Genome-wide efficient mixed model analysis for association studies. Nat Genet. 2012;44:821–4. 10.1038/ng.2310. DOI: 10.1038/ng.2310
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8:833–5. 10.1038/nmeth.1681. DOI: 10.1038/nmeth.1681
Listgarten J, Lippert C, Kang EY, Xiang J, Kadie CM, Heckerman D. A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics. 2013;29:1526–33. 10.1093/bioinformatics/btt177. DOI: 10.1093/bioinformatics/btt177
Svishcheva GR, Axenovich TI, Belonogova NM, van Duijn CM, Aulchenko YS. Rapid variance components-based method for whole-genome association analysis. Nat Genet. 2012;44:1166–70. DOI: 10.1038/ng.2410
Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 2016;12:e1005767. 10.1371/journal.pgen.1005767. DOI: 10.1371/journal.pgen.1005767
Patterson N, Price AL, Reich D. Population structure and Eigenanalysis. PLoS Genet. 2006;2:e190. 10.1371/journal.pgen.0020190. DOI: 10.1371/journal.pgen.0020190
Paschou P, Ziv E, Burchard EG, Choudhry S, Rodriguez-Cintron W, Mahoney MW, et al. PCA-correlated SNPs for structure identification in worldwide human populations. PLoS Genet. 2007;3:1672–86. 10.1371/journal.pgen.0030160. DOI: 10.1371/journal.pgen.0030160
Heath SC, Gut IG, Brennan P, McKay JD, Bencko V, Fabianova E, et al. Investigation of the fine structure of European populations with applications to disease association studies. Eur J Hum Genet EJHG. 2008;16:1413–29. 10.1038/ejhg.2008.210. DOI: 10.1038/ejhg.2008.210
Reich D, Price AL, Patterson N. Principal component analysis of genetic data. Nat Genet. 2008;40:491–2. 10.1038/ng0508-491. DOI: 10.1038/ng0508-491
Novembre J, Peter BM. Recent advances in the study of fine-scale population structure in humans. Curr Opin Genet Dev. 2016;41:98–105. 10.1016/j.gde.2016.08.007. DOI: 10.1016/j.gde.2016.08.007
Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491:119–24. 10.1038/nature11582. DOI: 10.1038/nature11582
