Abbott S, Fairbanks DJ. Experiments on plant hybrids by Gregor Mendel. Genetics. Genetics; 2016. pp. 407–422. https://doi.org/10.1534/genetics.116.195198 PMID: 27729492
Bush WS, Moore JH. Chapter 11: Genome-wide association studies. PLoS Comput Biol. 2012; 8: e1002822. https://doi.org/10.1371/journal.pcbi.1002822 PMID: 23300413
Lettre G, Lange C, Hirschhorn JN. Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol. 2007; 31: 358–362. https://doi.org/10.1002/gepi.20217 PMID: 17352422
Hall MA, Moore JH, Ritchie MD. Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine. Trends in Genetics. Elsevier Ltd; 2016. pp. 470–484. https://doi.org/10.1016/j.tig.2016.06.001 PMID: 27392675
Klein RJ, Zeiss C, Chew EY, Tsai J-Y, Sackler RS, Haynes C, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005; 308: 385–389. https://doi.org/10.1126/science. 1109557 PMID: 15761122
Maraganore DM, De Andrade M, Lesnick TC, Strain KJ, Farrer MJ, Rocca WA, et al. High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet. 2005; 77: 685–693. https://doi.org/10.1086/496902 PMID: 16252231
DeWan A, Liu M, Hartman S, Zhang SSM, Liu DTL, Zhao C, et al. HTRA1 promoter polymorphism in wet age-related macular degeneration. Science (80-). 2006; 314: 989–992. https://doi.org/10.1126/science.1133807 PMID: 17053108
Arking DE, Pfeufer A, Post W, Kao WHL, Newton-Cheh C, Ikeda M, et al. A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat Genet. 2006; 38: 644–651. https://doi.org/10.1038/ng1790 PMID: 16648850
Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007; 445: 881–5. https://doi.org/10.1038/nature05616 PMID: 17293876
Schymick JC, Scholz SW, Fung HC, Britton A, Arepalli S, Gibbs JR, et al. Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol. 2007; 6: 322–328. https://doi.org/10.1016/S1474-4422(07)70037-6 PMID: 17362836
Lencz T, Morgan T V., Athanasiou M, Dain B, Reed CR, Kane JM, et al. Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia. Mol Psychiatry. 2007; 12: 572–580. https://doi.org/10.1038/sj.mp.4001983 PMID: 17522711
Fung HC, Scholz S, Matarin M, Simón-Sánchez J, Hernandez D, Britton A, et al. Genome-wide genotyping in Parkinson’s disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol. 2006; 5: 911–916. https://doi.org/10.1016/S1474-4422(06)70578-6 PMID: 17052657
Matarín M, Brown WM, Scholz S, Simón-Sánchez J, Fung HC, Hernandez D, et al. A genome-wide genotyping study in patients with ischaemic stroke: initial analysis and data release. Lancet Neurol. 2007; 6: 414–420. https://doi.org/10.1016/S1474-4422(07)70081-9 PMID: 17434096
Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007; 447: 661–678. https://doi.org/10.1038/nature05911 PMID: 17554300
Ziegler A, König IR, Thompson JR. Biostatistical aspects of genome-wide association studies. Biometrical Journal. 2008. pp. 8–28. https://doi.org/10.1002/bimj.200710398 PMID: 18217698
Bierut LJ, Madden PAF, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, et al. Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007; 16: 24–35. https://doi.org/10.1093/hmg/ddl441 PMID: 17158188
Wallace C, Newhouse SJ, Braund P, Zhang F, Tobin M, Falchi M, et al. Genome-wide Association Study Identifies Genes for Biomarkers of Cardiovascular Disease: Serum Urate and Dyslipidemia. Am J Hum Genet. 2008; 82: 139–149. https://doi.org/10.1016/j.ajhg.2007.11.001 PMID: 18179892
Uda M, Galanello R, Sanna S, Lettre G, Sankaran VG, Chen W, et al. Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of β-thalassemia. Proc Natl Acad Sci U S A. 2008; 105: 1620–1625. https://doi.org/10.1073/pnas.0711566105 PMID: 18245381
Juran BD, Lazaridis KN. Genomics in the post-GWAS era. Semin Liver Dis. 2011; 31: 215–222. https://doi.org/10.1055/s-0031-1276641 PMID: 21538286
Zhang H, Morrison MA, DeWan A, Adams S, Andreoli M, Huynh N, et al. The NEI/NCBI dbGAP database: Genotypes and haplotypes that may specifically predispose to risk of neovascular age-related macular degeneration. BMC Med Genet. 2008; 9: 51. https://doi.org/10.1186/1471-2350-9-51 PMID: 18541031
Kooperberg C, Bis JC, Marciante KD, Heckbert SR, Lumley T, Psaty BM. Logic Regression for Analysis of the Association between Genetic Variation in the Renin-Angiotensin System and Myocardial Infarction or Stroke. Am J Epidemiol. 2006; 165: 334–343. https://doi.org/10.1093/aje/kwk006 PMID: 17082497
Jiao X, Ren J, Chen H, Ma J, Rao S, Huang K, et al. Ala499Val (CT) and Lys939Gln (AC) polymorphisms of the XPC gene: their correlation with the risk of primary gallbladder adenocarcinoma —a case-control study in China. Carcinogenesis. 2011; 32: 496–501. https://doi.org/10.1093/carcin/ bgq250 PMID: 21113018
Han W, Kim K-Y, Yang S-J, Noh D-Y, Kang D, Kwack K. SNP-SNP interactions between DNA repair genes were associated with breast cancer risk in a Korean population. Cancer. 2012; 118: 594–602. https://doi.org/10.1002/cncr.26220 PMID: 21751184
Ma L, Runesha HB, Dvorkin D, Garbe JR, Da Y. Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies. BMC Bioinformatics. 2008; 9: 315. https://doi.org/10.1186/1471-2105-9-315 PMID: 18644146
Singmann P, Baumert J, Herder C, Meisinger C, Holzapfel C, Klopp N, et al. Gene-gene interaction between apoa5 and usf1: Two candidate genes for the metabolic syndrome The KORA group. Obes Facts. 2009; 2: 235–242. https://doi.org/10.1159/000227288 PMID: 20054229
Neuhausen SL, Brummel S, Ding YC, Singer CF, Pfeiler G, Lynch HT, et al. Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers. Breast Cancer Res. 2009; 11: R76. https://doi.org/10.1186/bcr2414 PMID: 19843326
Enquobahrie DA, Smith NL, Bis JC, Carty CL, Rice KM, Lumley T, et al. Cholesterol Ester Transfer Protein, Interleukin-8, Peroxisome Proliferator Activator Receptor Alpha, and Toll-Like Receptor 4 Genetic Variations and Risk of Incident Nonfatal Myocardial Infarction and Ischemic Stroke. Am J Cardiol. 2008; 101: 1683–1688. https://doi.org/10.1016/j.amjcard.2008.02.052 PMID: 18549840
Verma SS, de Andrade M, Tromp G, Kuivaniemi H, Pugh E, Namjou-Khales B, et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front Genet. 2014; 5: 370. https://doi.org/10.3389/fgene.2014.00370 PMID: 25566314
McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011; 4: 13. https://doi.org/10.1186/1755-8794-4-13 PMID: 21269473
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018; 562: 203–209. https://doi.org/10.1038/s41586-018-0579-z PMID: 30305743
Hall MA, Wallace J, Lucas A, Kim D, Basile AO, Verma SS, et al. PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies. Nat Commun. 2017; 8: 1167. https://doi.org/10.1038/s41467-017-00802-2 PMID: 29079728
Mahachie John JM, Cattaert T, Lishout F Van, Gusareva ES, Steen K Van. Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction. PLoS One. 2012; 7: e29594. https://doi.org/10.1371/journal.pone.0029594 PMID: 22242176
Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J, Tcheandjieu C, et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020; 52: 680–691. https://doi.org/10.1038/s41588-020-0637-y PMID: 32541925
Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, et al. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Sci Adv. 2019; 5. https://doi.org/10.1126/sciadv.aaw3538 PMID: 31453325
Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci U S A. 2010; 107: 7395–7400. https://doi.org/10.1073/pnas.0912019107 PMID: 20385826
Cipriani V, Leung HT, Plagnol V, Bunce C, Khan JC, Shahid H, et al. Genome-wide association study of age-related macular degeneration identifies associated variants in the TNXB-FKBPL-NOTCH4 region of chromosome 6p21.3. Hum Mol Genet. 2012; 21: 4138–4150. https://doi.org/10.1093/hmg/dds225 PMID: 22694956
Naj AC, Scott WK, Courtenay MD, Cade WH, Schwartz SG, Kovach JL, et al. Genetic factors in nonsmokers with age-related macular degeneration revealed through genome-wide gene-environment interaction analysis. Ann Hum Genet. 2013; 77: 215–231. https://doi.org/10.1111/ahg.12011 PMID: 23577725
Kopplin LJ, Igo RP, Wang Y, Sivakumaran TA, Hagstrom SA, Peachey NS, et al. Genome-wide association identifies SKIV2L and MYRIP as protective factors for age-related macular degeneration. Genes Immun. 2010; 11: 609–621. https://doi.org/10.1038/gene.2010.39 PMID: 20861866
Williams FMK, Carter AM, Hysi PG, Surdulescu G, Hodgkiss D, Soranzo N, et al. Ischemic stroke is associated with the ABO locus: the EuroCLOT study. Ann Neurol. 2013; 73: 16–31. https://doi.org/10.1002/ana.23838 PMID: 23381943
Ritchie MD, Verma SS, Hall MA, Goodloe RJ, Berg RL, Carrell DS, et al. Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci. Mol Vis. 2014; 20: 1281–95. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4168835&tool=pmcentrez&rendertype=abstract PMID: 25352737
Anderson DH, Radeke MJ, Gallo NB, Chapin EA, Johnson PT, Curletti CR, et al. The pivotal role of the complement system in aging and age-related macular degeneration: Hypothesis re-visited. Progress in Retinal and Eye Research. NIH Public Access; 2010. pp. 95–112. https://doi.org/10.1016/j.preteyeres.2009.11.003 PMID: 19961953
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: 138–147. https://doi.org/10.1086/321276 PMID: 11404819
Holzinger ER, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. ATHENA: The analysis tool for heritable and environmental network associations. Bioinformatics. 2014; 30: 698–705. https://doi.org/10.1093/bioinformatics/btt572 PMID: 24149050
Motsinger AA, Reif DM, Dudek SM, Ritchie MD. Understanding the evolutionary process of grammatical evolution neural networks for feature selection in genetic epidemiology. Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB’06. NIH Public Access; 2006. pp. 263–270. https://doi.org/10.1109/CIBCB.2006.330945 PMID: 20634919
Breiman L. Random forests. Mach Learn. 2001; 45: 5–32. https://doi.org/10.1023/A:1010933404324
Urbanowicz RJ, Andrew AS, Karagas MR, Moore JH. Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: A learning classifier system approach. J Am Med Informatics Assoc. 2013; 20: 603–612. https://doi.org/10.1136/amiajnl-2012-001574 PMID: 23444013
Li R, Dudek SM, Kim D, Hall MA, Bradford Y, Peissig PL, et al. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData Min. 2016; 9. https://doi.org/10.1186/s13040-016-0094-4 PMID: 27168765
Li W, Reich J. A complete enumeration and classification of two-locus disease models. Hum Hered. 2000; 50: 334–349. https://doi.org/10.1159/000022939 PMID: 10899752
Carroll RJ, Bastarache L, Denny JC. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics. 2014; 30: 2375–2376. https://doi.org/10.1093/bioinformatics/btu197 PMID: 24733291
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012; 28: 3326–8. https://doi.org/10.1093/bioinformatics/bts606 PMID: 23060615
