- Genome-wide association
- Computational burden
- Data sparsity
- Methodological issues
Empirical evidence supporting the commonality of gene × gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene × gene interactions comprehensively. Currently, there is no single method that is recognized as the ‘best’ for detecting, characterizing, and interpreting gene × gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.
Copyright © 2007 S. Karger AG, Basel
- Cordell HJ: Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 2002;11:2463–2468.
- Wilson SR: Epistasis, Nature Encyclopedia of the Human Genome, D.N. Cooper (ed.). Nature Publishing Group, London, 2004;2:317.
- Bateson W: Mendel’s Principles of Heredity. Cambridge University Press, Cambridge, 1909.
- Fisher RA: The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edin 1918;52:399–433.
- Moore JH, Williams SM: New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.
- Sing CF, Boerwinkle EA: Genetic architecture of inter-individual variability in apolipoprotein, lipoprotein and lipid phenotypes. Ciba Found Symp 1987;130:99–127.
- Frankel WN, Schork NJ: Who’s afraid of epistasis? Nat Genet 1996;14:371–373.
- Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med 2002;4:45–61.
- Andersson C, Hamsten A, Karpe F: Gene × gene interaction between ApoB and ApoE in determining plasma levels and heterogeneity of ApoB-containing lipoproteins (abstract 2806). Circulation 1998;17(suppl 1):534.
- Cox NJ, Frigge M, Nicolae DL, Concannon P, Hanis CL, Bell GI, Kong A: Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans, Nat Genet 1999:21:213–215.
- Barnes KC, Mathias RA, Nickel R, et al: Testing for gene×gene interaction controlling total IgE in families from Barbados: Evidence of sensitivity regarding linkage heterogeneity among families. Genomics 2001;71:246–251.
- Dong CH, Wang S, Li WD, et al: Interacting genetic loci on chromosomes 20 and 10 influence extreme human obesity. Am J Hum Genet 2003;72:115–124.
- Cordell HJ, Todd JA, Bennett ST, Kawagushi Y and Farrell M: Two-locus maximum LOD score analysis of a multifactorial trait: 7 joint consideration of IDDM2 and IDDM4 with DDM1 in type 1 diabetes. Am J Hum Genet 1995;57:920–934.
- Cho JH, Nicolae DL, Gold LH, Fields CT, LaBuda MC, Rohal PM, et al: Identification of novel susceptibility loci for inflammatory bowel disease on chromosomes 1p, 3q, and 4q: evidence for epistasis between 1p and IBD1. Proc Natl Acad Sci USA 1998;95:7502–7507.
- Xu J, Langefeld CD, Zheng SL, Gillanders EM, Chang BL, Isaacs SD, et al: Interaction effect of PTEN and CDKN1B chromosomal regions on prostate cancer linkage. Hum Genet 2004;115:255–262.
- Aston CE, Ralph DA, Lalo DP, Manjeshwar S, Gramling BA, DeFreese DC, et al: Oligogenic combinations associated with breast cancer risk in women under 53 years of age. Hum Genet 2005;116:208–221.
- Wang S, Zhao H: Sample size needed to detect gene × gene interactions using association designs. Am J Epidemiol 2003;899–914.
- Risch N, Merikangas K: The future of genetic studies of complex human disease. Science 1996;273:1516–1519.
- Templeton AR: Epistasis and complex traits; in Wolf J, Brodie B III, Wade M (eds): Epistasis and the Evolutionary Process. New York: Oxford University Press, 2000.
- Culverhouse R, Suarez B, Lin T, Reich T: A perspective on epistasis: Limits of models displaying no main effect. Am J Hum Genet 2002;70:461–471.
- Moore JH: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 2003;56:73–82.
- Carlborg Ö, Haley CS: Epistasis: too often neglected in complex trait studies? Nat Rev Genet 2004;5:618–625.
- Thornton-Wells TA, Moore JH, Haines JL: Genetics, statistics and human disease: analytical retooling for complexity. Trends Genet 2004;20:640–647.
- Warden CH, Stone S, Chiu S, Diament AL, Corva P, Shattuck D, et al: Identification of a Congenic mouse line with obesity and body length phenotypes. Mamm Genome 2004;15:460–471.
- Heidema AG, Boer JMA, Nagelkerke N, Mariman ECM, van der A DL, Feskens EJM: The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases. BMC Genet DOI: 10.1186/1471-2156-7-23.
- Hoh J, Ott J: Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev 2003;4:701–709.
- Sing CF, Haviland MB, Reilly SL: Genetic architecture of common multi-factorial diseases; in Chadwick & Cardew G (eds): Variation in the Human Genome (Ciba Found Symp 1997). Chichester, John Wiley & Sons, 2003, pp 211–232.
- Zerba KE, Ferrell RE, Sing CF: Complex adaptive systems and human health: The influence of common genotypes of the apolipoprotein E (ApoE) gene polymorphism and age on the relational order within a field of lipid metabolism traits. Hum Genet 2000;107:466–475.
- Routman EJ, Cheverud JM: Gene effects on a quantitative trait: two-locus epistatic effects measured at microsatellite markers and at estimated QTL. Evolution 1997;51:1654–1662.
- Kim JH, Sen SA, Avery CS, Simpson E, Chandler P, Nishina PM, Churchill GA, Naggert JK: Genetic analysis of a new mouse model for non-insulin-dependent diabetes. Genomics 2001;74:273–286.
- Mackay TFC: The genetic architecture of quantitative traits. Ann Rev Genet 2001;35:303–339.
- Shimomura K, Low-Zeddies SS, King DP, Steeves TDL, Whiteley A, Kushla J, et al: Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome Res 2001;11:959–980.
- Ways JA, Cicilla GT, Garment MR, Koch LG: A genome scan for loci associated with aerobic running capacity in rats. Genomics 2002;80:13–20.
- Williams SM, Haines JL, Moore JH: The use of animal models in the study of complex disease: All else is never equal or why do so many human studies fail to replicate animal findings? BioEssays 2004;26:170–179.
- Segre D, Deluna A, Church GM, Kishony R: Modular epistasis in yeast metabolism. Nat Genet 2005;37:77–83.
- Marchini J, Donnelly P, Cardon LR: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005;37:413–417.
- Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev 2005;6:95–108.
- Wang WYS, Barratt BJ, Clayton DG, Todd JA: Genome-wide association studies: Theoretical and practical concerns. Nat Rev 2005;6:109–118.
- Ioannidis JPA, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG: Replication validity of genetic association studies. Nat Genet 2001;29:306–309.
- Cardon LR, Bell JI: Association study designs for complex diseases. Nat Rev Genet 2001;2:91–98.
- Li W, Reich W: A complete enumeration and classification of two-locus disease models. Hum Hered 2000;50:334–349.
- Moore JH, Ritchie MD: The challenges of whole-genome approaches to common diseases. J Am Med Assoc 2004;291:1642–1643.
- Moore JH, Gilberta JC, Tsaif C-T, Chiangf F-T, Holdena T, Barneya N, White BC: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol DOI: 10.1016/j.jtb.2005.11.036.
- Chen Y, Carlini DB, Baines JF, Parsch J, Braveman JM, Tanda S, Stephan W: RNA secondary structure and compensatory evolution. Genes Genet Sys 1999;74:271–286.
- Maisnier-Patin S, Andersson DI: Adaptation to the deletion and effects of antimicrobial drug resistance to mutations by compensatory evolution. Res Microbiology 2004;15:360–369.
- Wang T, Zeng Z-B: Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium. BMC Genet 2006;7:9.
- Culverhouse R, Klein T, Shannon W: Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 2004;27:141–152.
- Kirkpatrick S, Gelatt CD Jr, Vecchi MP: Optimization by simulated annealing. Science 220:671–680.
- Koza JR, Rice JP: Genetic generation of both the weights and architecture for a neural network. Volume II. IEEE Press, 1991.
- Zubenko GS, Hughes HB, Stiffler JS: D10S1423 identifies a susceptibility locus for Alzheimer’s disease in a prospective, longitudinal, double-blind study of asymptomatic individuals. Mol Psychiatry 2001;6:413–419.
- Badano JL, Leitch CC, Ansley SJ, May-Simera H, Lawson S, Lewis RA, et al: Dissection of epistasis in oligogenic Bardet-Biedl syndrome. Nature 2006;439:326–330.
- Krupa R, Blasiak J: An association of polymorphism of DNA repairs genes XRCC1 and XRCC3 with colorectal cancer. J Exp Clin Cancer Res 2004;23:285–294.
- Ye S, Dhillon S, Seear R, Dunleavey L, Day LB, Bannister W, Day INM, Simpson I: Epistatic interaction between variations in the angiotensin I converting enzyme and angiotensin II type 1 receptor genes in relation to extent of coronary atherosclerosis. Heart 2003;89:1195–1199.
- Carrasquillo MM, McCallion AS, Puffenberger EG, Kashuk CS, Nouri N, Chakravarti A: Genome-wide association study and mouse model identify interaction between RET and EDNRB pathways in Hirschsprung disease. Nat Genet 2002;32:237–244.
- Holm P, Rydlander B, Luthman H, Kockum I, for the European Consortium for IDDM Genome Studies: Interaction and association analysis of a type I diabetes susceptibility locus on chromosome 5q11-q13 and the 7q32 chromosomal region in Scandinavian families. Diabetes 2004;53:1584–1591.
- Ide A, Kawasaki E, Abiru N, Sun F, Kobayashi M, Fukushima T, et al: Association between IL-18 gene promoter polymorphisms and CTLA-4 gene 49A/G polymorphism in Japanese patients with type 1 diabetes. J Autoimmun 2004;22:73–78.
- Yang W-S, Hsiung CA, Ho L-T, Chen Y-T, He C-T, Curb JD, et al: Sapphire Study Group. 2003. Genetic epistasis of adiponectin and PPARγ2 genotypes in modulation of insulin sensitivity: A family-based association study. Diabetologia 2003;46:977–983.
- Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H, Ritchie MD, Delong GR, Abramson RK, Wright HH, Cuccaro ML, Hussman JP, Gilbert JR, Pericak-Vance MA: Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet 2005;77:377–388.
- Lai H-C, Lin W-Y, Lin Y-W, Chang C-C, Yu M-H, Chen C-C, Chu T-Y: Genetic polymorphisms of FAS and FASL (CD95/CD95L) genes in cervical carcinogenesis: an analysis of haplotype and gene-gene interaction. Gynecol Oncol 2005;99:113–118.
- Xiong M, Fan R, Jin L: Linkage disequilibrium mapping of quantitative trait loci under truncation selection. Hum Hered 2002;53:158–172.
- Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH: A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet Epidemiol 2006;30:111–123.
- Soares ML, Coelho T, Sousa A, Batalov S, Conceicao I, Sales-Luis ML, et al: Susceptibility and modifier genes in Portuguese transthyretin V30M amyloid polyneuropathy: complexity in a single-gene disease. Hum Mol Genet 2005;14:543–553.
- Tsai CT, Lai LP, Chiang FT, et al. Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation 2004;109:1640–1646.
- Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor-dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer. Am J Hum Genet 2001;69:138–147.
- Smith TR, Miller MS, Lohman K, Lange EM, Case LD, Mohrenweiser HW, Hu JJ: Polymorphisms for XRCC1 and XRCC3 genes and susceptibility to breast cancer. Cancer Lett 2003;190:183–190.
- Nelson MR, Kardia SL, Ferrell RE, Sing CF: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 2001;11:458–470.
- Putt W, Palmen J, Nicaud V, Tregouet D-A, Tahri-Daizadeh N, Flavell DM, et al: Variation in USF1 shows haplotypes effects, gene:gene and gene:environment associations with glucose and lipid parameters in the European Atherosclerosis Research Study II. Hum Mol Genet 2004;13:1587–1597.
- Becker T, Schumacher J, Cichon S, Baur MP, Knapp M: Haplotype interaction analysis of unlinked regions. Genet Epidemiol 2005;29:313–322.
- Qin S, Zhao X, Pan Y, Liu J, Feng G, Fu J, Bao J, Zhang Z, He L: An association study of the N-methyl-D-asparate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray. Eur J Hum Genet 2005;13:807–814.
- Cho YM, Ritchie MD, Moore JH, Moon MK, Lee YY, Yoon KH, et al: Multifactor dimensionality reduction reveals a two-locus interaction associated with type 2 diabetes mellitus. Diabetologia 2004;47:549–554.
- Searle SR: Linear models for unbalanced data. New York, Wiley and Sons, 1987.
- Agresti A: Categorical data analysis, 2nd edition. New York, Wiley, 2002.
- Rubin DB: Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. Ann Stat 1984;12:1151–1172.
- Gelman A, Meng X, Stern H: Posterior Predictive Assessment of Model Fitness via Realized Discrepancies (with discussion). Statistica Sinica 1996;6:733–807.
- Howard CG, Bock P: Using Hierarchical approach to avoid overfitting in early vision. IEEE 1994;826–829.
- Dudbridge F, Koeleman PC: Efficient computation of significance levels for multiple associations in large studies of correlated data, including genome-wide association studies. Am J Hum Genet 2004;75:424–435.
- Li J, Ji L: Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 2005;1–7.
- Westfall PH, Zaykin DH, Young SS: Multiple tests for genetic effects in association studies. Methods Mol Biol 2002;184:143–168.
- Storey JD, Tibshirani R: Statistical significance for genome-wide experiments. Proc Natl Acad Sci 2003;100:9440–9445.
- Holm S: A simple sequential rejective multiple test procedure. Scand J Stat 1979;6:65–70.
- Nakagawa S: Farewell to Bonferroni: the problems of low statistical power and publication bias. Behav Ecol 2004;15:1044–1045.
- Verhoeven KJF, Simonsen KL, McIntyre LM: Implementing false discovery rate control: increasing your power. Oikos 2005;108:643–647.
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B 1995;57:289–300.
- Benjamin Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001;29:1165–1188.
- Storey JD, Taylor JE, Siegmund D: Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: A unified approach. J R Stat Soc Ser B 2004;66:187–205.
- Yekutieli D, Benjamini Y: Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Inference 1999;82:171–196.
- Allison DB, Gadbury GL, Heo M, Fernandez JR, Leed C-K, Prollae TA, Weindruch R: A mixture model approach for the analysis of microarray gene expression data. Comp Stat Data Anal 2002;39:1–20.
- Gauderman WJ: Sample size requirements for association studies of gene × gene interaction. Am J Epidemiol 2002;155:478–484.
- Ionita I, Man M: Optimal two-stage strategy for detecting interacting genes in complex diseases. BMC Genet 2006; doi 10.1186/ 1471-2156-7-39.
- Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN: Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 2003;33:177–182.
- Florez JC, Hirschhorn J, Altshuler D: The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits. Ann Rev Genomics Hum Genet 2003;4:257–291.
- Daly MJ, Rioux JD: New approaches to gene hunting in IBD. Inflamm Bowel Dis 2004;10:312–317.
- Cheverud JM, Routman EJ: Epistasis and its contribution to genetic variance Components. Genetics 1995;139:1455–1461.
- Boerwinkle E, Sing CF: The use of measured genotype information in the analysis of quantitative phenotypes in man. III. Simultaneous estimation of the frequencies and effects of apolipoprotein E polymorphism and residual polygenetic effects on cholesterol, betapoliprotein and triglyceride levels. Ann Hum Genet 1987;51:211–226.
- Concato J, Feinstein AR, Holford TR: The risk of determining risk with multivariable models. Ann Intern Med 1993;118:201–210.
- Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR: A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–1379.
- Page GP, George V, Go RC, Page PZ, Allison DB: Are we there yet?’: Deciding when one has demonstrated specific genetic causation in complex diseases and quantitative traits. Am J Hum Genet 2003;73:711–719.
- Cockerham C: An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 1954;39:859–882.
- Wang T, Zeng Z-B: Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium. BMC Genetics 2006;7:9.
- Witte J: Gene × environment interaction; in Armitage P, Colton T (eds): Encyclopedia of Biostatistics. Chichester, Wiley, 1998, pp 1613–1614.
- Greenland S, Rothman KJ: Concepts of interaction; in Modern Epidemiology, ed. 2. Philadelphia, Lippincott-Raven 1998, pp 329–342.
- Clark AG: Limits to prediction of phenotypes from knowledge of genotypes; in Clegg MT (ed): Evolutionary Biology. New York, Plenum, 2002;32:205–224.
- Bradley JV: Distribution free statistical tests. Englewood Cliffs, Prentice Hall, 1968, p 15.
- Frawley W, Piatetsky-Shapiro G, Matheus C: Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pp 213–228.
- Hand D, Mannila H, Smyth P: Principles of Data Mining. Cambridge, MA, MIT Press, 2001.
- North VN, Curtis D, Sham PC: Application of logistic regression to case-control association studies involving two causative loci. Hum Hered 2005;59:79–87.
- Todd JA: Statistical false positive or true disease pathway? Nat Genet 2006;38:731–733.
- Tibshirani R: Regression shrinkage and selection via the Lasso. J R Stat Soc 1996;58:267–288.
- Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L: Sequence Analysis using Logic Regression. Genet Epidemiol 2001;21(suppl 1):S626–S631.
- Hoh J, Willie M, Zee R, Cheng S, Reynolds R, Lindpaintner K, Ott J: Selecting SNPs in two-stage analysis of disease association data: A model-free approach. Ann Hum Genet 2000;64:413–417.
- Millstein J, Conti DV, Gililand FD, Gauderman WJ: A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.
- Kooperberg C, Ruczinski I: Identifying Interacting SNPs Using Monte Carlo Logic Regression. Genet Epidemiol 2005;28:157–170.
- Zee RYL, Hoh J, Cheng S, Reynolds R, Grow MA, Silbergleit A, et al: Multi-locus interactions predict risk for post-PTCA restenosis: An approach to the genetic analysis of common complex disease. Pharmacogenomics J 2002;2:197–201.
- Daly MJ, Altschuler D: Partners in crime. Nat Genet 2005;37:337–338.
- Moore JH, Lamb JM, Brown NJ, Vaughan DE: A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels. Clin Genet 2002;62:74–79.
- Moore JH, Smolkin ME, Lamb JM, Brown NJ, Vaughan DE: The relationship between plasma t-PA and PAI-1 levels is dependent on epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms. Clin Genet 2002;62:53–59.
- Moore JH: Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.
- Ritchie MD, Hahn LW, and 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;24:150–157.
- Moore JH: Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.
- Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene × gene and gene × environment interactions. Bioinformatics 2003;19:376–382.
- Moore JH, Hahn LW: A cellular automata approach to detecting interactions among single-nucleotide polymorphisms in complex multifactorial diseases. Pac Symp Biocomp 2002;7:53–64.
- Sherriff A, Ott J: Application of neural networks for gene finding. Adv Genet 2001;42:287–297.
- Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH: Optimization of neural network architecture using genetic programming modeling improves detection and modeling of gene × gene interactions in studies of human diseases. BMC Bioinformatics 2003;28:4.
- Motsinger AA, Lee SL, Mellick G, Ritchie MD: GPNN: Power studies and applications of a neural network method for detecting gene gene interactions in studies of human diseases. BMC Bionformations 2006;7:39–49.
- Congdon CB, Sing CF, Reilly SL: Genetic algorithms for identifying combinations of genes and other risk factors associated with coronary artery disease. In Proceed Workshop Artif Intell & Genome, Chambery 1993, pp 107–117.
- Curtis D, North BV, Sham PC: Use of artificial neural network to detect association between disease and multiple marker genotypes. Ann Hum Genet 2001;65:95–107.
- Bush WS, Motsinger AA, Dudek SM, Ritchie MD: Can neural network constraints in GP provide power to detect genes associated with human disease? Appl Evol Comp Proceed 2005;3449:44–53.
- Ritchie MD, Coffey CS, Moore JH: Genetic programming neural networks as a bioinformatics tool for human genetics. Genet Evol Comp – GECCO 2004;proceed 3102:438–448.
- Motsinger AA, Dudek SM, Hahn LW, Ritchie MD: Comparison of neural network optimization for studies of human genetics. Lect Notes Comp Sci 2006;3907:103–114.
- Tomita Y, Tomida S, Hasegawa Y, Suzuki Y, Shirakwa T, Kobayashi T, Honda Y: Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma. BMC Bionformatics 2004;5:120–133.
- Utans J, Moody J: Selecting neural network architectures via the prediction risk application to corporate bond rating prediction. Conf proceed on the 1st international conf on artificial intelligence applications on Wall Street 1991.
- Evans DM, Marchini J, Morris AP, Cardon LR: Two-stage two-locus models in genome-wide association. PLoS Genet 2006;2:1424–1432.
- Andrew AS, Nelson HH, Kelsey KT, Moore JH, Meng AC, Casella DP, et al: Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. Carcinogenesis 2006;27:1030–1037.
- Coffey CS, Hebert PR, Ritchie MD, Krumholz HM, Gaziano JM, Ridker PM, Brown NJ, Vaughan DE, Moore JH: An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation. BMC Bioinformatics 2004;5:49.
- Motsinger AA, Ritchie MD: The effect of reduction in cross-validation Intervals on the performance of multifactor dimensionality reduction. Genet Epidemiol 2006;DOI 10.1002/gepi.20166.
- Hoh J, Wille A, Ott J: Trimming, Weighting, and Grouping SNPs in Human Case-Control association studies. Genome Res 2001;11:2115–2119.
- Wille A, Hoh J, Ott J: Sum statistics for the joint detection of multiple disease loci in case-control association studies with SNP markers. Genet Epidemiol 2003;25:350–359.
- Redden DT, Divers J, Vaughan LK, Tiwari HK, Beasley TM, Fernandez JR, Kimberly RP, Feng R, Padilla MA, Liu N, Miller MB, Allison DB: Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLoS Genet 2006;2:e137.
Solomon K. Musani
The Section on Statistical Genetics, Ryals Public Health Building Suite 517A
Department of Biostatistics, University of Alabama at Birmingham
Birmingham, AL 35294 (USA)
Tel. +1 205 975 9213, Fax +1 205 975 2540, E-Mail SMusani@ms.soph.uab.edu
Published online: February 2, 2007
Number of Print Pages : 18
Number of Figures : 1, Number of Tables : 1, Number of References : 138
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 63, No. 2, Year 2007 (Cover Date: February 2007)
Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (print), 1423–0062 (Online)
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