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Statistical Applications in Genetics and Molecular Biology

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Volume 12, Issue 3 (Jun 2013)


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Genetic model selection in genome-wide association studies: robust methods and the use of meta-analysis

Pantelis G. Bagos
  • Corresponding author
  • Department of Computer Science and Biomedical Informatics, University of Central Greece, Papasiopoulou 2-4, Lamia 35100, Greece
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-04-24 | DOI: https://doi.org/10.1515/sagmb-2012-0016


In genetic association studies (GAS) as well as in genome-wide association studies (GWAS), the mode of inheritance (dominant, additive and recessive) is usually not known a priori. Assuming an incorrect mode of inheritance may lead to substantial loss of power, whereas on the other hand, testing all possible models may result in an increased type I error rate. The situation is even more complicated in the meta-analysis of GAS or GWAS, in which individual studies are synthesized to derive an overall estimate. Meta-analysis increases the power to detect weak genotype effects, but heterogeneity and incompatibility between the included studies complicate things further. In this review, we present a comprehensive summary of the statistical methods used for robust analysis and genetic model selection in GAS and GWAS. We then discuss the application of such methods in the context of meta-analysis. We describe the theoretical properties of the various methods and the foundations on which they are based. We also present the available software implementations of the described methods. Finally, since only few of the available robust methods have been applied in the meta-analysis setting, we present some simple extensions that allow robust meta-analysis of GAS and GWAS. Possible extensions and proposals for future work are also discussed.

Keywords: meta-analysis; GWAS; robust methods; genetic model selection; genetic association


  • Armitage, P. (1955): “Tests for linear trends in proportions and frequencies,” Biometrics, 11, 375–386.CrossrefGoogle Scholar

  • Bagos, P. G. (2008): “A unification of multivariate methods for meta-analysis of genetic association studies,” Stat. Appl. Genet Mol. Biol., 7, Article31.PubMedGoogle Scholar

  • Bagos, P. G. (2011): “Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature,” BMC Genet, 12, 8.CrossrefPubMedGoogle Scholar

  • Bagos, P. G. (2012): “On the covariance of two correlated log-odds ratios,” Stat. Med., 31, 1418–1431.PubMedCrossrefGoogle Scholar

  • Bagos, P. G. and G. K. Nikolopoulos (2007): “A method for meta-analysis of case-control genetic association studies using logistic regression,” Stat. Appl. Genet Mol. Biol., 6, Article17.PubMedGoogle Scholar

  • Balding, D. J. (2006): “A tutorial on statistical methods for population association studies,” Nat. Rev. Genet, 7, 781–791.CrossrefPubMedGoogle Scholar

  • Becker, K. G., K. C. Barnes, T. J. Bright and S. A. Wang (2004): “The genetic association database,” Nat. Genet, 36, 431–432.CrossrefPubMedGoogle Scholar

  • Begum, F., D. Ghosh, G. C. Tseng and E. Feingold (2012): “Comprehensive literature review and statistical considerations for GWAS meta-analysis,” Nucleic Acids Res., 40, 3777–3784.CrossrefPubMedGoogle Scholar

  • Benyamin, B., P. M. Visscher and A. F. McRae (2009): “Family-based genome-wide association studies,” Pharmacogenomics, 10, 181–190.PubMedCrossrefGoogle Scholar

  • Berkey, C. S., D. C. Hoaglin, A. Antczak-Bouckoms, F. Mosteller and G. A. Colditz (1998): “Meta-analysis of multiple outcomes by regression with random effects,” Stat. Med., 17, 2537–2550.PubMedCrossrefGoogle Scholar

  • Burton, P. R., M. D. Tobin and J. L. Hopper (2005): “Key concepts in genetic epidemiology,” Lancet, 366, 941–951.PubMedCrossrefGoogle Scholar

  • Chalmers, T. C., J. Berrier, H. S. Sacks, H. Levin, D. Reitman and R. Nagalingam (1987): “Meta-analysis of clinical trials as a scientific discipline. II: Replicate variability and comparison of studies that agree and disagree,” Stat. Med., 6, 733–744.CrossrefPubMedGoogle Scholar

  • Chen, H. Y. (2003): “A note on the prospective analysis of outcome-dependent samples,” J. Roy. Soc. B, 65, 575–584.Google Scholar

  • Chen, J. and N. Chatterjee (2007): “Exploiting Hardy-Weinberg equilibrium for efficient screening of single SNP associations from case-control studies,” Hum. Hered., 63, 196–204.CrossrefPubMedGoogle Scholar

  • Chen, W. M. and G. R. Abecasis (2007): “Family-based association tests for genomewide association scans,” Am. J. Hum. Genet., 81, 913–926.Google Scholar

  • Clerget-Darpoux, F., C. Bonaiti-Pellie and J. Hochez (1986): “Effects of misspecifying genetic parameters in lod score analysis. Biometrics, 42, 393–399.CrossrefGoogle Scholar

  • Cochran, W. G. (1954): “Some methods for strengthening the common chi-squared tests,” Biometrics, 10, 417–451.CrossrefGoogle Scholar

  • Cordell, H. J. and D. G. Clayton (2005): “Genetic association studies,” Lancet, 366, 1121–1131.PubMedCrossrefGoogle Scholar

  • de Bakker, P. I., M. A. Ferreira, X. Jia, B. M. Neale, S. Raychaudhuri and B. F. Voight (2008): “Practical aspects of imputation-driven meta-analysis of genome-wide association studies,” Hum. Mol. Genet., 17, R122–R128.Google Scholar

  • DerSimonian, R. and N. Laird (1986): “Meta-analysis in clinical trials,”. Control. Clin. Trials, 7, 177–188.CrossrefPubMedGoogle Scholar

  • Edwardes, M. D. and M. Baltzan (2000): “The generalization of the odds ratio, risk ratio and risk difference to r x k tables,” Stat. Med., 19, 1901–1914.CrossrefGoogle Scholar

  • Evangelou, E., D. M. Maraganore and J. P. Ioannidis (2007): “Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease,” PLoS One, 2, e196.Google Scholar

  • Falconer, D. S. (1960): Introduction to quantitative genetics, Edinburgh/London: Oliver & Boyd.Google Scholar

  • Fisher, R. A. (1928): “The possible modification of the response of the wild type to recurrent mutations,” Am. Nat., 62, 115–126.CrossrefGoogle Scholar

  • Fisher, R. A. (1931): “The evolution of dominance,” Biol. Rev., 6, 345–368.CrossrefGoogle Scholar

  • Freidlin, B., M. J. Podgor and J. L. Gastwirth (1999): “Efficiency robust tests for survival or ordered categorical data,” Biometrics, 55, 883–886.PubMedGoogle Scholar

  • Freidlin, B., G. Zheng, Z. Li and J. L. Gastwirth (2002): “Trend tests for case-control studies of genetic markers: power, sample size and robustness,” Hum. Hered., 53, 146–152.CrossrefPubMedGoogle Scholar

  • Gastwirth, J. L. (1985): “The use of maximin efficiency robust tests in combining contingency tables and survival analysis,” J. Am. Stat. Assoc., 80, 380–384.CrossrefGoogle Scholar

  • Glass, G. (1976): “Primary, secondary and meta-analysis of research,” Educ. Res., 5, 3–8.Google Scholar

  • Gonzalez, J. R., L. Armengol, X. Sole, E. Guino, J. M. Mercader, X. Estivill and V. Moreno (2007): “SNPassoc: an R package to perform whole genome association studies,” Bioinformatics, 23, 644–645.Google Scholar

  • Gonzalez, J. R., J. L. Carrasco, F. Dudbridge, L. Armengol, X. Estivill and V. Moreno (2008): “Maximizing association statistics over genetic models,” Genet. Epidemiol., 32, 246–254.PubMedCrossrefGoogle Scholar

  • Greenland, S. (1998): Meta-analysis. In: Rothman, K. J. and Greenland, S. (Eds.), Modern Epidemiology, Lippincott Williams & Wilkins, Philadelphia, pp. 643–673.Google Scholar

  • Guan, Y. and M. Stephens (2008): “Practical issues in imputation-based association mapping,” PLoS Genet, 4, e1000279.CrossrefGoogle Scholar

  • Hao, K., E. Chudin, J. McElwee and E. E. Schadt (2009): “Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies,” BMC Genet, 10, 27.CrossrefPubMedGoogle Scholar

  • Higgins, J. P. and A. Whitehead (1996): “Borrowing strength from external trials in a meta-analysis,” Stat. Med., 15, 2733–2749.CrossrefGoogle Scholar

  • Higgins, J. P., A. Whitehead, R. M. Turner, R. Z. Omar and S. G. Thompson (2001): “Meta-analysis of continuous outcome data from individual patients,” Stat. Med., 20, 2219–2241.CrossrefPubMedGoogle Scholar

  • Hindorff, L. A., P. Sethupathy, H. A. Junkins, E. M. Ramos, J. P. Mehta, F. S. Collins and T. A. Manolio (2009): “Potential etiologic and functional implications of genome-wide association loci for human diseases and traits,” Proc. Natl. Acad. Sci. USA, 106, 9362–9367CrossrefGoogle Scholar

  • Hirschhorn, J. N., K. Lohmueller, E. Byrne and K. Hirschhorn (2002): “A comprehensive review of genetic association studies,” Genet. Med., 4, 45–61.PubMedCrossrefGoogle Scholar

  • Hothorn, L. A. and T. Hothorn (2009): “Order-restricted scores test for the evaluation of population-based case-control studies when the genetic model is unknown,” Biom. J., 51, 659–669.PubMedGoogle Scholar

  • Ioannidis, J. P. (2008): “Calibration of credibility of agnostic genome-wide associations,” Am. J. Med. Genet. B Neuropsychiatr. Genet., 147B, 964–972.Google Scholar

  • Ioannidis, J. P., N. A. Patsopoulos and E. Evangelou (2007): “Heterogeneity in meta-analyses of genome-wide association investigations,” PLoS One, 2, e841.Google Scholar

  • Jackson, D., I. R. White and S. G. Thompson (2010): “Extending DerSimonian and Laird’s methodology to perform multivariate random effects meta-analyses,” Stat. Med., 29, 1282–1297.Google Scholar

  • Jackson, D., R. Riley and I. R. White (2011): “Multivariate meta-analysis: potential and promise,” Stat. Med., 30, 2481–2498Google Scholar

  • Janssens, A. C., Ladd A. M. Gonzalez-Zuloeta, S. Lopez-Leon, J. P. Ioannidis, B. A. Oostra, M. J. Khoury and C. M. van Duijn (2009): “An empirical comparison of meta-analyses of published gene-disease associations versus consortium analyses,” Genet. Med., 11, 153–162.CrossrefGoogle Scholar

  • Joo, J., M. Kwak, K. Ahn and G. Zheng (2009): “A robust genome-wide scan statistic of the Wellcome Trust Case-Control Consortium,” Biometrics, 65, 1115–1122.CrossrefGoogle Scholar

  • Joo, J., M. Kwak, Z. Chen and G. Zheng (2010a): “Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty,” Stat. Med., 29, 158–180.PubMedGoogle Scholar

  • Joo, J., M. Kwak and G. Zheng (2010b): “Improving power for testing genetic association in case-control studies by reducing the alternative space,” Biometrics, 66, 266–276.CrossrefPubMedGoogle Scholar

  • Kacser, H. and J. A. Burns (1981): “The molecular basis of dominance,” Genetics, 97, 639–666.PubMedGoogle Scholar

  • Klein, R. J., C. Zeiss, E. Y. Chew, J. Y. Tsai, R. S. Sackler, C. Haynes, A. K. Henning, J. P. SanGiovanni, S. M. Mane, S. T. Mayne, M. B. Bracken, F. L. Ferris, J. Ott, C. Barnstable and J. Hoh (2005): “Complement factor H polymorphism in age-related macular degeneration,” Science, 308, 385–389.CrossrefPubMedGoogle Scholar

  • Langefeld, C. D. and T. E. Fingerlin (2007): “Association methods in human genetics,” Methods Mol. Biol., 404, 431–460.CrossrefPubMedGoogle Scholar

  • Lee, W. C. (2003): “Searching for disease-susceptibility loci by testing for Hardy-Weinberg disequilibrium in a gene bank of affected individuals,” Am. J. Epidemiol., 158, 397–400.CrossrefGoogle Scholar

  • Lee, W. C. (2004): “Case-control association studies with matching and genomic controlling,” Genet. Epidemiol., 27, 1–13.CrossrefPubMedGoogle Scholar

  • Lettre, G., C. Lange and J. N. Hirschhorn (2007): “Genetic model testing and statistical power in population-based association studies of quantitative traits,” Genet. Epidemiol., 31, 358–362.CrossrefPubMedGoogle Scholar

  • Lewis, C. M. (2002): “Genetic association studies: design, analysis and interpretation,” Brief Bioinform., 3, 146–153.CrossrefPubMedGoogle Scholar

  • Li, Q., G. Zheng, Z. Li and K. Yu (2008): “Efficient approximation of P-value of the maximum of correlated tests, with applications to genome-wide association studies,” Ann. Hum. Genet., 72, 397–406.PubMedCrossrefGoogle Scholar

  • Lin, D. Y. and D. Zeng (2010a): “Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data,” Genet. Epidemiol., 34, 60–66.PubMedGoogle Scholar

  • Lin, Y. and D. Zeng (2010b): “On the relative efficiency of using summary statistics versus individual-level data in meta-analysis,” Biometrika, 97, 321–332.CrossrefPubMedGoogle Scholar

  • Lopez-Bigas, N., B. J. Blencowe and C. A. Ouzounis (2006): “Highly consistent patterns for inherited human diseases at the molecular level,” Bioinformatics, 22, 269–277.CrossrefGoogle Scholar

  • Lu, G. and A. E. Ades (2004): “Combination of direct and indirect evidence in mixed treatment comparisons,” Stat. Med., 23, 3105–3124.CrossrefPubMedGoogle Scholar

  • Magi, R. and A. P. Morris (2010): “GWAMA: software for genome-wide association meta-analysis,” BMC Bioinformatics, 11, 288.PubMedCrossrefGoogle Scholar

  • Manolio, T. A. (2010): “Genomewide association studies and assessment of the risk of disease,” N Engl. J. Med., 363, 166–176.Google Scholar

  • Mathew, T. and K. Nordstrom (1999): “On the equivalence of meta-analysis using literature and using individual patient data,” Biometrics, 55, 1221–1223.PubMedCrossrefGoogle Scholar

  • Minelli, C., J. R. Thompson, K. R. Abrams and P. C. Lambert (2005a): “Bayesian implementation of a genetic model-free approach to the meta-analysis of genetic association studies,” Stat. Med., 24, 3845–3861.PubMedCrossrefGoogle Scholar

  • Minelli, C., J. R. Thompson, K. R. Abrams, A. Thakkinstian and J. Attia (2005b): “The choice of a genetic model in the meta-analysis of molecular association studies,” Int. J. Epidemiol, 34, 1319–1328.PubMedCrossrefGoogle Scholar

  • Misra, R. K. (1968): “245. Note: statistical tests of hypotheses concerning the degree of dominance in monofactorial inheritance. Biometrics, 24, 429–434.CrossrefGoogle Scholar

  • Miyagawa, T., N. Nishida, J. Ohashi, R. Kimura, A. Fujimoto, M. Kawashima, A. Koike, T. Sasaki, H. Tanii, T. Otowa, Y. Momose, Y. Nakahara, J. Gotoh, Y. Okazaki, S. Tsuji and K. Tokunaga (2008): “Appropriate data cleaning methods for genome-wide association study,” J. Hum. Genet., 53, 886–893.Google Scholar

  • Moonesinghe, R., M. J. Khoury, T. Liu and J. P. Ioannidis (2008): “Required sample size and nonreplicability thresholds for heterogeneous genetic associations,” Proc. Natl. Acad. Sci. USA, 105, 617–622.CrossrefGoogle Scholar

  • Muller, H. H., R. Pahl and H. Schafer (2007): “Including sampling and phenotyping costs into the optimization of two stage designs for genomewide association studies,” Genet. Epidemiol., 31, 844–852.CrossrefPubMedGoogle Scholar

  • Nakaoka, H. and I. Inoue (2009): “Meta-analysis of genetic association studies: methodologies, between-study heterogeneity and winner’s curse,” J. Hum. Genet., 54, 615–623.Google Scholar

  • Nguyen, T. T., R. Pahl and H. Schafer (2009): “Optimal robust two-stage designs for genome-wide association studies,” Ann. Hum. Genet., 73, 638–651.PubMedCrossrefGoogle Scholar

  • Nikolopoulos, G. K., P. G. Bagos and S. Bonovas (2011): “Developing the evidence base for cancer chemoprevention: use of meta-analysis,” Curr. Drug Targets, 12, 1989–1997.CrossrefPubMedGoogle Scholar

  • Normand, S. L. (1999): “Meta-analysis: formulating, evaluating, combining, and reporting,” Stat Med, 18, 321–359.PubMedCrossrefGoogle Scholar

  • Olkin, I. and Sampson, A. (1998): “Comparison of meta-analysis versus analysis of variance of individual patient data,” Biometrics, 54, 317–322.CrossrefPubMedGoogle Scholar

  • Pan, D., Q. Li, N. Jiang, A. Liu and K. Yu (2011): “Robust joint analysis allowing for model uncertainty in two-stage genetic association studies,” BMC Bioinformatics, 12, 9.PubMedCrossrefGoogle Scholar

  • Panagiotou, O. A., E. Evangelou and J. P. Ioannidis (2010): “Genome-wide significant associations for variants with minor allele frequency of 5% or less—an overview: A HuGE review,” Am. J. Epidemiol., 172, 869–889CrossrefPubMedGoogle Scholar

  • Pereira, T. V. and R. C. Mingroni-Netto (2011): “A note on the use of the generalized odds ratio in meta-analysis of association studies involving bi- and tri-allelic polymorphisms,” BMC Res. Notes, 4, 172.CrossrefPubMedGoogle Scholar

  • Pereira, T. V., N. A. Patsopoulos, A. C. Pereira and J. E. Krieger (2011): “Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication,” Int J Epidemiol, 40, 457–469.PubMedCrossrefGoogle Scholar

  • Petiti, D. B. (1994) Meta-analysis decision analysis and cost-effectiveness analysis, Oxford University Press, New York.Google Scholar

  • Prentice, R. L. and R. Pyke (1979): “Logistic disease incidence models and case-control studies,” Biometrika, 66, 403–411.CrossrefGoogle Scholar

  • Purcell, S., B. Neale, K. Todd-Brown, L. Thomas, M. A. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. de Bakker, M. J. Daly and P. C. Sham (2007): “PLINK: a tool set for whole-genome association and population-based linkage analyses,” Am. J. Hum. Genet., 81, 559–575.Google Scholar

  • Sacks, H. S., J. Berrier, D. Reitman, V. A. Ancona-Berk and T. C. Chalmers (1987): “Meta-analyses of randomized controlled trials,” N Engl. J. Med., 316, 450–455.CrossrefGoogle Scholar

  • Salanti, G. and J. P. Higgins (2008): “Meta-analysis of genetic association studies under different inheritance models using data reported as merged genotypes,” Stat. Med., 27, 764–777.CrossrefPubMedGoogle Scholar

  • Salanti, G., L. Southam, D. Altshuler, K. Ardlie, I. Barroso, M. Boehnke, M. C. Cornelis, T. M. Frayling, H. Grallert, N. Grarup, L. Groop, T. Hansen, A. T. Hattersley, F. B. Hu, K. Hveem, T. Illig, J. Kuusisto, M. Laakso, C. Langenberg, V. Lyssenko, M. I. McCarthy, A. Morris, A. D. Morris, C. N. Palmer, F. Payne, C. G. Platou, L. J. Scott, B. F. Voight, N. J. Wareham, E. Zeggini and J. P. Ioannidis (2009): “Underlying genetic models of inheritance in established type 2 diabetes associations,” Am. J. Epidemiol., 170, 537–545.PubMedCrossrefGoogle Scholar

  • Sasieni, P. D. (1997): “From genotypes to genes: doubling the sample size,” Biometrics, 53, 1253–1261.PubMedCrossrefGoogle Scholar

  • Schaid, D. J. and S. S. Sommer (1994): “Comparison of statistics for candidate-gene association studies using cases and parents,” Am. J. Hum. Genet., 55, 402–409.Google Scholar

  • Skol, A. D., L. J. Scott, G. R. Abecasis and M. Boehnke (2006): “Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies,” Nat. Genet., 38, 209–213.CrossrefPubMedGoogle Scholar

  • So, H. C. and P. C. Sham (2011): “Robust association tests under different genetic models, allowing for binary or quantitative traits and covariates,” Behav. Genet., 41, 768–775.CrossrefPubMedGoogle Scholar

  • Song, K. and R. C. Elston (2006): “A powerful method of combining measures of association and Hardy-Weinberg disequilibrium for fine-mapping in case-control studies,” Stat. Med., 25, 105–126.PubMedCrossrefGoogle Scholar

  • Spielman, R. S. and W. J. Ewens (1996): “The TDT and other family-based tests for linkage disequilibrium and association,” Am. J. Hum. Genet., 59, 983–989.Google Scholar

  • Spielman, R. S., R. E. McGinnis and W. J. Ewens (1993): “Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM),” Am. J. Hum. Genet., 52, 506–516.Google Scholar

  • Strasser, H. and C. Weber (1999): “On the asymptotic theory of permutation statistics,” Math. Method Stat., 8, 220–250.Google Scholar

  • Stroup, D. F., J. A. Berlin, S. C. Morton, I. Olkin, G. D. Williamson, D. Rennie, D. Moher, B. J. Becker, T. A. Sipe and S. B. Thacker (2000): “Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. J. Am. Med. Assoc., 283, 2008–2012.Google Scholar

  • Teo, Y. Y. (2008): “Common statistical issues in genome-wide association studies: a review on power, data quality control, genotype calling and population structure,” Curr. Opin. Lipidol., 19, 133–143.PubMedCrossrefGoogle Scholar

  • Thakkinstian, A., P. McElduff, C. D’Este, D. Duffy and J. Attia (2005): “A method for meta-analysis of molecular association studies,” Stat. Med., 24, 1291–1306.CrossrefPubMedGoogle Scholar

  • Thompson, S. G. and S. J. Sharp (1999): “Explaining heterogeneity in meta-analysis: a comparison of methods,” Stat. Med., 18, 2693–2708.CrossrefPubMedGoogle Scholar

  • Trikalinos, T. A., G. Salanti, E. Zintzaras and J. P. Ioannidis (2008): “Meta-analysis methods,” Adv. Genet., 60, 311–334.PubMedCrossrefGoogle Scholar

  • Turner, R. M., R. Z. Omar, M. Yang, H. Goldstein and S. G. Thompson (2000): “A multilevel model framework for meta-analysis of clinical trials with binary outcomes,” Stat. Med., 19, 3417–3432.PubMedCrossrefGoogle Scholar

  • van Houwelingen, H. C., K. H. Zwinderman and T. Stijnen (1993): “A bivariate approach to meta-analysis,” Stat. Med., 12, 2273–2284.CrossrefGoogle Scholar

  • Wang, J. Y. and J. J. Tai (2009): “Robust quantitative trait association tests in the parent-offspring triad design: conditional likelihood-based approaches,” Ann. Hum. Genet., 73, 231–244.CrossrefPubMedGoogle Scholar

  • Wheeler, E. and Barroso, I. (2011): “Genome-wide association studies and type 2 diabetes,” Brief Funct. Genomics, 10, 52–60.PubMedCrossrefGoogle Scholar

  • White, I. R. (2009): “Multivariate random-effects meta-analysis,” Stat. J., 9, 40–56.Google Scholar

  • Willer, C. J., Y. Li and G. R. Abecasis (2010): “METAL: fast and efficient meta-analysis of genomewide association scans,” Bioinformatics, 26, 2190–2191.PubMedCrossrefGoogle Scholar

  • Wittke-Thompson, J. K., A. Pluzhnikov and N. J. Cox (2005): “Rational inferences about departures from Hardy-Weinberg equilibrium,” Am. J. Hum. Genet., 76, 967–986.Google Scholar

  • Wright, S. (1934): “Physiological and evolutionary theories of dominance,” Am. Nat., 68, 24–53.CrossrefGoogle Scholar

  • WTCCC. (2007): “Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls,” Nature, 447, 661–678.Google Scholar

  • Yuan, M., X. Tian, G. Zheng and Y. Yang (2009): “Adaptive transmission disequilibrium test for family trio design,” Stat. Appl. Genet. Mol. Biol., 8, Article30.PubMedGoogle Scholar

  • Zang, Y. and W. K. Fung (2010): “Robust tests for matched case-control genetic association studies,” BMC Genet., 11, 91.PubMedCrossrefGoogle Scholar

  • Zang, Y. and W. K. Fung (2011): “Robust Mantel-Haenszel test under genetic model uncertainty allowing for covariates in case-control association studies,” Genet. Epidemiol., 35, 695–705.CrossrefPubMedGoogle Scholar

  • Zang, Y., W. K. Fung and G. Zheng (2010): “Simple algorithms to calculate asymptotic null distribution for robust tests in case-control genetic association studies in R,” J. Stat. Software 33.Google Scholar

  • Zeggini, E. and J. P. Ioannidis (2009): “Meta-analysis in genome-wide association studies,” Pharmacogenomics, 10, 191–201.PubMedCrossrefGoogle Scholar

  • Zheng, G. and X. Tian (2006): “Robust trend tests for genetic association using matched case-control design,” Stat. Med., 25, 3160–3173.CrossrefPubMedGoogle Scholar

  • Zheng, G. and H. K. Ng (2008): “Genetic model selection in two-phase analysis for case-control association studies,” Biostatistics, 9, 391–399.PubMedCrossrefGoogle Scholar

  • Zheng, G., B. Freidlin and J. L. Gastwirth (2002): “Robust TDT-type candidate-gene association tests,” Ann. Hum. Genet., 66, 145–155.CrossrefPubMedGoogle Scholar

  • Zheng, G., B. Freidlin, Z. Li and J. L. Gastwirth (2003): “Choice of scores in trend tests for case-control studies of candidate-gene associations,” Biometrical J., 45, 335–348.CrossrefGoogle Scholar

  • Zheng, G., B. Freidlin and J. L. Gastwirth (2006a): “Comparison of robust tests for genetic association using case-control studies,” IMS Lecture Notes-Monograph Series, 49, 253–265.Google Scholar

  • Zheng, G., B. Freidlin and J. L. Gastwirth (2006b): “Robust genomic control for association studies,” Am. J. Hum. Genet., 78, 350–356.Google Scholar

  • Zheng, G., M. Meyer, W. Li and Y. Yang (2008): “Comparison of two-phase analyses for case-control genetic association studies,” Stat. Med., 27, 5054–5075.PubMedCrossrefGoogle Scholar

  • Zheng, G., J. Joo and Y. Yang (2009a): “Pearson’s test, trend test, and MAX are all trend tests with different types of scores,” Ann. Hum. Genet., 73, 133–140.CrossrefGoogle Scholar

  • Zheng, G., J. Joo, D. Zaykin, C. Wu and N. Geller (2009b): “Robust tests in genome-wide scans under incomplete linkage disequilibrium,” Stat. Sci., 24, 503–516.CrossrefGoogle Scholar

  • Zhou, B., J. Shi and A. S. Whittemore (2011): “Optimal methods for meta-analysis of genome-wide association studies,” Genet. Epidemiol., 35, 581–591.CrossrefPubMedGoogle Scholar

  • Ziegler, A., I. R. Konig and J. R. Thompson (2008): “Biostatistical aspects of genome-wide association studies,” Biom. J., 50, 8–28.PubMedGoogle Scholar

  • Zintzaras, E. (2010): “The generalized odds ratio as a measure of genetic risk effect in the analysis and meta-analysis of association studies,” Stat. Appl. Genet. Mol. Biol., 9, Article21.PubMedGoogle Scholar

  • Zintzaras, E. and Santos, M. (2011): “Estimating the mode of inheritance in genetic association studies of qualitative traits based on the degree of dominance index,” BMC Med. Res. Method., 11, 171.CrossrefGoogle Scholar

  • Zuo, Y., G. Zou and H. Zhao (2006): “Two-stage designs in case-control association analysis,” Genetics, 173, 1747–1760.CrossrefPubMedGoogle Scholar

About the article

Corresponding author: Pantelis G. Bagos, Department of Computer Science and Biomedical Informatics, University of Central Greece, Papasiopoulou 2-4, Lamia 35100, Greece

Published Online: 2013-04-24

Published in Print: 2013-06-01

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2012-0016.

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