Detection of interacting risk factors for complex traits is challenging. The choice of an appropriate method, sample size, and allocation of cases and controls are serious concerns. To provide empirical guidelines for planning such studies and data analyses, we investigated the performance of the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) methods under various experimental scenarios. We developed the mathematical expectation of accuracy and used it as an indicator parameter to perform a gene-gene interaction study. We then examined the statistical power of GMDR and MDR within the plausible range of accuracy (0.50∼0.65) reported in the literature. The GMDR with covariate adjustment had a power of>80% in a case-control design with a sample size of≥2000, with theoretical accuracy ranging from 0.56 to 0.62. However, when the accuracy was<0.56, a sample size of≥4000 was required to have sufficient power. In our simulations, the GMDR outperformed the MDR under all models with accuracy ranging from 0.56∼0.62 for a sample size of 1000–2000. However, the two methods performed similarly when the accuracy was outside this range or the sample was significantly larger. We conclude that with adjustment of a covariate, GMDR performs better than MDR and a sample size of 1000∼2000 is reasonably large for detecting gene-gene interactions in the range of effect size reported by the current literature; whereas larger sample size is required for more subtle interactions with accuracy<0.56.
Citation: Chen G-B, Xu Y, Xu H-M, Li MD, Zhu J, Lou X-Y (2011) Practical and Theoretical Considerations in Study Design for Detecting Gene-Gene Interactions Using MDR and GMDR Approaches. PLoS ONE 6(2): e16981. https://doi.org/10.1371/journal.pone.0016981
Editor: Thomas Mailund, Aarhus University, Denmark
Received: August 27, 2010; Accepted: January 19, 2011; Published: February 28, 2011
Copyright: © 2011 Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded in part by the National Institutes of Health Grants DA025095, DA12844, GM081488, DK080100, and the National Science Foundation of China 30571131. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Complex traits are controlled by multiple genetic factors working in concert and responding to the environment. Although the exact inheritance mechanisms of such traits are largely unknown, it is commonly accepted that there are interactions of numerous biological processes, which contribute, directly or indirectly, to phenotypes , . These genetic mechanisms differ from those of conventional Mendelian traits in several ways: (1) multiple genes are involved , ; (2) the roles of the genes are defined in the context of their related genes; and (3) the magnitude of the gene effects depends on the environment to which they are exposed . A major achievement in detecting epistasis for complex traits is the development of constructive induction approaches , including the multifactor dimensionality reduction method (MDR) , , , the combinatorial partitioning method (CPM) , and the restricted partition method (RPM) . The MDR is a powerful approach to detect gene-gene (G×G) interactions and ideally discriminates between discrete clinical endpoints when using multilocus genotypes . To circumvent the weaknesses of existing MDR approaches , we previously developed a generalized MDR (GMDR) statistical framework applicable to both dichotomous and quantitative phenotypes that allows adjustment for covariates in population-based study designs . We then extended our approach to family-based designs with pedigree-based GMDR (PGMDR) , and other extensions of it are emerging , , . So far, MDR and its extensions have identified many interacting genetic variants underlying various complex human diseases, such as Alzheimer disease , asthma , atrial fibrillation , autism , bladder cancer , hypertension , nicotine dependency , , , , prostate cancer , , schizophrenia , sporadic breast cancer , thrombotic stroke , and Type II diabetes ,  (see Table S1 for details).
Statistical power is a key factor to consider when an investigator designs a trial. Although there is a vast literature on power analysis for single-factor approaches , , , , , , , , , fewer studies have explored the statistical power of MDR and its extended approaches to detect interactions. A thorough study of power for interaction detection under various theoretical assumptions is thus warranted, as statistical power depends on the specific experimental scenario defined by factors such as sample size, significance level, penetrance, population prevalence, allele frequencies, interaction orders, interaction patterns, and sampling scheme, all of which are difficult to determine exactly and can be evaluated only by simulations. To reflect the reality as much as possible for gene-gene interaction studies, we assessed statistical power through intensive simulations of hypothetical scenarios with regard to the information in the literature.
The primary purpose of this study was to examine the statistical power for detecting G×G interactions in case-control designs using GMDR and MDR approaches through simulating various scenarios with the goal of providing empirical guidelines for designing such studies. Although it is generally preferred to use the traditional parameters such as heritability and genotype-relative-risk (GRR)  to characterize experimental scenarios, we propose using accuracy as an indicator parameter to capture the characteristics of an ascertained population. We demonstrate that accuracy is practically estimable and Testing Accuracy (TA) converges to theoretical accuracy in a large sample. Furthermore, we establish an empirical link between TA and heritability.
Materials and Methods
Although the MDR and GMDR methods, as well as the underlying terminology, have been presented in the literature , , , we offer a brief summary here to enable readers to follow our presentation easily. In general, these methods share the same framework  (Figure S1). In step one, the dataset is partitioned randomly into equal or nearly equal subdivisions. (We use throughout this report.) One subdivision is used as the testing set and the rest as the independent training set. In step two, a subset of discrete genetic or environmental factors is selected from all factors of interest. We have combinations exhaustively. In step three, the training set stretches into -dimensional space, and each genotyped subject is allocated to a cell accordingly. The values of the score statistic can be summed in each cell. Here, the GMDR differs from the MDR in which the numbers of cases and controls are directly employed. Without adjustment for covariates, the GMDR is reduced to MDR . Each non-empty cell is then labeled as either high-risk, if the average statistic value is not less than a preset threshold T, or low-risk otherwise. In step four, an interaction model is created by pooling high- and low-risk cells into distinct groups. Some fitness measure is then assessed. Without loss of generality, here we used accuracy (i.e., classification accuracy in step four and TA in step six), although other appropriate measure can also be used. Balanced accuracy may be a better alternative in unbalanced data sets . In step five, all other possible combinations of factors in the training set are examined, and the best -factor model with the maximum classification accuracy is recorded. In step six, the best model from step five is evaluated for TA by the testing set. There are pairs of training-testing sets, so the above procedure is repeated independently times on the sets, and the best models are ranked.
As both the MDR and the GMDR use classification accuracy to identify the best model and TA to evaluate the goodness of fit, we examine here the property of ‘accuracy’, which is defined aswhere TP is true positive having a high-risk value in the high-risk group, TN is true negative with a low-risk value in the low-risk group, FP is false positive, and FN is false negative. When other metrics are used such as sensitivity , specificity , and balanced accuracy , they can be evaluated similarly. For an ascertained population, accuracy is a better characteristic parameter than heritability or GRR because even the same heritability or GRR can result in various genotype distributions with different allele frequencies, prevalences, penetrances, and ascertainment schemes. Further, accuracy is a natural measure for the contribution rate of genes of interest because we do not intend to estimate heritability and GRR parameters in the nonparametric MDR and GMDR approaches. In what follows, we use the logistical model to elucidate accuracy through constructing a conditional genotypic distribution and conditional score distributions and then to calculate the mathematical expectation of accuracy.
Logistic model for a dichotomous trait
For a complex trait, in addition to a functional genotypic combination, environmental factors affect penetrance. We construct a general penetrance function by considering genotypic and covariate effects together. For a dichotomous phenotype, , affected subjects are coded and unaffected . Assume the dichotomous trait has a Bernoulli distribution with the probability for a subject being affected; this situation can be modeled with a generalized linear model:
(1)where is a logit link function, is the intercept, is the coding for genotype , is the coding for the covariate, and and are the corresponding parameters, respectively. Given the subject, the probability of being affected is:(2)
where is estimated from Equation (2) where and are their maximum likelihood estimates (MLE) in model (1) under the null hypothesis H0: .
Conditional genotype and score distributions
To derive the theoretical accuracy, we first focus on the genotype distribution for a case-control sample. Consider the case sample by repeated application of Bayes' theorem; for genotype , we have:(4)
where is the probability of being affected for a given genotype , is the prior probability of genotype in the population from which the sample comes, and the denominator is the sum of the numerator over all genotypes. By applying Equation (4) to the control sample, for a given genotype , we obtain , where is the probability of an unaffected subject and . Under the null hypothesis, the penetrances are the same for all genotypes, and thus Equation (4) can be simplified to for both case and control samples. In contrast, under the alternative hypothesis, the value of depends on genotype . For complex traits, it is likely that covariate(s) are involved in their etiologies, and thus is further determined by the environmental factor, say, , so that: , as presented in Equation (2). , which is obtained by the integral of the expression over variable given its probability density function :(5)
To demonstrate the method, we offer the theoretical genotype distribution for a checkerboard model scenario, as commonly employed in this type of interaction study , , . In the following sections, we consider a penetrance function containing only one covariate, but when necessary, it can easily be extended by incorporating more covariates and other effects; e.g., gene × environment factors. We assume a balanced case-control design with 2000 unrelated subjects, MAF = 0.5, , , , and a covariate . Under such assumptions, the trait is expected to have a heritability of 0.043 (according to the definition of Culverhouse et al. ), and there are two differential risk genotypic groups with their expected penetrances of 0.073 and 0.221 (0.005 and 0.057 if the covariate is excluded), which can be calculated from Equation (5) through numerical solution. After applying these equations, we obtain the expected genotype distribution for the case-control sample, as presented in Figure 1A (see Text S1 for details on calculating this distribution). Such an approach of generating the conditional genotype distribution is flexible and can be applied easily to other scenarios. When no covariate is considered, as assumed in the MDR approach , the genotype distribution becomes a simpler form.
(A): Conditional genotype distribution; (B): Conditional score distribution without covariate adjustment; and (C): Conditional score distribution with covariate adjustment. The parameters used in our simulations under the balanced case-control design are: = 2000, MAF = 0.5, = -5.30, = 2.5, = 1, and .
The sums of the affected and unaffected scores in genotypic cell can be calculated as: (6)respectively, where is the number of the cases or the controls and denotes the expectations of the score of an affected or an unaffected subject given genotype . can be computed, respectively:
In the case without adjustment by the covariate (), these two equations can be simplified, where p is the prevalence of the disease in the sample with its expectation . Figures 1B and 1C show the score distributions without and with covariant adjustment, respectively (see Text S2 for details on calculating the distributions illustrated in Figure 1C). Although only one covariate was adjusted in the derivation of the score distribution, such adjustment of the covariate is necessary and can be applied to cases with more than one covariate.
Accuracy and Testing Accuracy
As defined, the TA always ranges from 0.5 to 1.0. For the GMDR method with and without covariate adjustment, the accuracies for the case shown in Figures 1B and 1C are 0.648 and 0.743, respectively. Indeed, as discussed previously , without adjustment of covariates, the accuracy can be estimated directly from the conditional genotype distribution. This has been confirmed by the identical values of other statistics calculated from the distribution in Figures 1A and 1B.
TA is commonly used in GMDR and MDR. Because it is context-dependent, its mathematical expectation is difficult to derive straightforwardly. Empirically, we show in Figure 2 that when the sample size increases to infinity under a checkerboard model, TA approaches accuracy, which is the theoretical upper bound of TA. For the cases illustrated, TA closely converges to accuracy with a sample size of 1000∼2000. The upper limit of TA can be attained when, in the testing set, each genotypic cell is recognized correctly as high or low risk after the cell has been classified correctly in the training set.
The result was based on a checkerboard model whose parameters were the same as shown in Figure 1. The solid lines are the analytical accuracy and represent the upper bound of the testing accuracy. The three lines downward are the means of the testing accuracies from 200 simulations with a sample size of 2000, 1000, and 500. Because the lines for a sample size of>2000 are coincident with the analytical accuracy lines, they are not shown.
As approximately 85% of detected interactions involved more than one, but less than four genetic loci (Table S1), in this report, we present only the results from three interaction models on the basis of 10 diallelic loci: one digenic (i.e., two functional gene loci involved), one trigenic (i.e., there functional loci involved), and one tetragenic (i.e., four functional loci involved). For convenience of notation, loci are denoted by different letters and the two alleles at each locus by uppercase and lowercase; e.g., A and a for locus 1, B and b for locus 2, etc. For the digenic model, the checkerboard, which was commonly used in epistatic studies because of its weak marginal effects, was employed , , . As elucidated previously, accuracy can serve as an indicator statistic to guide experimental design, so we relaxed the definition of the detailed genetic architecture of high-order interaction and focused on the TA a model can reach. For simplicity, we used models called the 3 uppercase letter model (3ULM), in which genotypes with 3 uppercase letters were set as high risk (e.g., AaBbCc, AABbcc, AAbbCc), and the 4 uppercase-letter model (4ULM), in which genotypes containing 4 uppercase letters were set as high risk for tetragenic interaction.
We employed a balanced experimental design with three moderate sample sizes (500, 1000, and 2000) and two large samples (4000 and 10,000) because large samples have been more prevalent in many recent reports , , . To cover a broad spectrum, we set three levels (0.10, 0.25, and 0.50) of minor allele frequency (MAF) for interacting loci. Hardy-Weinberg and linkage equilibria were assumed throughout the simulations.
Our simulated populations followed the penetrance function defined in Equation (2) where is the intercept with a value of -5.30, is the predictor variable coding for G×G interaction, and is the covariate with a normal distribution . Our simulated genotypic effects were = 1.0, 1.5, 2.0, and 2.5, respectively, and . We investigated three interaction models, four levels of , three levels of MAF, and five sample sizes. There were 180 scenarios in total for our simulation study. For each scenario, we simulated 200 replications in order to produce a precise evaluation of statistical power.
To calculate statistical power, we needed to determine the threshold for each scenario under GMDR and MDR, respectively. For GMDR, we shuffled the residual scores to generate pseudo-samples under the null hypothesis of no association with interaction, and TA was evaluated for each set of pseudo-samples. After repeating this procedure 1000 times and ranking the 1000 TAs obtained, the threshold for TA at a 5% significance level can be determined for the scenario under investigation. The power was calculated by the proportion of the true models identified in 200 simulations with a TA larger than the threshold evaluated for this scenario. The best model was identified on the maximization of average TA and cross-validation consistency (CVC) according to the principle of parsimony that the simplest model is preferred, and the simpler interaction model was chosen if the two statistics suggested different models. The permutation procedure was similar for MDR to calculate the statistical power, except for shuffling the phenotypic values instead of the residual scores obtained with adjustment of the covariate in GMDR. Such a protocol was commonly used in other reported power studies on the MDR method , .
The GMDR software was used to detect gene-gene interactions under various scenarios. The default setting of parameters was adopted in this study, and the GMDR software was also used to conduct MDR algorithm by converting the status of each individual to the corresponding score without covariate adjustment.
For comparison of the three models, their accuracies were calculated by the aforementioned method (Table 1). The heritability under each scenario was calculated, and the relations between accuracy and heritability are plotted in Figure 3. Because each interaction underlying a complex trait often contributes only a small fraction to the overall heritability, the estimated heritability for any single interaction is<0.05. In addition, there appears to be a linear correlation between accuracy and heritability, with an (coefficient of correlation) ranging from 0.89 to 0.98 for the three models (Figure 3). If we excluded accuracies below 0.52, where MAF = 0.1, increased for both 3ULM and 4ULM, especially for the 3ULM model, with increasing from 0.89 to 0.95 (Figure 3). There were many G×G interactions detected underlying human diseases (Figure S2, and Table S1), in which mostly the strength of the interactions was measured by TA, rather than heritability. When applying the linear correlation obtained from simulations to the interactions detected by MDR and its extended methods, we predict that the corresponding heritability for most detected gene-gene interactions is between 0.01 and 0.05.
The solid line in each panel is fitted with the method of least squares, and its is shown in bold font. The dashed lines in 3ULM and 4ULM panels were fitted alike while excluding dots below 0.52, and their values are shown above the reference lines indicating accuracy of 0.52. For the six regression models, the p value for F test was<0.001.
Generally speaking, for the three interaction models simulated, the proportion of wrong models that were significant at the 5% level was close to 0.05, as expected (data not shown). Furthermore, most wrong models contained one or more functional loci, and therefore, the wrong models could be treated as partially detected.
Figure 4 presents the powers of GMDR and MDR for the checkerboard model. As shown, the GMDR had at least 80% for a sample size of≥1000, when the theoretical accuracy is around 0.56∼0.62. This appears to be true for a sample of 500 when the accuracy is>0.60. It is clear that the GMDR outperformed MDR in most scenarios. This was attributed mainly to adjustment of the covariate in the GMDR. However, such an advantage diminished when the accuracy was>0.62, as both the GMDR and the MDR methods showed almost full power. This was also true for a larger sample (i.e., >2000; data not shown).
For each panel, 12 combinations, as defined in Table 1, were simulated, forming three levels of MAF (0.1, 0.25, and 0.5) and four levels of interactive effects (1.0, 1.5, 2.0, and 2.5). Simulation results from sample sizes of 4000 and 10,000 are not shown because no difference in power estimates were detected by the GMDR and MDR methods.
Figures 5 and 6 show the powers for the 3ULM and 4ULM. As shown in Table 1, because the accuracy is<0.52 when MAF = 0.1, the power results for those scenarios are less meaningful and thus will not be presented. Similar to the results in the digenic model, the GMDR outperformed MDR when the accuracy was between 0.56∼0.62, and it was more apparent for 3ULM at sample sizes of 500 and 1000. For the GMDR, in order to yield a power greater than 80% efficiently with accuracy at 0.56, a reasonable sample size should be at least 2000 for trigenic and 4000 for tetragenic models.
For each panel, 12 combinations, as defined in Table 1 were simulated, as shown here, which were formed of three levels of MAFs (0.1, 0.25, and 0.5) and four levels of interaction effects (1.0, 1.5, 2.0, and 2.5).
For each panel, 12 combinations, as defined in Table 1, were simulated, as shown here, which were formed of three levels of MAFs (0.1, 0.25, and 0.5) and four levels of interaction effects (1.0, 1.5, 2.0, and 2.5). Simulation results from the sample of 10,000 are not shown because no difference in power estimates was detected for the GMDR and MDR methods.
Widespread but elusive multifactor interactions usually result in a weak marginal correlation between a factor and the phenotype, posing a significant challenge in identification of the risk factors for complex diseases. Increasing effort is being expended to design powerful detection methods. Although several promising methods are available, the relevant issues of study design and data analysis for detecting interactions including sample size for a desirable power and the efficiency of statistical methods have not been well explored. Here, we compared the statistical power and the accuracy of two commonly used methods, MDR and GMDR through theoretical computation and simulation studies under a broad range of sample sizes and hypothetical parameter settings in which the real parameters would potentially fall. The results provide an empirical guideline for investigators to plan appropriate studies.
In previous power studies of MDR , , , heritability was commonly employed as an indicator parameter. As heritability depends not only on genotypic penetrance and disease prevalence but also on genotypic frequencies in a studied population, it is a measure both of the population and of the gene effects — in other words, heritability is a population-specific parameter even for the same phenotype. Often, if not always, the original reference and an ascertained population show differences in allele frequencies, and heritability measured from the original population is not sensitive in reflecting the property of an ascertained population and vice versa. In case-control designs, thus, heritability is an indicator parameter of less theoretical and practical value.
We believe that accuracy is a better metric to characterize the connection between sample size and power in an interaction study. First, in both GMDR and MDR, the classification accuracy and TA are computed directly from the sample. Second, classification accuracy and TA converge asymptotically to the theoretical accuracy and can offer an estimate of accuracy with a reasonably large sample. Third, accuracy is a straightforward and comprehensive measure of the strength of causality and the goodness of fit of the model, through which other factors such as gene frequencies, gene effects, heritability, and ascertainment conditions influence statistical power. To gain a better understanding of accuracy, we developed a general analytical method to compute the mathematical expectation of accuracy, previously investigated in silico, for a case-control sample. As an indicator statistic, accuracy worked well in our simulations. It should be noticed that balanced accuracy suggested by Velez et al  may be a better metric to measure the fitness when the numbers of cases and controls are unequal for the MDR method.
Furthermore, we found an empirical linear correlation between accuracy and heritability in a wide range of circumstances given different MAFs and penetrances under balanced case-control designs. This will help find a connection between the previous reports ,  and the present study. For the cases simulated and under the sampling scheme investigated, accuracy ranging from 0.55∼0.65 can be converted to a heritability of 0.01∼0.05. This implies that most of the interactions in the literature, the TAs of which fell in this range (Figure S2), have a heritability of 0.01∼0.05 with a sample size of 1000 to 2000. This correlation provides an interpretation of genetic meaning for interactions detected by GMDR and MDR, and probably is applicable to interactions detected by other nonparametric statistics, such as balanced testing accuracy .
In this study, we evaluated the statistical power of GMDR and MDR using accuracy as an indicator to determine the sample sizes required to provide sufficient testing power in a case-control design. The GMDR with covariate adjustment could have a power of>80% for an unrelated case-control design with a sample size≥2000, whereas the theoretical accuracy is around 0.56∼0.62; when the accuracy is<0.56 (heritability close to 0.01), a sample size of at least 4000 would be required to provide sufficient power. Generally speaking, when the sample size was 1000∼2000, GMDR appeared to outperform MDR for all simulated models within the accuracy range, from 0.56 to 0.62, which was close to the densely distributed region of TA in the published data (Figure S2). As the sample size became larger, their difference became less obvious. Large samples will become more common in the near future, although most studies have a sample size of<2000. The benefit of large samples in improving statistical power and detecting interactions of much smaller effect sizes may be validated in the future. As argued recently, however, tiny effects are increasingly discovered in genome-wide association studies with the help of enlarged samples, but whether tiny effects are of great interest remains unclear , . Balancing the sample size and the significance of the interaction detected deserves consideration, such as in Figure S3, yet more data are needed to confirm that the strength of interactions decreases in tandem with the sample size.
Although the above results were obtained entirely on the basis of a case-control design, it can be introduced into the discordant sib pair design because of their similarity in population structure. For quantitative traits, as the process of gaining the mathematical expectation of accuracy should be derived differently, it requires an additional endeavor to reach similar conclusions and consequently mandates further work. It seems difficult, although probable, that in the future, interaction studies will move to the genome-wide scale , and consequently the choice of genotyping chips  and imputation approaches  should be considered.
Six steps involved in data reduction algorithm.
A distribution of testing accuracy from the recently reported literature on gene-gene interactions detected by the MDR or GMDR approaches, with a mean of 0.606, SD of 0.047, and range of 0.50 to 0.70 (Shapiro-Wilk test: p = 0.8033). The width of each bin is 0.02. A detailed list of these studies yielding the values used in this study is provided in Table S1.
Scatter plot of reported gene-gene interactions with respect to their testing accuracy and sample sizes. The vertical lines partition the literature into four intervals with respect to their sample sizes: (0, 500), (500, 1000), (1000, 2000), and (2000, 4000). The location of each black circle is determined by the means of testing accuracy and sample size over the open spots within each interval flanked by two neighboring vertical lines. Because of the limited information available, the open circle for the sample size of≥4000 is not shown.
Testing accuracy of human diseases detected with GMDR/MDR methods in the recent literature.
Conditional genotype distribution of the checkerboard model.
We thank Wen-Jun Zheng and Yann Klimentidis for critical reading for the manuscript.
Conceived and designed the experiments: GBC XYL. Performed the experiments: GBC XYL. Analyzed the data: GBC YX HMX. Wrote the paper: GBC MDL JZ XYL.
- 1. Moore JH, Williams SM (2005) Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27: 637–646.JH MooreSM Williams2005Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis.Bioessays27637646
- 2. Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56: 73–82.JH Moore2003The ubiquitous nature of epistasis in determining susceptibility to common human diseases.Hum Hered567382
- 3. Carlborg O, Jacobsson L, Ahgren P, Siegel P, Andersson L (2006) Epistasis and the release of genetic variation during long-term selection. Nat Genet 38: 418–420.O. CarlborgL. JacobssonP. AhgrenP. SiegelL. Andersson2006Epistasis and the release of genetic variation during long-term selection.Nat Genet38418420
- 4. Phillips PC (2008) Epistasis–the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9: 855–867.PC Phillips2008Epistasis–the essential role of gene interactions in the structure and evolution of genetic systems.Nat Rev Genet9855867
- 5. Hunter DJ (2005) Gene-environment interactions in human diseases. Nat Rev Genet 6: 287–298.DJ Hunter2005Gene-environment interactions in human diseases.Nat Rev Genet6287298
- 6. Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, et al. (2006) A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 241: 252–261.JH MooreJC GilbertCT TsaiFT ChiangT. Holden2006A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.J Theor Biol241252261
- 7. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, et al. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69: 138–147.MD RitchieLW HahnN. RoodiLR BaileyWD Dupont2001Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.Am J Hum Genet69138147
- 8. Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH (2006) A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet Epidemiol 30: 111–123.ER MartinMD RitchieL. HahnS. KangJH Moore2006A novel method to identify gene-gene effects in nuclear families: the MDR-PDT.Genet Epidemiol30111123
- 9. Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19: 376–382.LW HahnMD RitchieJH Moore2003Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.Bioinformatics19376382
- 10. Nelson MR, Kardia SL, Ferrell RE, Sing CF (2001) A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 11: 458–470.MR NelsonSL KardiaRE FerrellCF Sing2001A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.Genome Res11458470
- 11. Culverhouse R, Klein T, Shannon W (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27: 141–152.R. CulverhouseT. KleinW. Shannon2004Detecting epistatic interactions contributing to quantitative traits.Genet Epidemiol27141152
- 12. Hahn L, Moore J (2004) Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biology 4: L. HahnJ. Moore2004Ideal discrimination of discrete clinical endpoints using multilocus genotypes.In Silico Biology4
- 13. Heidema AG, Boer JM, Nagelkerke N, Mariman EC, van der AD, et al. (2006) The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases. BMC Genet 7: 23.AG HeidemaJM BoerN. NagelkerkeEC MarimanAD van der2006The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.BMC Genet723
- 14. Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, et al. (2007) A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 80: 1125–1137.XY LouGB ChenL. YanJZ MaJ. Zhu2007A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence.Am J Hum Genet8011251137
- 15. Lou XY, Chen GB, Yan L, Ma JZ, Mangold JE, et al. (2008) A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies. Am J Hum Genet 83: 457–467.XY LouGB ChenL. YanJZ MaJE Mangold2008A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies.Am J Hum Genet83457467
- 16. Cattaert T, Urrea V, Naj A, De Lobel L, De Wit V, et al. (2010) FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS One 5: e10304.T. CattaertV. UrreaA. NajL. De LobelV. De Wit2010FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals.PLoS One5e10304
- 17. Lee S, Chung Y, Elston R, Kim Y, Park T (2007) Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions. Bioinformatics 23: 2589–2595.S. LeeY. ChungR. ElstonY. KimT. Park2007Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions.Bioinformatics2325892595
- 18. Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH (2006) A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. Genet Epidemiol 30: 111–123.ER MartinMD RitchieL. HahnS. KangJH Moore2006A novel method to identify gene-gene effects in nuclear families: the MDR-PDT.Genet Epidemiol30111123
- 19. Liang X, Slifer M, Martin ER, Schnetz-Boutaud N, Bartlett J, et al. (2009) Genomic convergence to identify candidate genes for Alzheimer disease on chromosome 10. Hum Mutat 30: 463–471.X. LiangM. SliferER MartinN. Schnetz-BoutaudJ. Bartlett2009Genomic convergence to identify candidate genes for Alzheimer disease on chromosome 10.Hum Mutat30463471
- 20. Chan IH, Tang NL, Leung TF, Huang W, Lam YY, et al. (2008) Study of gene-gene interactions for endophenotypic quantitative traits in Chinese asthmatic children. Allergy 63: 1031–1039.IH ChanNL TangTF LeungW. HuangYY Lam2008Study of gene-gene interactions for endophenotypic quantitative traits in Chinese asthmatic children.Allergy6310311039
- 21. Asselbergs FW, Moore JH, van den Berg MP, Rimm EB, de Boer RA, et al. (2006) A role for CETP TaqIB polymorphism in determining susceptibility to atrial fibrillation: a nested case control study. BMC Med Genet 7: 39.FW AsselbergsJH MooreMP van den BergEB RimmRA de Boer2006A role for CETP TaqIB polymorphism in determining susceptibility to atrial fibrillation: a nested case control study.BMC Med Genet739
- 22. Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, et al. (2005) Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet 77: 377–388.DQ MaPL WhiteheadMM MenoldER MartinAE Ashley-Koch2005Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism.Am J Hum Genet77377388
- 23. Andrew AS, Nelson HH, Kelsey KT, Moore JH, Meng AC, et al. (2006) Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. Carcinogenesis 27: 1030–1037.AS AndrewHH NelsonKT KelseyJH MooreAC Meng2006Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility.Carcinogenesis2710301037
- 24. Williams SM, Ritchie MD, Phillips JA 3rd, Dawson E, Prince M, et al. (2004) Multilocus analysis of hypertension: a hierarchical approach. Hum Hered 57: 28–38.SM WilliamsMD RitchieJA Phillips 3rdE. DawsonM. Prince2004Multilocus analysis of hypertension: a hierarchical approach.Hum Hered572838
- 25. Li MD, Lou XY, Chen G, Ma JZ, Elston RC (2008) Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence. Biol Psychiatry 64: 951–957.MD LiXY LouG. ChenJZ MaRC Elston2008Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence.Biol Psychiatry64951957
- 26. Li MD, Mangold JE, Seneviratne C, Chen GB, Ma JZ, et al. (2009) Association and interaction analyses of GABBR1 and GABBR2 with nicotine dependence in European- and African-American populations. PLoS One 4: e7055.MD LiJE MangoldC. SeneviratneGB ChenJZ Ma2009Association and interaction analyses of GABBR1 and GABBR2 with nicotine dependence in European- and African-American populations.PLoS One4e7055
- 27. Beuten J, Gelfond J, Franke J, Weldon K, Crandall A, et al. (2009) Single and multigenic analysis of the association between variants in 12 steroid hormone metabolism genes and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev 18: 1869–1880.J. BeutenJ. GelfondJ. FrankeK. WeldonA. Crandall2009Single and multigenic analysis of the association between variants in 12 steroid hormone metabolism genes and risk of prostate cancer.Cancer Epidemiol Biomarkers Prev1818691880
- 28. Xu J, Lowey J, Wiklund F, Sun J, Lindmark F, et al. (2005) The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk. Cancer Epidemiol Biomarkers Prev 14: 2563–2568.J. XuJ. LoweyF. WiklundJ. SunF. Lindmark2005The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk.Cancer Epidemiol Biomarkers Prev1425632568
- 29. Qin S, Zhao X, Pan Y, Liu J, Feng G, et al. (2005) An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray. Eur J Hum Genet 13: 807–814.S. QinX. ZhaoY. PanJ. LiuG. Feng2005An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray.Eur J Hum Genet13807814
- 30. Shen CD, Zhang WL, Sun K, Wang YB, Zhen YS, et al. (2007) Interaction of genetic risk factors confers higher risk for thrombotic stroke in male Chinese: a multicenter case-control study. Ann Hum Genet 71: 620–629.CD ShenWL ZhangK. SunYB WangYS Zhen2007Interaction of genetic risk factors confers higher risk for thrombotic stroke in male Chinese: a multicenter case-control study.Ann Hum Genet71620629
- 31. Neuman R, Wasson J, Atzmon G, Wainstein J, Yerushalmi Y, et al. (2010) Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population. PLoS One 5: e9903.R. NeumanJ. WassonG. AtzmonJ. WainsteinY. Yerushalmi2010Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population.PLoS One5e9903
- 32. Cho YM, Ritchie MD, Moore JH, Park JY, Lee KU, et al. (2004) Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus. Diabetologia 47: 549–554.YM ChoMD RitchieJH MooreJY ParkKU Lee2004Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus.Diabetologia47549554
- 33. Chen WM, Abecasis GR (2006) Estimating the power of variance component linkage analysis in large pedigrees. Genet Epidemiol 30: 471–484.WM ChenGR Abecasis2006Estimating the power of variance component linkage analysis in large pedigrees.Genet Epidemiol30471484
- 34. Chen WM, Deng HW (2001) A general and accurate approach for computing the statistical power of the transmission disequilibrium test for complex disease genes. Genet Epidemiol 21: 53–67.WM ChenHW Deng2001A general and accurate approach for computing the statistical power of the transmission disequilibrium test for complex disease genes.Genet Epidemiol215367
- 35. Kaplan NL, Martin ER (2001) Power calculations for a general class of tests of linkage and association that use nuclear families with affected and unaffected sibs. Theor Popul Biol 60: 193–201.NL KaplanER Martin2001Power calculations for a general class of tests of linkage and association that use nuclear families with affected and unaffected sibs.Theor Popul Biol60193201
- 36. Kaplan NL, Martin ER, Weir BS (1997) Power studies for the transmission/disequilibrium tests with multiple alleles. Am J Hum Genet 60: 691–702.NL KaplanER MartinBS Weir1997Power studies for the transmission/disequilibrium tests with multiple alleles.Am J Hum Genet60691702
- 37. Lange C, DeMeo DL, Laird NM (2002) Power and design considerations for a general class of family-based association tests: quantitative traits. Am J Hum Genet 71: 1330–1341.C. LangeDL DeMeoNM Laird2002Power and design considerations for a general class of family-based association tests: quantitative traits.Am J Hum Genet7113301341
- 38. Lange C, Laird NM (2002) Power calculations for a general class of family-based association tests: dichotomous traits. Am J Hum Genet 71: 575–584.C. LangeNM Laird2002Power calculations for a general class of family-based association tests: dichotomous traits.Am J Hum Genet71575584
- 39. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273: 1516–1517.N. RischK. Merikangas1996The future of genetic studies of complex human diseases.Science27315161517
- 40. Iles MM (2008) What Can Genome-Wide Association Studies Tell Us about the Genetics of Common Disease. PLoS Genet 4: e33.MM Iles2008What Can Genome-Wide Association Studies Tell Us about the Genetics of Common Disease.PLoS Genet4e33
- 41. Spencer C, Su Z, Donnely P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5: e1000477.C. SpencerZ. SuP. DonnelyJ. Marchini2009Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip.PLoS Genet5e1000477
- 42. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet 9: 255–266.PM VisscherWG HillNR Wray2008Heritability in the genomics era–concepts and misconceptions.Nat Rev Genet9255266
- 43. Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, et al. (2007) A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 31: 306–315.DR VelezBC WhiteAA MotsingerWS BushMD Ritchie2007A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction.Genet Epidemiol31306315
- 44. Culverhouse R, Suarez BK, Lin J, Reich T (2002) A perspective on epistasis: limits of models displaying no main effect. Am J Hum Genet 70: 461–471.R. CulverhouseBK SuarezJ. LinT. Reich2002A perspective on epistasis: limits of models displaying no main effect.Am J Hum Genet70461471
- 45. Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24: 150–157.MD RitchieLW HahnJH Moore2003Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.Genet Epidemiol24150157
- 46. Motsinger AA, Ritchie MD (2006) The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction. Genet Epidemiol 30: 546–555.AA MotsingerMD Ritchie2006The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction.Genet Epidemiol30546555
- 47. Amos CI, Wu X, Broderick P, Gorlov IP, Gu J, et al. (2008) Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet 40: 616–622.CI AmosX. WuP. BroderickIP GorlovJ. Gu2008Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1.Nat Genet40616622
- 48. Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, et al. (2008) A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature 452: 633–637.RJ HungJD McKayV. GaborieauP. BoffettaM. Hashibe2008A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25.Nature452633637
- 49. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, et al. (2008) A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452: 638–642.TE ThorgeirssonF. GellerP. SulemT. RafnarA. Wiste2008A variant associated with nicotine dependence, lung cancer and peripheral arterial disease.Nature452638642
- 50. Motsinger AA, Lee SL, Mellick G, Ritchie MD (2006) GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinformatics 7: 39.AA MotsingerSL LeeG. MellickMD Ritchie2006GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.BMC Bioinformatics739
- 51. Goldstein DB (2009) Common genetic variation and human traits. N Eng J Med 370: 1696–1698.DB Goldstein2009Common genetic variation and human traits.N Eng J Med37016961698
- 52. Hirschhorn JN (2009) Genomewide association studies–illuminating biologic pathways. N Eng J Med 360: 1699–1701.JN Hirschhorn2009Genomewide association studies–illuminating biologic pathways.N Eng J Med36016991701
- 53. Zhang Y, Liu JS (2007) Bayesian inference of epistatic interactions in case-control studies. Nat Genet 39: 1167–1173.Y. ZhangJS Liu2007Bayesian inference of epistatic interactions in case-control studies.Nat Genet3911671173
- 54. Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39: 906–913.J. MarchiniB. HowieS. MyersG. McVeanP. Donnelly2007A new multipoint method for genome-wide association studies by imputation of genotypes.Nat Genet39906913