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Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches.

Chen GB, Xu Y, Xu HM, Li MD, Zhu J, Lou XY - PLoS ONE (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.

ABSTRACT
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.

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Power comparison of GMDR and MDR for sample sizes of 500, 1000, 2000, and 4000 under the 4ULM (tetragenic model) at alpha = 0.05.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.
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pone-0016981-g006: Power comparison of GMDR and MDR for sample sizes of 500, 1000, 2000, and 4000 under the 4ULM (tetragenic model) at alpha = 0.05.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.

Mentions: 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.


Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches.

Chen GB, Xu Y, Xu HM, Li MD, Zhu J, Lou XY - PLoS ONE (2011)

Power comparison of GMDR and MDR for sample sizes of 500, 1000, 2000, and 4000 under the 4ULM (tetragenic model) at alpha = 0.05.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.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3046176&req=5

pone-0016981-g006: Power comparison of GMDR and MDR for sample sizes of 500, 1000, 2000, and 4000 under the 4ULM (tetragenic model) at alpha = 0.05.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.
Mentions: 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.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.

ABSTRACT
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.

Show MeSH