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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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Related in: MedlinePlus

Linear correlation between accuracy and heritability.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.
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pone-0016981-g003: Linear correlation between accuracy and heritability.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.

Mentions: 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.


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)

Linear correlation between accuracy and heritability.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.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0016981-g003: Linear correlation between accuracy and heritability.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.
Mentions: 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.

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
Related in: MedlinePlus