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Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study.

He H, Oetting WS, Brott MJ, Basu S - BMC Med. Genet. (2009)

Bottom Line: We generated several interaction models with different magnitudes of interaction effect.We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients.We have compared their performances for different two-way and three-way interaction models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, USA.

ABSTRACT

Background: There is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions). The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR), a variant of logistic regression with L2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk.

Methods: We compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients.

Results: In this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were consistently better for all data scenarios, but, generally MDR outperformed PLR for more complex models. The ROC analysis on the real dataset suggests that MDR outperforms PLR in detecting gene-gene interaction on the real dataset.

Conclusion: As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of the methods to detect gene-gene interaction.

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Related in: MedlinePlus

The case-control distribution for MDR. The case-control distribution of the finally selected 3-way interaction model for MDR. If a person falls in the red cell, MDR classifies him as a case(AR), otherwise a control.
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Figure 6: The case-control distribution for MDR. The case-control distribution of the finally selected 3-way interaction model for MDR. If a person falls in the red cell, MDR classifies him as a case(AR), otherwise a control.

Mentions: We first applied MDR on this dataset. We considered only up to 3 way interactions. Ten-fold cross validation was used to obtain the best model for each given number (n = 1,2,3) SNPs. With a given number of SNPs, the training error was used to choose the best model at each cross validation and CV consistency was used to select the best model across the 10-fold cross validation. For one SNP model (main effect model), 6 out of 10 times, MDR chose SNP rs875740 and thus the best model was rs875740. For two SNPs model (2-way interaction model), the best one was rs2741045*rs288326; and for three SNPs model(3-way interaction model), the best model was rs937369*rs4459610*rs1805335. The results of MDR is shown in Table 6. We also reported the averaged training error and averaged test error for these models. We obtained the best overall model based on the averaged test error. The best overall model was (rs937369 (ABCC1)) × (rs4459610 (ACE)) × (rs1805335 (RAD23B)) with averaged test error 0.36 from 10-fold cross-validation. The sensitivity and specificity of the best three SNPs model(also the best overall model)were 0.833 and 0.642 respectively (Figure 5). The p-values from the single SNP association analyses were 0.007, 0.010 and 0.016 for SNPs rs937369, rs4459610 and rs1805335 respectively. Figure 6 shows the distribution of the cases and controls in the 3-way contingency table of the 3 selected SNPs.


Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study.

He H, Oetting WS, Brott MJ, Basu S - BMC Med. Genet. (2009)

The case-control distribution for MDR. The case-control distribution of the finally selected 3-way interaction model for MDR. If a person falls in the red cell, MDR classifies him as a case(AR), otherwise a control.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2800840&req=5

Figure 6: The case-control distribution for MDR. The case-control distribution of the finally selected 3-way interaction model for MDR. If a person falls in the red cell, MDR classifies him as a case(AR), otherwise a control.
Mentions: We first applied MDR on this dataset. We considered only up to 3 way interactions. Ten-fold cross validation was used to obtain the best model for each given number (n = 1,2,3) SNPs. With a given number of SNPs, the training error was used to choose the best model at each cross validation and CV consistency was used to select the best model across the 10-fold cross validation. For one SNP model (main effect model), 6 out of 10 times, MDR chose SNP rs875740 and thus the best model was rs875740. For two SNPs model (2-way interaction model), the best one was rs2741045*rs288326; and for three SNPs model(3-way interaction model), the best model was rs937369*rs4459610*rs1805335. The results of MDR is shown in Table 6. We also reported the averaged training error and averaged test error for these models. We obtained the best overall model based on the averaged test error. The best overall model was (rs937369 (ABCC1)) × (rs4459610 (ACE)) × (rs1805335 (RAD23B)) with averaged test error 0.36 from 10-fold cross-validation. The sensitivity and specificity of the best three SNPs model(also the best overall model)were 0.833 and 0.642 respectively (Figure 5). The p-values from the single SNP association analyses were 0.007, 0.010 and 0.016 for SNPs rs937369, rs4459610 and rs1805335 respectively. Figure 6 shows the distribution of the cases and controls in the 3-way contingency table of the 3 selected SNPs.

Bottom Line: We generated several interaction models with different magnitudes of interaction effect.We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients.We have compared their performances for different two-way and three-way interaction models.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, USA.

ABSTRACT

Background: There is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions). The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR), a variant of logistic regression with L2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk.

Methods: We compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients.

Results: In this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were consistently better for all data scenarios, but, generally MDR outperformed PLR for more complex models. The ROC analysis on the real dataset suggests that MDR outperforms PLR in detecting gene-gene interaction on the real dataset.

Conclusion: As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of the methods to detect gene-gene interaction.

Show MeSH
Related in: MedlinePlus