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An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.

Yang CH, Lin YD, Yang CS, Chuang LY - BMC Genomics (2015)

Bottom Line: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans.Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene-gene interaction with less computational complexity than the MDR in high-order interaction analysis.FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. chyang@cc.kuas.edu.tw.

ABSTRACT

Background: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results: Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene-gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion: FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar .

No MeSH data available.


Related in: MedlinePlus

Performance comparison between MDR and FMDR for the chronic dialysis data set. (a) accuracy box plot for MDR and FMDR, (b) OR box plot for MDR and FMDR, and (c) power analysis box plot for MDR and FMDR for 100 tests. For each test, the samples in the data set are randomly sorted, and then applied to MDR and FMDR
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Fig4: Performance comparison between MDR and FMDR for the chronic dialysis data set. (a) accuracy box plot for MDR and FMDR, (b) OR box plot for MDR and FMDR, and (c) power analysis box plot for MDR and FMDR for 100 tests. For each test, the samples in the data set are randomly sorted, and then applied to MDR and FMDR

Mentions: For the 100 tests, we summed up the frequencies of the results based on the cross-validation consistency (CVC) and the classification error rate in each test. The accuracy and OR of the best candidate model was evaluated. Table 2 shows the best, worst, and mean (±SD) in the 100 tests for MDR and FMDR. For the 3- –6-loci models producing the best accuracy amongst the 100 tests, both MDR and FMDR had the same candidate model, and also had the same accuracy and OR. In the models for 3- to 6-loci with the lowest accuracy amongst the 100 tests, MDR and FMDR were different slightly, and the accuracy and OR also differed. A box plot was used to compare the two methods for 3-, 4-, 5-, and 6-loci interactions. Figure 4a and b respectively shows the accuracy and OR box plot of MDR and FMDR. Paired t-test comparison results indicate that the accuracy and OR values for 3- –6-loci analysis over 100 test runs were similar for both MDR and FMDR. Figure 4c shows the box plot of the power results of MDR and FMDR for four order interactions. As the order of interaction increases, both MDR and FMDR shows increasing power values. All powers of MDR and FMDR exceeded 0.8. A summary of the 100 test runs shows that the difference between MDR and FMDR was statistically significant for 3- and 6-loci, but the average difference between the two methods is very slight, i.e., −0.011 at 3-loci and 0.002 at 6-loci. In addition, the powers in the 4- and 5-loci analysis over 100 test runs are similar for both MDR and FMDR.Table 2


An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.

Yang CH, Lin YD, Yang CS, Chuang LY - BMC Genomics (2015)

Performance comparison between MDR and FMDR for the chronic dialysis data set. (a) accuracy box plot for MDR and FMDR, (b) OR box plot for MDR and FMDR, and (c) power analysis box plot for MDR and FMDR for 100 tests. For each test, the samples in the data set are randomly sorted, and then applied to MDR and FMDR
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4487567&req=5

Fig4: Performance comparison between MDR and FMDR for the chronic dialysis data set. (a) accuracy box plot for MDR and FMDR, (b) OR box plot for MDR and FMDR, and (c) power analysis box plot for MDR and FMDR for 100 tests. For each test, the samples in the data set are randomly sorted, and then applied to MDR and FMDR
Mentions: For the 100 tests, we summed up the frequencies of the results based on the cross-validation consistency (CVC) and the classification error rate in each test. The accuracy and OR of the best candidate model was evaluated. Table 2 shows the best, worst, and mean (±SD) in the 100 tests for MDR and FMDR. For the 3- –6-loci models producing the best accuracy amongst the 100 tests, both MDR and FMDR had the same candidate model, and also had the same accuracy and OR. In the models for 3- to 6-loci with the lowest accuracy amongst the 100 tests, MDR and FMDR were different slightly, and the accuracy and OR also differed. A box plot was used to compare the two methods for 3-, 4-, 5-, and 6-loci interactions. Figure 4a and b respectively shows the accuracy and OR box plot of MDR and FMDR. Paired t-test comparison results indicate that the accuracy and OR values for 3- –6-loci analysis over 100 test runs were similar for both MDR and FMDR. Figure 4c shows the box plot of the power results of MDR and FMDR for four order interactions. As the order of interaction increases, both MDR and FMDR shows increasing power values. All powers of MDR and FMDR exceeded 0.8. A summary of the 100 test runs shows that the difference between MDR and FMDR was statistically significant for 3- and 6-loci, but the average difference between the two methods is very slight, i.e., −0.011 at 3-loci and 0.002 at 6-loci. In addition, the powers in the 4- and 5-loci analysis over 100 test runs are similar for both MDR and FMDR.Table 2

Bottom Line: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans.Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene-gene interaction with less computational complexity than the MDR in high-order interaction analysis.FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. chyang@cc.kuas.edu.tw.

ABSTRACT

Background: Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results: Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene-gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion: FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar .

No MeSH data available.


Related in: MedlinePlus