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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 on six simulated models for different minor allele frequencies (MAFs) and different sample sizes (a–f of Fig. 2). For all models, heritability h2 = 0.2, and MAF includes 0.1, 0.2, and 0.4. For each model, 100 datasets are generated by randomly sorted samples. The figures show the box plot, where the boundary of the box closest to zero indicates the 25th percentile, a line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Error bars near the top and bottom of the boxes respectively indicate the 90th and 10th percentiles
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Fig2: Performance comparison between MDR and FMDR on six simulated models for different minor allele frequencies (MAFs) and different sample sizes (a–f of Fig. 2). For all models, heritability h2 = 0.2, and MAF includes 0.1, 0.2, and 0.4. For each model, 100 datasets are generated by randomly sorted samples. The figures show the box plot, where the boundary of the box closest to zero indicates the 25th percentile, a line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Error bars near the top and bottom of the boxes respectively indicate the 90th and 10th percentiles

Mentions: All simulated models set the 50 attributes with a heritability of 0.2. The minor allele frequencies (MAFs) were 0.1, 0.2, and 0.4. The sample sizes were 800 and 1600, in which the total number of cases is equal to the total number of controls. The simulation data was generated using GAMETES, software used for generating complex n-loci models with random architectures [31]. The settings and results of the six models are shown in Table 1. Figure 2 shows the power analysis box plots of six models. A summary of the six simulation data set shows that the difference between MDR and FMDR was statistically significant for 4-loci and 5-loci, but there was only a slight difference between the averages of the two methods.Table 1


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 on six simulated models for different minor allele frequencies (MAFs) and different sample sizes (a–f of Fig. 2). For all models, heritability h2 = 0.2, and MAF includes 0.1, 0.2, and 0.4. For each model, 100 datasets are generated by randomly sorted samples. The figures show the box plot, where the boundary of the box closest to zero indicates the 25th percentile, a line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Error bars near the top and bottom of the boxes respectively indicate the 90th and 10th percentiles
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Performance comparison between MDR and FMDR on six simulated models for different minor allele frequencies (MAFs) and different sample sizes (a–f of Fig. 2). For all models, heritability h2 = 0.2, and MAF includes 0.1, 0.2, and 0.4. For each model, 100 datasets are generated by randomly sorted samples. The figures show the box plot, where the boundary of the box closest to zero indicates the 25th percentile, a line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Error bars near the top and bottom of the boxes respectively indicate the 90th and 10th percentiles
Mentions: All simulated models set the 50 attributes with a heritability of 0.2. The minor allele frequencies (MAFs) were 0.1, 0.2, and 0.4. The sample sizes were 800 and 1600, in which the total number of cases is equal to the total number of controls. The simulation data was generated using GAMETES, software used for generating complex n-loci models with random architectures [31]. The settings and results of the six models are shown in Table 1. Figure 2 shows the power analysis box plots of six models. A summary of the six simulation data set shows that the difference between MDR and FMDR was statistically significant for 4-loci and 5-loci, but there was only a slight difference between the averages of the two methods.Table 1

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