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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

MDR and FMDR execution times on six simulated models for different MAFs and different sample sizes (a-f of Fig. 3). The horizontal axis represents the execution time in log10 milliseconds, while the vertical axis represents the number of loci in the model
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Fig3: MDR and FMDR execution times on six simulated models for different MAFs and different sample sizes (a-f of Fig. 3). The horizontal axis represents the execution time in log10 milliseconds, while the vertical axis represents the number of loci in the model

Mentions: Figure 3 showed the execution times for the simulation data sets. The MDR execution times in all locus orders were collected in a stand-alone test. The total of all FMDR execution times for all locus orders was collected since FMDR uses a continual analysis strategy. For the six simulation data sets, MDR and FMDR required similar durations to implement the 2-loci analysis. When comparing 2-loci and n-loci (n = 3, 4, 5) in model 1, the growth times between MDR and FMDR for 3-loci to 5-loci were 3.796 vs. 2.691, 38.279 vs. 8.712, and 424.18 vs. 43.260 (milliseconds). Similarly, Figure C1 of supplementary Additional file 3 compares the 2-loci and n-loci in models 3–6. We compared the growth time between 800 and 1600 samples in different MAFs. For MAF = 0.1, MDR and FMDR for 2-loci to 5-loci were 1.162 vs. 1.197, 1.796 vs. 1.452, 2.140 vs. 1.599, and 2.063 vs. 1.774. Similarly, Figure D1 of supplementary Additional file 4 shows the growth times between MDR and FMDR in other MAFs (i.e., MAF = 0.2 and MAF = 0.4). The results for the simulation data sets showed that FMDR effectively reduces MDR computational time.Fig. 3


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)

MDR and FMDR execution times on six simulated models for different MAFs and different sample sizes (a-f of Fig. 3). The horizontal axis represents the execution time in log10 milliseconds, while the vertical axis represents the number of loci in the model
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: MDR and FMDR execution times on six simulated models for different MAFs and different sample sizes (a-f of Fig. 3). The horizontal axis represents the execution time in log10 milliseconds, while the vertical axis represents the number of loci in the model
Mentions: Figure 3 showed the execution times for the simulation data sets. The MDR execution times in all locus orders were collected in a stand-alone test. The total of all FMDR execution times for all locus orders was collected since FMDR uses a continual analysis strategy. For the six simulation data sets, MDR and FMDR required similar durations to implement the 2-loci analysis. When comparing 2-loci and n-loci (n = 3, 4, 5) in model 1, the growth times between MDR and FMDR for 3-loci to 5-loci were 3.796 vs. 2.691, 38.279 vs. 8.712, and 424.18 vs. 43.260 (milliseconds). Similarly, Figure C1 of supplementary Additional file 3 compares the 2-loci and n-loci in models 3–6. We compared the growth time between 800 and 1600 samples in different MAFs. For MAF = 0.1, MDR and FMDR for 2-loci to 5-loci were 1.162 vs. 1.197, 1.796 vs. 1.452, 2.140 vs. 1.599, and 2.063 vs. 1.774. Similarly, Figure D1 of supplementary Additional file 4 shows the growth times between MDR and FMDR in other MAFs (i.e., MAF = 0.2 and MAF = 0.4). The results for the simulation data sets showed that FMDR effectively reduces MDR computational time.Fig. 3

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