Limits...
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

FMDR flowchart
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: FMDR flowchart

Mentions: FMDR proposes a new framework to improve the MDR computational time. Figure 1 shows the FMDR flowchart consisting of five steps: (1) data processing, (2) selection of training and testing sets, (3) evaluation of all possible combinations, (4) identifying the best model, and (5) statistical analysis of the best model. In the FMDR, the number of selected SNPs is limited to two at the outset. The framework is represented by the thick frame in steps (2) and (5) (Fig. 1). In step (2), the framework checks whether or not the number of loci is equal to two. If yes, all available two-order locus combinations amongst the loci are created and regarded as conditions. All these conditions are then used to evaluate the contingency table (step (3)), and the classification error rate in each combination is estimated by Equation (3) (step (4)). In step (5), all two-order locus combinations are sorted based on the classification error rate, and then the results of the best n% combinations with the minimum classification error rate are saved into the ith memory where i is the ith-fold cross-validation. When ten cross-validations are computed, the best 2-loci model is output to show related gene–gene interaction information. If the number of order exceeds two (i.e., m-loci, m > 2), each cross-validation uses the corresponding memory and the recorded results of the best n% combinations to create the available combinations (go to step (2)), i.e., conditions. In step (3), these conditions are evaluated using the contingency table, and the classification error rates of the conditions are estimated in step (4). The results are then sorted and the best n% combinations are saved into ith memory to analyze the next interaction order. This process tremendously reduces the number of available combinations. The processes are repeatedly implemented until the defined number of selected SNPs is analyzed.Fig. 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)

FMDR flowchart
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: FMDR flowchart
Mentions: FMDR proposes a new framework to improve the MDR computational time. Figure 1 shows the FMDR flowchart consisting of five steps: (1) data processing, (2) selection of training and testing sets, (3) evaluation of all possible combinations, (4) identifying the best model, and (5) statistical analysis of the best model. In the FMDR, the number of selected SNPs is limited to two at the outset. The framework is represented by the thick frame in steps (2) and (5) (Fig. 1). In step (2), the framework checks whether or not the number of loci is equal to two. If yes, all available two-order locus combinations amongst the loci are created and regarded as conditions. All these conditions are then used to evaluate the contingency table (step (3)), and the classification error rate in each combination is estimated by Equation (3) (step (4)). In step (5), all two-order locus combinations are sorted based on the classification error rate, and then the results of the best n% combinations with the minimum classification error rate are saved into the ith memory where i is the ith-fold cross-validation. When ten cross-validations are computed, the best 2-loci model is output to show related gene–gene interaction information. If the number of order exceeds two (i.e., m-loci, m > 2), each cross-validation uses the corresponding memory and the recorded results of the best n% combinations to create the available combinations (go to step (2)), i.e., conditions. In step (3), these conditions are evaluated using the contingency table, and the classification error rates of the conditions are estimated in step (4). The results are then sorted and the best n% combinations are saved into ith memory to analyze the next interaction order. This process tremendously reduces the number of available combinations. The processes are repeatedly implemented until the defined number of selected SNPs is analyzed.Fig. 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