Gene-gene interaction analysis for quantitative trait using cluster-based multifactor dimensionality reduction method

Lee, You-Jung; Kim, Hyein; Park, Taesung; Park, Soo‐Jin

doi:10.1504/ijdmb.2018.10013376

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…For dealing with continuous traits, quantitative MDR (QMDR) was proposed by comparing the sample mean of each genotype combination with the global mean [41]. To handle outliers and to make the distributional assumption free, cluster-based MDR has been proposed [42]. For survival time with censored data, Surv-MDR was proposed, which uses the logrank test statistic as a classifier [43].…”

Section: Multifactor Dimensionality Reductionmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.

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Section: Multifactor Dimensionality Reductionmentioning

confidence: 99%

Overview of frequent pattern mining

Ott

Park

2022

Genomics Inform

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“…Quantitative MDR (QMDR) for continuous traits uses the sample mean of each genotype combination as a classifier, reducing the computing time with comparable performance [ 15 ]. Recently, cluster-based MDR (CL-MDR) has been proposed as a method that is less sensitive to outliers and distributional assumptions [ 16 ]. For survival time with censored data, Surv-MDR was proposed, which uses the log-rank test statistic to classify the cells of a multifactor combination [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial rank-based multifactor dimensionality reduction to detect gene–gene interactions for multivariate phenotypes

et al. 2021

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Background Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene–gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling’s T2 statistic to evaluate interaction models, but it is well known that Hotelling’s T2 statistic is highly sensitive to heavily skewed distributions and outliers. Results We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR. Conclusions Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.

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“…It is possible to determine the level of expression of genes that describe the cellular state in a given condition and time with microarray and other recent technologies such as next generation sequencing. Since genetic or environmental factors can be explained by the risk of a specific disease, finding gene-gene or gene-environment interactions may be critical to gaining a better understanding of the factors affecting the risk of disease [4]. The inference of GRNs to improve their structure has potential implications for medicine and drug design; while finding links between genes through wet-lab experiments is costly and time-consuming [1].…”

Section: Introductionmentioning

confidence: 99%

EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

Khojasteh

Olyaee

Khanteymoori

2021

Preprint

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The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

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Gene-gene interaction analysis for quantitative trait using cluster-based multifactor dimensionality reduction method

Cited by 3 publications

References 22 publications

Overview of frequent pattern mining

Overview of frequent pattern mining

Spatial rank-based multifactor dimensionality reduction to detect gene–gene interactions for multivariate phenotypes

EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

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