Weighted Risk Score-Based Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions in  Nasopharyngeal Carcinoma

Li, Chao Feng; Luo, Fu; Zeng, Yi Xin; Jia, Wei Hua

doi:10.3390/ijms150610724

Cited by 8 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selected SNPs 4, 9 4, 6, 9 4, 9, 17, 20 4, 9, 20, 22 Selected SNPs 4, 9 4, 9, 10 4, 7, 9, 22 N/A mdr only Execution time (s) 6.4 261. 5 9,741.7 (320,700)…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

Hong¹,

Lee²,

Oh³

2016

ETRI J

View full text Add to dashboard Cite

The detection of gene–gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the “curse of dimensionality” — that is, it works well for two‐ or three‐way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10‐way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre‐evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

show abstract

“…Selected SNPs 4, 9 4, 6, 9 4, 9, 17, 20 4, 9, 20, 22 Selected SNPs 4, 9 4, 9, 10 4, 7, 9, 22 N/A mdr only Execution time (s) 6.4 261. 5 9,741.7 (320,700)…”

Section: Resultsmentioning

confidence: 99%

“…Various follow-up studies have been undertaken to improve the original MDR method. Li and others suggested weighted risk score-based MDR [5]. Bulinski studied the impact of the choice of penalty function, and his study is used as a basis for MDR methods [6].…”

Section: Introductionmentioning

confidence: 99%

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

Hong¹,

Lee²,

Oh³

2016

ETRI J

View full text Add to dashboard Cite

show abstract

“…Therefore, GRS for epistatic interactions is underexplored. Although aggregated scores of Multifactor Dimensionality Reduction (MDR) were investigated years ago [18,19], the computational burden of MDR has rendered it impossible to perform genome-wide scan of epistatic interactions. Because genome-wide genotype datasets, especially those produced by next-generation sequencing technologies, are being increasingly generated, efficient models for summarizing the genetic risks of epistatic interactions are highly desirable.…”

Section: Introductionmentioning

confidence: 99%

Understanding the genetic risks of complex diseases using the additive epistatic interaction model: a simulation study

Wang¹

2019

Preprint

View full text Add to dashboard Cite

Thousands of Genome-Wide Association Studies (GWAS) have been carried out to pinpoint genetic variants associated with complex diseases. However, the proportion of phenotypic variance which can be explained by the identified genetic variants is relatively low, leading to the "missing heritability" problem. This problem may be partly caused by the inadequate understanding of the genetic mechanisms of complex diseases. Here, we propose the additive epistatic interaction model, consisting of widespread pure epistatic interactions whose effects are additive and can be summarized by a genetic risk score. Based on a simulated genotype dataset, the additive epistatic interaction model well depicted genetic risks and hereditary patterns of complex diseases. Based on the 1000 Genomes Project data, the additive epistatic interaction model accurately classified human populations. Moreover, the model's genetic risk score can be replaced by a deep learning model which is more resistant to noises. We suggest that the additive epistatic interaction model may help to understand the genetic mechanisms and risks of complex diseases.

show abstract

“…Moreover, MDR has been successfully applied to identify SNP-SNP interactions in various diseases, e.g., atrial fibrillation (32) and coronary artery disease (33). However, MDR cannot quantitatively evaluate the disease susceptibility of genotype combinations (34). Recently, improved MDR methods such as MDR-ER (35) and weighted risk score-based MDR (34) were developed to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

“…However, MDR cannot quantitatively evaluate the disease susceptibility of genotype combinations (34). Recently, improved MDR methods such as MDR-ER (35) and weighted risk score-based MDR (34) were developed to solve this problem. Moreover, MDR-ER introduces two functions to improve the classification step and an evaluation of error rate in MDR, and it can be applied to the data set of imbalanced numbers of cases and controls.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER)

et al. 2016

View full text Add to dashboard Cite

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP‑SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR‑ER analysis, six significant SNP‑SNP interaction models with seven genes (highest cross‑validation consistency, 10; classification error rates, 41.3‑21.0; and prediction error rates, 47.4‑55.3) were identified. CD4 and VEGFA genes were associated in a 2‑loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40‑2.90; P=1.71E‑04) and it also appeared in all the best 2‑7‑loci models. When the loci number increased, the classification error rates and P‑values decreased. The powers in 2‑7‑loci in all models were >0.9. The minimum classification error rate of the MDR‑ER‑generated model was shown with the 7‑loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54‑23.87; P=4.03E‑31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR‑ER can effectively and correctly identify the best SNP‑SNP interaction models in an imbalanced data set for breast cancer cases.

show abstract

Weighted Risk Score-Based Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions in Nasopharyngeal Carcinoma

Cited by 8 publications

References 34 publications

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

Boosting Multifactor Dimensionality Reduction Using Pre-evaluation

Understanding the genetic risks of complex diseases using the additive epistatic interaction model: a simulation study

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER)

Contact Info

Product

Resources

About