Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

Association of Genetic Variants in IGF2-Related Genes With Risk of Metabolic Syndrome in the Chinese Han Population

AIMS

To explore associations between polymorphisms of IGF2-related genes including H19, IGF2, IGF2BP2 and IGF2R and Metabolic syndrome (MetS) susceptibility in the Chinese Han population.

METHODS

66 subjects with MetS and 257 control subjects were collected for inclusion in a case-control study. PCR-RFLP was used to investigate polymorphisms in the H19, IGF2, IGF2BP2 and IGF2R genes. Elisa was used to detect the serum IGF2 concentrations.

RESULTS

Females carrying the GG and AG genotypes of rs680 (IGF2) exhibited a lower risk of MetS, compared with those harboring AA (adjusted OR = 0.388, p = 0.027), while GG and AG genotypes were associated with lower fasting glucose and HbA1c. In males, the Waist-to-Hip Ratio (WHR) and the level of TG were significantly higher in GG and AG genotypes than in the AA genotype of rs680 in IGF2. Levels of HDL-c were lower in men with GG and AG genotypes compared with those carrying the AA genotype. Serum IGF2 concentrations did not change among different genotypes. Finally, multifactor dimensionality reduction (MDR) analysis identified interactions between four polymorphisms: rs3741279 (H19), rs680 (IGF2), rs1470579 (IGF2BP2) and rs629849 (IGF2R).

CONCLUSIONS

Our study suggests that IGF2-related genes including H19, IGF2, IGF2BP2 and IGF2R genes may play pivotal roles in the development of MetS.

Correlation between the rs7101 and rs1063169 polymorphisms in the FOS noncoding region and susceptibility to and prognosis of colorectal cancer

BACKGROUND

The FOS gene is located on human chromosome 14q21-31 and encodes the nuclear oncoprotein c-Fos. This study analyzed the correlation between the FOS noncoding region rs7101 and rs1063169 polymorphisms and colorectal cancer susceptibility and prognosis.

METHODS

We analyzed the FOS genotypes in 432 colorectal cancer patients and 315 healthy subjects by PCR/Sanger sequencing. Survival was analyzed by Kaplan-Meier and Cox regression analysis. Western blot was used to detect the expression of c-Fos protein in cancer tissues and adjacent tissues in colorectal cancer patients with different genotypes.

RESULTS

The presence of a T allele at rs7101 and a T allele at rs1063169 in FOS carried a higher risk of colorectal cancer [adjusted odds ratio (OR) = 1.237, 95% confidence interval (95% CI) = 1.131-1.346, P ≤ .001 and adjusted OR = 1.218, 95% CI = 1.111-1.327, P ≤ .001, respectively]. c-Fos protein levels were significantly higher in variant cancer tissues than in normal mucosa tissues (P < .05), and c-Fos proteins levels were also higher in homozygous variant cancer tissues than in heterozygous variant cancer tissues. The 3-year survival rate of patients with wild-type FOS was higher than that of patients with variant FOS (P < .05).

CONCLUSION

The rs7101 and rs1063169 polymorphisms in the noncoding region of FOS are associated with the risk of developing colorectal cancer and the progression of colorectal cancer, which may be because the mutation enhances the expression of c-Fos protein to promote the incidence and development of colorectal cancer.

KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies

Background

Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data.

Results

Experimental results on both simulated data and real genome-wide data show that KNN-MDR has interesting properties in terms of accuracy and power, and that, in many cases, it significantly outperforms its recent competitors.

Conclusions

The presented methodology (KNN-MDR) is valuable in the context of loci and interactions mapping and can be seen as an interesting addition to the arsenal used in complex traits analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1599-7) contains supplementary material, which is available to authorized users.

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

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.

An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.