An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions

The Role of Genetic Variants in the Long Non-Coding RNA Genes MALAT1 and H19 in the Pathogenesis of Childhood Obesity

Long non-coding RNAs (lncRNAs) play important roles in the maintenance of metabolic homeostasis. Recently, many studies have suggested that lncRNAs, such as Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) and Imprinted Maternally Expressed Transcript (H19), might participate in the pathogenesis of metabolic disorders such as obesity. We conducted a case-control study with 150 Russian children and adolescents aged between 5 and 17 years old in order to assess the statistical association between the single nucleotide polymorphisms (SNPs) rs3200401 in MALAT1 and rs217727 in H19, and the risk of developing obesity in this population. We further explored the possible association of rs3200401 and rs217727 with BMI Z-score and insulin resistance. The MALAT1 rs3200401 and H19 rs217727 SNPs were genotyped using Taqman SNP genotyping assay. The MALAT1 rs3200401 SNP was identified as a risk factor for childhood obesity (p < 0.05) under the dominant and allelic models, and the CT heterozygous genotype was associated with the risk of increased BMI and with insulin resistance. The H19 rs217727 SNP had no significant association with obesity risk (all p > 0.05). Our findings thus suggest that MALAT1 SNP rs3200401 is a potential indicator of obesity susceptibility and pathogenesis in children and adolescents.

Impact of MIR31HG polymorphisms on risk of breast cancer in Chinese women

BACKGROUND

Breast cancer (BC) is one of the leading causes of death worldwide. This study explored the relationship between the MIR31HG gene polymorphisms and the risk of BC in Chinese women.

METHODS

Eight single nucleotide polymorphisms (SNPs) in MIR31HG were genotyped among 545 patients with BC and 530 healthy controls using Agena MassARRAY analysis. The PLINK software was used to calculate the odds ratio (OR) and 95% confidence intervals (CIs) via the logistic regression analysis. Multi-factor dimensionality reduction (MDR) analysis was performed to study the impact of SNP-SNP interaction on BC risk.

RESULTS

MIR31HG rs72703442-AA (OR 0.29, 95% CI 0.10-0.79, p = 0.026), rs55683539-TT (OR 0.46, 95% CI 0.26-0.80, p = 0.012) and rs2181559-AA (OR 0.59, 95% CI 0.40-0.89, p = 0.038) were associated with a reduced risk of BC in Chinese women, as well as stratified results at age ≥ 52 years. Rs79988146 was correlated with estrogen receptor (ER) and progesterone receptor (PR)in Chinese female BC patients under various genetic models. Age at menarche stratification indicated that rs1332184 was associated with increased risk in BC patients, whereas stratification by number of births indicated that rs10965064 was associated with reduced risk in BC patients. MDR analysis showed that the best single-locus model for predicting of BC risk are rs55683539, which, rs55683539-CC group was a high risk group and rs55683539-TT group was a low risk group.

CONCLUSIONS

The results indicated that the MIR31HG polymorphisms were associated with a reduced risk of BC in Chinese women.

Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.

MIR17HG polymorphisms contribute to high-altitude pulmonary edema susceptibility in the Chinese population

High-altitude pulmonary edema (HAPE) is a common acute altitude sickness. This study was designed to investigate the effect of MIR17HG polymorphisms on HAPE risk in the Chinese population. The Agena MassARRAY platform was used to genotype six single-nucleotide polymorphisms (SNPs) in the MIR17HG gene in 244 HAPE patients and 243 non-HAPE controls. The odds ratio (OR) and 95% confidence interval were used to evaluate the association between each MIR17HG polymorphisms and the risk of HAPE under a polygenetic model. Statistical analysis was performed using the χ² test. Multifactor dimensionality reduction (MDR) analysis was used to analyze the impacts of SNP–SNP interactions on the risk of HAPE. According to the allele model, the HAPE risk of people with the rs7318578 A allele of MIR17HG was lower than that of people with the C allele (OR 0.74, p = 0.036).Logistic regression analysis of four models for all selected MIR17HG SNPs showed significant differences in the frequencies of rs7318578 (OR 0.74, p = 0.037) and rs17735387 (OR 1.51, p = 0.036) between cases and controls. The results of the sex stratification analysis showed that among males, rs17735387 in the MIR17HG gene is associated with an increased risk of HAPE. MDR analysis showed that the best combination model was a three-locus model incorporating rs72640334, rs7318578, and rs7336610. This study revealed the correlations between rs7318578 and rs17735387 on the MIR17HG gene and the risk of HAPE in the Chinese population, providing a theoretical basis for the early screening, prevention, and diagnosis of HAPE in high-risk populations.

Relationship Between KCNQ1 Polymorphism and Type 2 Diabetes Risk in Northwestern China

Purpose

This study aimed to explore the relationship between KCNQ1 polymorphism and type 2 diabetes mellitus (T2DM) risk in the population of Northwest China.

Patients and Methods

Case-control strategy was used to reveal the correlation between KCNQ1 polymorphism and T2DM risk, and MDR analysis clarified the influence of KCNQ1 polymorphism interaction on T2DM risk. The related proteins, functions, and signal pathways of KCNQ1 were further explored through bioinformatics methods. PCR was used to explore the relative expression of KCNQ1 in T2DM patients and the controls.

Results

Studies showed that rs163177, rs163184, rs2237895 and rs2283228 on the KCNQ1 gene are closely related to the risk of T2DM in Northwest China. MDR results showed that the three-locus model is the best model for T2DM risk assessment, which increases the risk of T2DM. The bioinformatics results showed that KCNQ1 closely-acted proteins are mainly involved in signal pathways such as gastric acid secretion and renin secretion. The PCR results showed that, compared with the controls, the expression of KCNQ1 was up-regulated in T2DM patients.

Conclusion

The results revealed that KCNQ1 polymorphism is related to the risk of T2DM in the population of Northwest China and provide a scientific basis for the early screening and prevention of T2DM high-risk populations.

Association between ABHD1 and DOK6 polymorphisms and susceptibility to Hirschsprung disease in Southern Chinese children

Hirschsprung disease (HSCR) is an infrequent congenital intestinal dysplasia. The known genetic variations are unable to fully explain the pathogenesis of HSCR. The α/β‐hydratase domain 1 (ABHD1) interferes with the proliferation and migration of intestinal stem cells. Docking protein 6 (DOK6) is involved in neurodevelopment through RET signalling pathway. We examined the association of ABHD1 and DOK6 genetic variants with HSCR using 1470 controls and 1473 HSCR patients from Southern Chinese children. The results clarified that DOK6 rs12968648 G allele significantly increased HSCR susceptibility, in the allelic model (p = 0.034; OR = 1.12, 95%CI = 1.01~1.24) and the dominant model (p = 0.038; OR = 1.12, 95%CI = 1.01~1.25). Clinical stratification analysis showed that rs12968648 G allele was associated with increased risk of short‐segment HSCR (S‐HSCR), in the allelic model (p = 0.028; OR = 1.14, 95%CI = 1.01~1.28) and the additive model (p = 0.030; OR = 1.14, 95%CI = 1.01~1.28). ABHD1 rs2304678 C allele had higher risk to develop total colonic aganglionosis (TCA) in the allelic model (p = 7.04E‐03; OR = 1.67, 95%CI = 1.15~2.43) and the dominant model (p = 4.12E‐03; OR = 1.93, 95%CI = 1.23~3.04). DOK6 rs12968648 and ABHD1 rs2304678 had significant intergenic synergistic effect according to logical regression (p = 0.0081; OR = 0.76, 95%CI = 0.63~0.93) and multifactor dimensionality reduction (MDR, p = 0.0045; OR = 1.25, 95%CI = 1.07~1.46). This study verified two susceptible variations of HSCR on ABHD1 and DOK6. Their roles in HSCR should be conducted in further studies.

Influence of IGF2BP2, HMG20A, and HNF1B genetic polymorphisms on the susceptibility to Type 2 diabetes mellitus in Chinese Han population

Background: The present study aimed to investigate the roles of insulin related gene IGF2BP2, HMG20A, and HNF1B variants in the susceptibility of Type 2 diabetes mellitus (T2DM), and to identify their association with age, gender, BMI, and smoking and alcohol drinking behavior among the Han Chinese population.

Methods: About 508 patients with T2DM and 503 healthy controls were enrolled. Rs11927381 and rs7640539 in IGF2BP2, rs7178572 in HMG20A, rs4430796, and rs11651052 in HNF1B were genotyped by using the Agena MassARRAY. Odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistic regression.

Results: We found that HMG20A rs7178572 (OR = 1.25, P = 0.015) and HNF1B rs11651052 (OR = 1.26, P = 0.019) increased the risk of T2DM. Rs7178572, rs4430796, and rs11651052 might be related to the higher T2DM susceptibility not only by itself but also by interacting with age, gender smoking, and alcohol drinking. Rs11927381 also conferred the higher T2DM susceptibility at age ≤ 59 years. Besides, rs7178572-AA (P = 0.032) genotype and rs11651052 GG (P = 0.018) genotype were related to higher glycated hemoglobin and insulin level, respectively.

Conclusion: Specifically, we first found that rs11927381, rs7640539, and rs11651052 were associated with risk of T2DM among the Han Chinese population. We also provide evidence that age, gender, BMI, smoking, and drinking status have an interactive effect with these variants on T2DM susceptibility.

Role of IL-9, IL-2RA, and IL-2RB genetic polymorphisms in coronary heart disease

BACKGROUND

Coronary heart disease (CHD) is one of the leading causes of disability and death worldwide. Inflammatory cytokines play an essential role in the pathogenesis of CHD. This study aimed to detect the potential association between interleukin (IL)-9, IL-2RA, and IL-2RB variants and CHD in a Han Chinese population.

METHODS

This case-control study included 499 CHD patients and 496 healthy controls. Seven single-nucleotide polymorphisms (SNPs) were genotyped to investigate the possible association between the polymorphisms and CHD risk. Interactions between SNPs and CHD risk were analyzed via multifactor dimensionality reduction (MDR).

RESULTS

We observed an association between IL‑9 rs55692658 (OR = 1.72, p = 0.003) and increased CHD risk. Age-stratified analysis indicated that regardless of the participants' age, IL‑9 rs55692658 and IL-2RB rs1573673 contributed significantly to CHD susceptibility (p < 0.05, respectively). Results showed an association between IL‑9 rs55692658 and an increased risk for CHD (OR = 2.32, p = 0.003), while IL-2RA rs12722498 was correlated with decreased susceptibility to CHD (OR = 0.54, p = 0.033) in female patients. Furthermore, IL-2RA rs12569923 was related to diabetes risk in CHD patients (OR = 1.50, p = 0.028). The MDR analysis revealed a positive interaction between the SNPs.

CONCLUSION

The present study demonstrated that IL‑9 rs55692658, IL-2RA rs12569923, IL-2RA rs12722498, and IL-2RB rs3218264 polymorphisms might be related to CHD. The results require validation in larger studies.

Genetic polymorphisms of PGF and TNFAIP2 genes related to cervical cancer risk among Uygur females from China

BACKGROUND

PGF and TNFAIP2 are important angiogenic factors, which were abnormal expression in cervical cancer (CC). However, there is currently no report investigating the relationship of PGF and TNFAIP2 gene polymorphisms to CC risk.

METHODS

We conducted a case-control study of 342 CC patients and 498 cancer-free controls in a Chinese Uygur female population. Three SNPs (PGF rs8019391, PGF rs2268615, and TNFAIP2 rs710100) were selected and genotyped to assess the possible association of PGF and TNFAIP2 polymorphisms with CC susceptibility. Logistic regression analysis adjusted by age was used.

RESULTS

PGF rs2268615 (OR = 1.39, 95% CI = 1.04-1.86, p = 0.024) and TNFAIP2 rs710100 (OR = 1.44, 95% CI =1.07-1.95, p = 0.018) polymorphisms were associated with the increased risk of CC. Moreover, T allele of PGF rs8019391 was highly represented in patients with stage III-IV compared with stage I-II (OR = 2.17, p = 4.58 × 10- 4). MDR analysis revealed a positive interaction between the SNPs.

CONCLUSION

Our data indicated that PGF rs2268615, and TNFAIP2 rs710100 polymorphisms might be risk factors for CC susceptibility, which contributed to the increased risk of CC.

TRIAL REGISTRATION

Not applicable.

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis

OBJECTIVE

Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.

METHODS AND MATERIAL

In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.

RESULTS

We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.

CONCLUSION

FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.

Contribution of IL-7/7R genetic polymorphisms in coronary heart disease in Chinese Han population

BACKGROUND

Coronary heart disease (CHD) is a common chronic inflammatory disease. Interleukin (IL)-7/IL-7R has been reported to be involved in the development of CHD. However, the relationship between IL-7/7R genetic polymorphisms and CHD among the Han Chinese population remains unclear.

METHODS

To examine whether IL-7/7R variants contributed to CHD, six single-nucleotide polymorphisms (SNPs) were genotyped by using the Agena MassARRAY platform in 499 CHD patients and 496 controls. Logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs). The linkage disequilibrium was analyzed using Haploview software. The association between clinical parameters and IL-7/7R polymorphisms was determined by a one-way ANOVA.

RESULTS

IL-7R rs969129 G (OR = 1.20, 95% CI: 1.00-1.43, p = 0.047) allele and GG (OR = 1.45, 95% CI: 1.01-2.08, p = 0.044) genotype carriers had a higher risk for CHD. IL-7R haplotype "ACAG" (OR = 1.43, 95% CI: 1.09-1.87, p = 0.010) conferred an increased CHD risk. Rs969129, rs6451231, and rs117173992 were related to CHD susceptibility in males and/or the subgroup of individuals aged >61 years. IL-7R rs969129, rs10053847, rs6451231, and rs118137916 variants were associated with diabetes in patients with CHD. Moreover, rs969129, rs6451231, and rs117173992 were associated with high-density lipoprotein cholesterol (HDL-C) concentrations, whereas rs118137916 and rs10053847 were associated with low-density lipoprotein cholesterol (LDL-C) levels (p < 0.05).

CONCLUSION

IL-7/7R variants were related to the genetic predisposition of CHD in the Chinese Han population. These findings increase our knowledge regarding the effect of IL-7/7R on CHD.

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multifactor dimensionality reduction (MDR). Early MDR methods mainly focused on binary traits. More recently, several extensions of MDR have been developed for analyzing various traits such as quantitative traits and survival times. Newer technologies, such as genome-wide association studies (GWAS), have now been developed for assessing multiple traits, to simultaneously identify genetic variants associated with various pathological phenotypes. It has also been well demonstrated that analyzing multiple traits has several advantages over single trait analysis. While there remains a need to find GGIs for multiple traits, such studies have become more difficult, due to a lack of novel methods and software. Herein, we propose a novel multi-CMDR method, by combining fuzzy clustering and MDR, to find GGIs for multiple traits. Multi-CMDR showed similar power to existing methods, when phenotypes followed bivariate normal distributions, and showed better power than others for skewed distributions. The validity of multi-CMDR was confirmed by analyzing real-life Korean GWAS data.

Association between IL-1R2 polymorphisms and lung cancer risk in the Chinese Han population: A case-control study

BACKGROUND

Interleukin-1 receptor 2 (IL-1R2), as an anti-inflammatory cytokine, is involved in the pathogenesis and progression of lung cancer. However, the role of IL-1R2 polymorphisms in patients with lung cancer has yet to be fully elucidated.

METHODS

Six single-nucleotide polymorphisms (SNPs) in IL-1R2 were genotyped in 259 patients and 346 healthy controls. We used the chi-squared test, genetic model analysis, Haploview analysis, and multifactor dimensionality reduction (MDR) to evaluate the potential association between IL-1R2 polymorphisms and lung cancer susceptibility. Bioinformatics analyses were conducted to analyze the expression level of IL-1R2 and its association with the overall survival of lung cancer.

RESULTS

Our results found that rs3218977-GG was associated with a decreased risk of lung cancer (odds ratio [OR] = 0.39; 95% confidence interval [CI]: 0.17-0.87; p = 0.023), and rs2072472 had a significant risk-increasing effect in the dominant model (AG + GG vs. AA: OR = 1.54; 95% CI: 1.09-2.20; p = 0.015). The MDR model also revealed that rs2072472 is the most influential risk factor of lung cancer (testing accuracy = 0.543; cross-validation consistency = 10/10; p = 0.032). In addition, our results indicated that the IL-1R2 mRNA level was downregulated in lung cancer patients, whereas the high expression of IL-1R2 was related to a poor prognosis in lung cancer.

CONCLUSIONS

Our results suggest that genetic variants of IL-1R2 may play a role in lung cancer susceptibility. Further population and functional validations of our findings are warranted.

EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP (c++ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

HC-HDSD: A method of hypergraph construction and high-density subgraph detection for inferring high-order epistatic interactions

Detecting epistatic interactions, or nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), has gained increasing attention in explaining the "missing heritability" of complex diseases. Though much work has been done in mapping SNPs underlying diseases, most of them constrain to 2-order epistatic interactions. In this paper, a method of hypergraph construction and high-density subgraph detection, named HC-HDSD, is proposed for detecting high-order epistatic interactions. The hypergraph is constructed by low-order epistatic interactions that identified using the normalized co-information measure and the exhaustive search. The hypergraph consists of two types of vertices: real ones representing main effects of SNPs and virtual ones denoting interactive effects of epistatic interactions. Then, both maximal clique centrality algorithm and near-clique mining algorithm are employed to detect high-density subgraphs from the constructed hypergraph. These high-density subgraphs are inferred as high-order epistatic interactions in the HC-HDSD. Experiments are performed on several simulation data sets, results of which show that HC-HDSD is promising in inferring high-order epistatic interactions while substantially reducing the computation cost. In addition, the application of HC-HDSD on a real Age-related Macular Degeneration (AMD) data set provides several new clues for the exploration of causative factors of AMD.

HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> SPOCK1) and (LINGO2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).

Genetic modifiers as relevant biological variables of eye disorders

From early in the study of mammalian genetics, it was clear that modifiers can have a striking influence on phenotypes. Today, several modifiers have now been studied in enough detail to allow a glimpse of how they function and influence our perspective of disease. With respect to diseases of the eye, some modifiers are an important source of phenotypic variation that can elucidate how genes function in networks to collectively shape ocular anatomy and physiology, thus influencing our understanding of basic biology. Other modifiers represent an opportunity for new therapeutic targets, whose manipulation could be used to mitigate ophthalmic disease. Here, we review progress in the study of genetic modifiers of eye disorders, with examples from mice and humans that together illustrate the ubiquitous nature of genetic modifiers and why they are relevant biological variables in experimental design. Special emphasis is given to ophthalmic modifiers in mice, especially those relevant to selection of genetic background and those that might inadvertently be a source of experimental variability. These modifiers are capable of influencing interpretations of many experiments using targeted genome manipulations such as knockouts or transgenics. Whereas there are fewer examples of modifiers of eye disorders in humans with a molecular identification, there is ample evidence that they exist and should be considered as a relevant biological variable in human genetic studies as well.