Generalized Multifactor Dimensionality Reduction Method Research Articles

Impact of interaction between interleukin-6 gene polymorphism and Helicobacter pylori infection on susceptibility to gastric cancer.

This study aimed to evaluate the association between four single nucleotide polymorphisms (SNPs) of the interleukin-6 (IL-6) gene and gastric cancer (GC), and impact of interaction between IL-6 SNPs and Helicobacter pylori (H. pylori ) infection on susceptibility to GC. Logistic regression was used to test the relationships between four SNPs of IL-6 gene and GC susceptibility. A generalized multifactor dimensionality reduction (GMDR) model was employed to assess the interaction effect between IL-6 gene and H. pylori infection on GC risk. Logistic regression analysis indicated that the rs1800795-C allele was associated with increased GC risk, adjusted ORs (95% CI) were 1.80 (1.21-2.41) (CC vs. GG) and 1.68 (1.09-2.30) (C vs. G), respectively. The rs10499563-C allele was associated with decreased risk of GC, and adjusted ORs (95% CI) were 0.62 (0.31-0.93) (TC vs. TT), 0.52 (0.18-0.89) (CC vs. TT) and 0.60 (0.29-0.92) (C vs. T), respectively. GMDR methods found a two-dimensional model combination (including rs1800795 and H. pylori infection) was statistically significant. The selected model had testing balanced accuracy of 59.85% and the best cross-validation consistencies of 10/10 ( P = 0.0107). Compared with H. pylori -negative subjects with rs1800795- GG genotype, H. pylori -positive participants with GC or CC genotype had the highest risk of GC, the OR (95% CI) was 3.34 (1.78-4.97). The rs1800795-C allele was associated with increased GC risk and the rs10499563-C allele was associated with decreased GC risk. The interaction between rs1800795 and H. pylori infection was also correlated with increased risk of GC.

Genetic variant in miR-17-92 cluster binding sites is associated with esophageal squamous cell carcinoma risk in Chinese population

BackgroundSingle nucleotide polymorphisms (SNPs) located in microRNA (miRNA) binding sites can affect the interactions between miRNAs and target genes, which is related to cancer susceptibility and tumorigenesis. However, the association between SNPs located in miR-17-92 cluster binding sites and ESCC risk remains unclear. Therefore, we aimed to explore the relationship between polymorphisms in miR-17-92 cluster binding sites and ESCC susceptibility.MethodsSix SNPs in the binding sites of miR-17-92 cluster were selected using bioinformatics databases, and their association with ESCC risk was investigated in a case-control study (including 488 cases and 512 controls) based on the population from high incidence areas of ESCC in China. We evaluated the SNP-SNP and SNP-smoking interactions using generalized multifactor dimensionality reduction (GMDR). Moreover, the expression of the miR-17-92 cluster and its target genes was determined in ESCC and adjacent normal tissues by quantitative real-time polymerase chain reaction (qRT-PCR). The dual-luciferase reporter assay was conducted to verify the effect of SNPs on the binding affinity between miRNAs and target genes.ResultsWe found that the SNP rs1804506 C > T had a significant association with the decreased ESCC risk. The SNP rs1804506 T allele was associated with a significantly decreased risk of ESCC in the additive model (OR = 0.817, 95% CI = 0.681–0.981, P = 0.030). The rs1804506 T allele had more striking effects on reducing ESCC risk in older individuals, female or non-smoker subgroups. We also found a significant interaction effect between rs1366600 and smoking by GMDR methods (P = 0.011). Additionally, the expression levels of miR-19a-3p and TGFBR3 were significantly downregulated in ESCC tissues compared with normal tissues, and the carriers of rs1804506 TT genotype had lower expression level of TGFBR3 than those of rs1804506 CC/CT genotype. Following dual-luciferase reporter assay showed that the rs1804506 T allele reduced the binding of miR-19a-3p and TGFBR3 3′-UTR.ConclusionsOur findings suggest that the rs1804506 polymorphism in miR-17-92 cluster binding sites contributes to the susceptibility of ESCC, which might provide new clues and scientific evidence for the etiology and biomarkers for the prevention and treatment of ESCC.

Effects of long-term exposure to major outdoor air pollutants for the risk of chronic inflammatory airway diseases in adults with potential interaction detection in Taiwan Biobank

BackgroundResearch on the association between chronic inflammatory airway diseases (CIADs), defined as asthma, chronic obstructive pulmonary disease, and impaired lung function, and long-term exposure of outdoor air pollutants for adults has been scarce. Research questionEffects of long-term exposure for outdoor air pollutants to the risk of CIADs in adults. Materials and methodsApplying data from Taiwan Biobank, 10-year accumulative exposure of major outdoor air pollutants, PM2.5, PM10, NO2, SO2, CO, and O3, was estimated by Kriging's interpolation, based on 76 nationwide air pollution monitoring stations linked to residency information at township or district level at baseline interview for volunteered community participants older than 30 years old. Eligible cases and randomly selected healthy controls constructed a nested case-control study with frequency matching on age and sex at a ratio of 1:4. We adopted logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of outdoor air pollutants to the risk of CIADs for univariable and multivariable analyses. For detecting potential interactions among critical covariates, we used the generalized multifactor dimensionality reduction method. ResultsHigher 10-year cumulative exposure of NO2 and SO2 put people at a significantly elevated relative risk of CIADs after confounding control, showing adjusted ORs of 1.07 (95% CI: 1.01, 1.14) for NO2 and 1.23 (95% CI: 1.15, 1.31) for SO2 for each increment of standard deviation. Furthermore, a significant synergistic interaction (P-value < 0.01 for interaction term) between PM10 and CO in tertiles was detected with an adjusted OR of 5.01 (95% CI: 2.95, 8.49) for the highest tertiles of PM10 and CO combined, relative to the group of lowest tertile combination. ConclusionOur outcomes contributed to a more profound understanding on the effects of long-term exposure of air pollutants to CIADs in etiology, highlighting a synergistic interaction between PM10 and CO.

Association of maternal MTHFD1 and MTHFD2 gene polymorphisms with congenital heart disease in offspring

To study the association of maternal methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) and methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) gene polymorphisms with congenital heart disease (CHD) in offspring. A hospital-based case-control study was conducted. The mothers of 683 children with CHD alone who attended Hunan Children's Hospital, from November 2017 to March 2020 were enrolled as the case group, and the mothers of 740 healthy children who attended the same hospital during the same period and did not have any deformity were enrolled as the control group. A questionnaire survey was performed to collect related exposure data, and then venous blood samples (5 mL) were collected from the mothers to detect MTHFD1 and MTHFD2 gene polymorphisms. A multivariate logistic regression analysis was used to evaluate the association of MTHFD1 and MTHFD2 gene polymorphisms with CHD. The four-gamete test in Haploview 4.2 software was used to construct haplotypes and evaluate the association between haplotypes and CHD. The generalized multifactor dimensionality reduction method and logistic regression analysis were used to examine gene-gene interaction and its association with CHD. The multivariate logistic regression analysis showed that maternal MTHFD1 gene polymorphisms at rs11849530 (GA vs AA: OR=1.49; GG vs AA: OR=2.04) andat rs1256142 (GA vs GG: OR=2.34; AA vs GG: OR=3.25) significantly increased the risk of CHD in offspring (P<0.05), while maternal MTHFD1 gene polymorphisms at rs1950902 (AA vs GG: OR=0.57) and MTHFD2 gene polymorphisms at rs1095966 (CA vs CC: OR=0.68) significantly reduced the risk of CHD in offspring (P<0.05). The haplotypes of G-G-G (OR=1.86) and G-A-G (OR=1.35) in mothers significantly increased the risk of CHD in offspring (P<0.05). The gene-gene interaction analyses showed that the first-order interaction between MTHFD1 rs1950902 and MTHFD1 rs2236222 and the second-order interaction involving MTHFD1 rs1950902, MTHFD1 rs1256142, and MTHFD2 rs1095966 might be associated with risk of CHD (P<0.05). Maternal MTHFD1 and MTHFD2 gene polymorphisms and their haplotypes, as well as the interaction between MTHFD1 rs1950902 and MTHFD1 rs2236222 and between MTHFD1 rs1950902, MTHFD1 rs1256142, and MTHFD2 rs1095966, are associated with the risk of CHD in offspring.

The Gender-Specific Interaction of DVL3 and GSK3β Polymorphisms on Major Depressive Disorder Susceptibility in a Chinese Han Population: A Case-Control Study.

Based on the “oxidative stress hypothesis” of major depressive disorder (MDD), cells regulate their structure through the Wnt pathway. Little is known regarding the interactions of dishevelled 3 (DVL3) and glycogen synthase kinase 3 beta (GSK3β) polymorphisms with MDD. The aim of the current study was to verify the relationship between DVL3 and GSK3β genetic variants in a Chinese Han population and further to evaluate whether these interactions exhibit gender-specificity. A total of 1136 participants, consisting of 541 MDD patients and 595 healthy subjects, were recruited. Five single-nucleotide polymorphisms (SNPs) of DVL3/GSK3β were selected to assess their interaction by use of a generalized multifactor dimensionality reduction method. The genotype and haplotype frequencies of DVL3/GSK3β polymorphisms were significantly different between patients and controls for DVL3 rs1709642 (P < 0.01) and GSK3β rs334558, rs6438552, and rs2199503 (P < 0.01). In addition, our results also showed that there were significant interaction effects between DVL3 and GSK3β polymorphisms and the risk of developing MDD, particularly in women. The interaction between DVL3 (rs1709642) and GSK3β (rs334558, rs6438552) showed a cross-validation (CV) consistency of 10/10, a P value of 0.001, and a testing accuracy of 59.22%, which was considered as the best generalized multifactor dimensionality reduction (GMDR) model. This study reveals the interaction between DVL3 and GSK3β polymorphisms on MDD susceptibility in a female Chinese Han population. The effect of gender should be taken into account in future studies that seek to explore the genetic predisposition to MDD relative to the DVL3 and GSK3β genes.

Pro-inflammatory cytokine polymorphisms and interactions with dietary alcohol and estrogen, risk factors for invasive breast cancer using a post genome-wide analysis for gene\u2013gene and gene\u2013lifestyle interaction

Molecular and genetic immune-related pathways connected to breast cancer and lifestyles in postmenopausal women are not fully characterized. In this study, we explored the role of pro-inflammatory cytokines such as C-reactive protein (CRP) and interleukin-6 (IL-6) in those pathways at the genome-wide level. With single-nucleotide polymorphisms (SNPs) in the biomarkers and lifestyles together, we further constructed risk profiles to improve predictability for breast cancer. Our earlier genome-wide association gene-environment interaction study used large cohort data from the Women’s Health Initiative Database for Genotypes and Phenotypes Study and identified 88 SNPs associated with CRP and IL-6. For this study, we added an additional 68 SNPs from previous GWA studies, and together with 48 selected lifestyles, evaluated for the association with breast cancer risk via a 2-stage multimodal random survival forest and generalized multifactor dimensionality reduction methods. Overall and in obesity strata (by body mass index, waist, waist-to-hip ratio, exercise, and dietary fat intake), we identified the most predictive genetic and lifestyle variables. Two SNPs (SALL1 rs10521222 and HLA-DQA1 rs9271608) and lifestyles, including alcohol intake, lifetime cumulative exposure to estrogen, and overall and visceral obesity, are the most common and strongest predictive markers for breast cancer across the analyses. The risk profile that combined those variables presented their synergistic effect on the increased breast cancer risk in a gene–lifestyle dose-dependent manner. Our study may contribute to improved predictability for breast cancer and suggest potential interventions for the women with the risk genotypes and lifestyles to reduce their breast cancer risk.

Gene-gene and gene-lifestyle interactions of AKAP11, KCNMA1, PUM1, SPTBN1, and EPDR1 on osteoporosis risk in middle-aged adults.

Osteoporosis is associated with genetic and environmental factors. The aim of this article was to determine how the polygenic risk scores (PRS) of genetic variants that affect osteoporosis and its related signaling interact with the lifestyle of middle-aged adults. The study examined 8845 participants from Ansan/Ansung cohorts. Osteoporosis was defined as a T-score of bone mineral density ≤-2.5 in either the wrist or tibia; 1136 participants had osteoporosis. Genome-wide association studies of individuals 40 to 65 y of age were conducted and the best gene-gene interactions from the genetic variants related to osteoporosis were selected and explored using the generalized multifactor dimensionality reduction method. PRS for the best model (PRSBM) was calculated by weighted PRS that was divided into low, medium, and high groups. The model that contributed the most influence on osteoporosis risk with gene-gene interactions included AKAP11_rs238340, KCNMA1_ rs628948, PUM1_rs7529390, SPTBN1_ rs6752877, and EPDR1_rs2722298. The risk for osteoporosis in the tibia was elevated by 1.71-fold in the high PRSBM group compared with the low PRSBM group. Energy and nutrient intake did not have any interaction with PRSBM and thus did not influence risk for osteoporosis. However, interestingly, only coffee and caffeine intake did interact with PRSBM and affected risk for osteoporosis. In patients with low coffee (<3 cup/wk) and caffeine(<60 mg/d) consumption, osteoporosis risk was higher in the high PRSBM group than the low PRSBM group by 2.27- and 2.29-fold, respectively. In the low coffee intake group, bone mineral density in the high PRSBM group was significantly higher than in the low PRSBM arm. Carriers with high PRSBM increased susceptibility to osteoporosis, especially in low coffee and caffeine intake. The results can be applied to personalized nutrition for lowering the risk for osteoporosis.

Interaction between eNOS gene polymorphism and current smoking on susceptibility to coronary heart disease in Chinese people.

This study aims to explore the relation between endothelial nitric oxide synthase (eNOS) single-nucleotide polymorphisms (SNPs) and the risk of coronary heart disease (CHD). SNPstats (online software: http://bioinfo.iconcologia.net/SNPstats) was performed to test Hardy-Weinberg equilibrium in controls. Generalized multifactor dimensionality reduction (GMDR) was adopted to screen the preferable interaction between eNOS SNPs and smoking. The frequency for the rs1799983-T allele was 31.1% in CHD patients, which was significantly higher than that of 19.8% in controls (P < 0.05). The frequency for the rs891512-A allele was 28.8% in cases, which was also significantly higher than that of 20.1% in controls (P < 0.05). Logistic regression analysis showed that both rs1799983-T and rs891512-A alleles were related with increased risk of CHD, and the odds ratios (ORs) [95% confidence interval (CI)] were 1.71 (1.31-2.15) and 1.57 (1.14-2.07), respectively. High-order interactions were investigated among SNPs and environmental factors using the GMDR method. The data showed that a two-locus model (rs1799983 × smoking) had a testing accuracy of 0.60 (P = 0.001). We found that current smokers with rs1799983-GT or TT within eNOS gene have the highest CHD risk, compared to never smokers with rs1799983-GG genotype, OR (95% CI) = 2.74 (1.78-3.85), after covariates adjustment for age, gender, BMI, and alcohol drinking. The rs1799983-T and rs891512-A alleles and interaction between rs1799983 and smoking were all risk factors of CHD.

Impact of IL-10 gene polymorphisms and its interaction with environment on susceptibility to systemic lupus erythematosus.

This study aims to explore the impact of interleukin (IL)-10 single nucleotide polymorphisms (SNPs) and its interaction with environment on the risk of systemic lupus erythematosus (SLE). Chi-square testing method was used to investigate whether the distributions for genotype of four SNPs were differed from Hardy-Weinberg equilibrium (HWE). Logistic regression was used to test the association between IL-10 SNPs and SLE risk. The best interaction combinations between IL-10 SNPs and environmental factors were assessed by generalized multifactor dimensionality reduction (GMDR). Both rs1800896-G and rs1800871-T alleles were associated with increased risk of SLE, the odds ratios (ORs) (95% confidence interval (CI)) for the two SNPs were 1.68 (1.25–2.09) and 1.47 (1.12–1.94), respectively. Then, we used the GMDR method to analyze the high-order interactions of four SNPs within IL-10 gene and environmental factors on SLE risk. We found a significant interaction combination (two-locus model with P = 0.001) between rs1800896 and smoking, after adjusting for gender, age, body mass index (BMI), and alcohol drinking. We also used two-variable stratified analysis by logistic regression to analyze the synergistic effect between two variables (rs1800896 and smoking), which had significant significance in GMDR model. We found that current smokers with rs1800896-AG or GG genotype have the highest SLE risk, compared with never smokers with the rs1800896-AA genotype, OR (95% CI) = 2.24 (1.52–3.58). The rs1800896-G and rs1800871-T alleles and interaction between rs1800896 and current smoking were all associated with increased risk of SLE.

Effect of gene–gene and gene–environment interaction on the risk of first‐ever stroke and poststroke death

BackgroundMultiple genetic and environmental factors contribute to the individual‐level heterogeneity in stroke. This study aimed to assess how the genetic interactions confer risk of stroke.MethodsIn a Chinese case‐control study including 1,405 strokes and 1,263 controls who were followed up (range, 0.1–6.0 years), eight genes, including apolipoprotein(a) (APOA1), methylenetetrahydrofolate reductase (MTHFR), vitamin K epoxide reductase complex subunit 1 (VKORC1), arachidonate 5‐lipoxygenase‐activating protein (ALOX5AP), NOTCH3, chromosome 9p21.3(Chr.9p21.3), vascular endothelial growth factor (VEGFA), and kinase insert domain‐containing receptor (KDR), were analyzed for interactions by the generalized multifactor dimensionality reduction method and validated by the multivariate logistic regression models. The genetic associations with carotid artery intima‐media thickness (IMT) were examined.ResultsThe interaction of VKORC1 and Chr.9p21.3 was identified for stroke and its worse prognosis, and subjects having the VKORC1 rs2359612C and Chr.9p21.3 rs10757274G alleles had higher risks for stroke (OR = 1.83, 95% CI = 1.32–2.52) as well as for stroke recurrence (HR = 1.84, 95% CI = 1.24–2.73), cardiovascular events (HR = 1.65, 95% CI = 1.15–2.38), and cardiovascular mortality (HR = 2.16, 95% CI = 1.24–3.79). Supporting, they were associated with higher IMT. Hypertension or physical inactivity increased the risk effect. The interaction of VEGFA rs833061C and KDR rs2305948T was identified for hemorrhagic stroke.ConclusionsOur findings identified two novel genetic interactions of VKORC1 and Chr.9p21.3 and of VEGFA and KDR for risk of stroke and subtypes as well as future stroke prognosis.

Variants in matrix metalloproteinase-9 gene are associated with hemorrhagic transformation in acute ischemic stroke patients with atherothrombosis, small artery disease, and cardioembolic stroke.

ObjectiveThe potential effect of matrix metalloproteinase‐9 (MMP‐9) variants and these variants interactions on hemorrhagic transformation (HT) risk after ischemic stroke (IS) remain unclear. The aims of present study were to investigate the associations of six variants in MMP‐9 with HT, and these variants interactions whether related to increased HT risk.MethodA total of 705 patients with IS who were admitted to the participating hospitals within 48 hr of symptom onset were consecutively enrolled between March 2014 and December 2016. HT was confirmed by brain computed tomography (CT) scan during 14 days from stroke onset. Six variants of MMP‐9 gene were measured by mass spectrometry. Interactions of gene variant–gene variant were assessed through generalized multifactor dimensionality reduction method (GMDR).ResultsHT occurred in 104 (14.8%) patients. There were no differences in genotypes for the six variants between patients with and without HT using univariate analysis (all p > 0.05). GMDR analysis revealed that there was a synergistic effect of gene variant–gene variant interactions between rs3918242 and rs3787268 in MMP‐9 gene. Cox regression analysis showed that high‐risk interactions of rs3918242 and rs3787268 were associated with increased risk of HT after adjusting for covariates (hazard ratio: 2.08; 95% confidence interval: 1.34–7.85; p = 0.016).ConclusionIncidence of HT is common in acute IS in Chinese population. The mechanisms leading to HT are most likely multifactorial. Two‐loci interactions of rs3918242 and rs3787268 in MMP‐9 gene may confer a higher risk for HT.

Epistatic Interaction Between 5-HT1A and Vascular Endothelial Growth Factor Gene Polymorphisms in the Northern Chinese Han Population With Major Depressive Disorder.

Aims: Serotonin 1A receptor (5-HT1A) and vascular endothelial growth factor (VEGF) are widely expressed in the neurons of the hippocampus and have significant roles in the pathophysiological processes of major depressive disorders (MDDs). The present study was designed to examine 5-HT1A and VEGF gene polymorphisms and whether the gene–gene interaction of 5-HT1A and VEGF gene variants was associated with MDD. Methods: A total of 264 MDD patients and 264 healthy controls were included in the present genetic study. The rs6295, rs1364043, and rs878567 single-nucleotide polymorphisms (SNPs) in the 5-HT1A gene and the rs699947, rs833061, and rs2010963 SNPs in the VEGF gene were selected for genotypic analyses. The generalized multifactor dimensionality reduction method was employed to assess their interactions. Results: The genotype distributions of the two genes’ respective SNPs were significantly different between patients and controls for 5-HT1A rs6295 (p = 0.041) and VEGF rs2010963 (p = 0.035); however, no significant allelic variation in 5-HT1A (rs6295, rs1364043, and rs878567) and VEGF (rs699947, rs833061, and rs2010963) was found. The interactions between 5-HT1A (rs6295, rs1364043, and rs878567) and VEGF (rs699947, rs833061, and rs2010963) had a cross-validation (CV) consistency of 10/10 and a p value of 0.0107, which was considered as the best generalized multifactor dimensionality reduction (GMDR) model. Conclusions: The interactions between 5-HT1A and VEGF gene polymorphisms may play a key role in the development of MDD in the Northern Chinese Han population.

The Interaction of TPH2 and 5-HT2A Polymorphisms on Major Depressive Disorder Susceptibility in a Chinese Han Population: A Case-Control Study.

Purpose: TPH2 and 5-HT2A appear to play vital roles in the homeostatic regulation of serotonin levels in the brain, their genetic variations may lead to impaired homeostatic regulation of serotonin resulting in abnormal levels of serotonin in the brain, thus predisposing individuals to MDD. However, research studies have yet to confirm which gene-gene interaction effect between TPH2 and 5-HT2A polymorphisms results in increased susceptibility to MDD.Methods: A total of 565 participants, consisting of 278 MDD patients and 287 healthy controls from the Chinese Han population, were recruited for the present study. Six single nucleotide polymorphisms (SNPs) of TPH2/5-HT2A were selected to assess their interaction by use of a generalized multifactor dimensionality reduction method.Results: A-allele carriers of rs11178997 and rs120074175 were more likely to suffer from MDD than T-allele carriers of rs11178997, or G-allele carriers of rs120074175. The interaction between TPH2 (rs120074175, rs11178997) and 5-HT2A (rs7997012) was considered as the best multi-locus model upon the MDD susceptibility.Conclusions: Our data identified an important effect of TPH2 genetic variants (rs11178997 and rs120074175) upon the risk of MDD, and suggested that the interaction of TPH2/5-HT2A polymorphism variants confer a greater susceptibility to MDD in Chinese Han population.

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

Dvl3 polymorphism interacts with life events and pro-inflammatory cytokines to influence major depressive disorder susceptibility

The purpose of this study is to explore Dvl3 variants and their interaction with negative life events on MDD susceptibility in a Chinese Han population. Additionally, we also attempted to identify whether there is an association between Dvl3 variants and pro-inflammatory cytokines. A total of 1102 participants, consisting of 550 patients with MDD and 552 healthy subjects, were recruited for genotyping by TaqMan allelic discrimination assay. Pro-inflammatory cytokine mRNA levels in peripheral blood were measured by QPCR. After the assessment of negative life events by the Life Events Scale, the Dvl3 gene–environment interaction (G × E) and risk factors were evaluated using generalized multifactor dimensionality reduction method (GMDR) and logistic regression analysis, respectively. This study is the first to reveal the interaction between Dvl3 allelic variations and negative life events as well as pro-inflammatory cytokines on MDD susceptibility in a Chinese Han population.

Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions

BackgroundGene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method is an extension of MDR method that is applicable to various types of traits, and allows covariate adjustments. Our previous Fuzzy MDR (FMDR) is another extension for overcoming simple binary classification. FMDR uses continuous member-ship values instead of binary membership values 0 and 1, improving power for detecting causal SNPs and more intuitive interpretations in real data analysis. Here, we propose the fuzzy generalized multifactor dimensionality reduction (FGMDR) method, as a combined analysis of fuzzy set-based analysis and GMDR method, to detect GGIs associated with diseases using fuzzy set theory.ResultsThrough simulation studies for different types of traits, the proposed FGMDR showed a higher detection ratio of causal SNPs, compared to GMDR. We then applied FGMDR to two real data: Crohn’s disease (CD) data from the Wellcome Trust Case Control Consortium (WTCCC) with a binary phenotype and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) data from Korean population with a continuous phenotype. The interactions derived by our method include the pre-reported interactions associated with phenotypes.ConclusionsThe proposed FGMDR performs well for GGI detection with covariate adjustments. The program written in R for FGMDR is available at http://statgen.snu.ac.kr/software/FGMDR.

Association study of interaction of tryptophan hydroxylase 2 with serotonin 1A receptor gene polymorphism on major depressive disorder

Objective To explore the interaction between tryptophan hydroxylase 2(TPH2) gene polymorphisms (rs4570625, rs11178997) and serotonin 1A receptor(5-HT1A) gene polymorphisms (rs878567, rs1364043, rs6265) and the association with major depressive disorder (MDD) in a Chinese Han population. Methods The DNA isolated from peripheral blood samples of 288 MDD patients 288 healthy subjects was detected by single base primer extension assay (Snapshot). The generalized multifactor dimensionality reduction (GMDR) method was used to analyze the gene-gene interaction. Results Significant differences were found in the genotype(patients (TT: 27, TA: 152, AA: 109), controls (TT: 82, TA: 105, AA: 101), P<0.01) and allele(patients (T: 206, A: 370), controls (T: 269, A: 307), P<0.01) frequencies of rs11178997 within TPH2 between MDD patients and controls. Statistically, a greater risk of developing MDD was found in individuals with an rs11178997 A-allele(OR=1.574, 95%CI=1.243-1.993). The interaction between TPH2 (rs4570625, rs11178997) and 5-HT1A (rs878567, rs1364043, rs6265) was considered as the best multi-locus model, and this showed a testing accuracy of 57.67% and a CV consistency of 10/10. And this interaction had a significant effect on the risk of MDD (P=0.0107). Conclusion There may be an association between the interaction of TPH2 and 5-HT1A polymorphisms and MDD. Key words: Major depressive disorder; Tryptophan hydroxylase 2; Serotonin 1A receptor; Gene-gene interaction

Interactions among the variants of insulin-related genes and nutrients increase the risk of type 2 diabetes

Asians easily develops type 2 diabetes (T2DM) since they have insufficient glucose-stimulated insulin secretion (GSIS) in insulin resistant states. Since this may be associated with genetic background, the hypothesis of this study was that inter-genetic and gene-nutrient interactions may explain the low insulin secretory capacity of Asians. Accordingly, we identified the best gene-gene and gene-nutrient interactions using generalized multifactor dimensionality reduction (GMDR) in a large Korean cohort (n=8,842). Initially, we used 105 genetic variants associated with GSIS to identify the best gene-gene interaction model using the GMDR method. The best model included six SNPs, FNBP1L-rs4847428, FNBP1L-rs23766, GLIS3-rs2027393, GLIS3-rs3892354, GLIS3-rs486163 and DLC1-rs17093957. For each individual, we obtained the genetic risk scores based on the best model (GRSBM) to predict the GSIS levels. The GRSBM were divided into low, medium and high groups, and the association between T2DM and the GRSBM was measured using logistic regression. We analyzed the interaction between the GRSBM and the nutrition intakes. The adjusted odds ratios for T2DM risk increased by 1.701 fold in the high-score group compared to the low-score group. HOMA-B, an index of insulin secretion capacity, but not insulin resistance index was much lower in the high-score group than the low-score group. The association between the GRSBM and T2DM risk was greater in subjects with high energy intakes and low Ca intake, than those with low energy intake and high Ca intake. The high-score group was susceptible to T2DM incidence due to lower GSIS than the low-score group especially in subjects with high energy intake. In conclusion, the hypothesis of the study was accepted. These findings suggested that individuals with high GRSBM of the 6 genes in the model should avoid diets in high energy and low in calcium (<500 mg/day) to protect against T2DM.

Impact of SNP-SNP interaction among ABCB1, ARRB2, DRD1 and OPRD1 on methadone dosage requirement in Han Chinese patients.

To evaluate the potential association of the genetic polymorphisms in ABCB1, ARRB2, DRD1 and OPRD1 genes with methadone dosage requirement among Han Chinese opioid-dependent patients. Eight SNPs in ABCB1, ARRB2, DRD1 and OPRD1 genes were selected and genotyped using Sequenom MassARRAY platform among 257 methadone maintenance treatment patients. The required information about stable methadone dose, urine analysis for opioid and socio-demographic characteristics was collected. Generalized multifactor dimensionality reduction method was performed to analyze the SNP-SNP interaction. We found that patients carrying the rs529520TG genotype of OPRD1 probably required higher methadone treatment dosage. A 3-locus SNP-SNP interaction pattern (rs1128503 in ABCB1, rs529520 in OPRD1 and rs1045280 in ARRB2) was significantly associated with the methadone dosage requirement (p=0.029). Our results suggested that specific OPRD1 variants and interaction among polymorphisms in ABCB1, OPRD1 and ARRB2 genes contributes to methadone dosage requirement in Han Chinese opioid-dependent patients.

Association study on interaction effects of CREB1 gene and BDNF gene polymorphisms and recurrent major depressive disorder

Objective To investigate the interactions between cAMP-response element-binding protein 1(CREB1)gene polymorphisms (rs889895, rs3770704, rs2551645, rs4675690)and brain derived neurotrophic factor(BDNF)gene polymorphisms (rs7124442, rs10835210) and the association with recurrent major depressive disorder. Methods The blood samples were taken from 768 recurrent major depressive disorder patients and 511 healthy controls.The DNA was isolated from blood samples and was detected by SNP Sequenom Mass Array analysis. Chi-square test was used to compare differences in the frequency distribution of alleles and genotype between depression and controls.The generalized multifactor dimensionality reduction (GMDR) method was used to analyze the gene-gene interaction.Binary logistic regression was used to verify the optimal model. Results After adjusting the factors of sex and age, the GMDR analysis showed rs10835210 was the optimal model.In this model, the testing balanced accuracy was 0.5319 and cross-validation consistency value was 10/10.And rs10835210 had a statistically significant effect on the risk of recurrent major depressive disorder(P=0.0107). There was no significant gene-gene interaction of five tag SNPs on recurrent major depressive disorder(P>0.05). Binary logistic regression analysis showed the AC contributed to a significantly lower risk of recurrent major depressive disorder than did the CC(OR=0.772, 95%CI=0.608-0.980, P=0.033). It was failed to find the genetic polymorphism of CREB1 rs889895. Conclusion BDNF rs10835210 may be one of the biological markers of recurrent major depressive disorder. Key words: Recurrent major depressive disorder; cAMP-response element binding protein; BDNF; Tag SNP; Generalized multifactor dimensionality reduction