Model-based Multifactor Dimensionality Reduction Research Articles

MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

Maternal Age at Menarche Gene Polymorphisms Are Associated with Offspring Birth Weight.

In this study, the association between maternal age at menarche (AAM)-related polymorphisms and offspring birth weight (BW) was studied. The work was performed on a sample of 716 pregnant women and their newborns. All pregnant women underwent genotyping of 50 SNPs of AAM candidate genes. Regression methods (linear and Model-Based Multifactor Dimensionality Reduction (MB-MDR)) with permutation procedures (the indicator pperm was calculated) were used to identify the correlation between SNPs and newborn weight (transformed BW values were analyzed) and in silico bioinformatic examination was applied to assess the intended functionality of BW-associated loci. Four AAM-related genetic variants were BW-associated including genes such as POMC (rs7589318) (βadditive = 0.202/pperm = 0.015), KDM3B (rs757647) (βrecessive = 0.323/pperm = 0.005), INHBA (rs1079866) (βadditive = 0.110/pperm = 0.014) and NKX2-1 (rs999460) (βrecessive = -0.176/pperm = 0.015). Ten BW-significant models of interSNPs interactions (pperm ≤ 0.001) were identified for 20 polymorphisms. SNPs rs7538038 KISS1, rs713586 RBJ, rs12324955 FTO and rs713586 RBJ-rs12324955 FTO two-locus interaction were included in the largest number of BW-associated models (30% models each). BW-associated AAM-linked 22 SNPs and 350 proxy loci were functionally related to 49 genes relevant to pathways such as the hormone biosynthesis/process and female/male gonad development. In conclusion, maternal AMM-related genes polymorphism is associated with the offspring BW.

Brazilian Multiethnic Association Study of Genetic Variant Interactions among FOS, CASP8, MMP2 and CRISPLD2 in the Risk of Nonsyndromic Cleft Lip with or without Cleft Palate

Associations of CRISPLD2 (cysteine-rich secretory protein LCCL domain containing 2) and genes belonging to its activation pathway, including FOS (Fos proto-oncogene), CASP8 (caspase 8) and MMP2 (matrix metalloproteinase 2), with nonsyndromic orofacial cleft risk, have been reported, but the results are yet unclear. The aim of this study was to evaluate single nucleotide polymorphisms (SNPs) in FOS, CASP8 and MMP2 and to determine their SNP-SNP interactions with CRISPLD2 variants in the risk of nonsyndromic cleft lip with or without cleft palate (NSCL±P) in the Brazilian population. The SNPs rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) were genotyped using TaqMan allelic discrimination assays in a case-control sample containing 801 NSCL±P patients (233 nonsyndromic cleft lip only (NSCLO) and 568 nonsyndromic cleft lip and palate (NSCLP)) and 881 healthy controls via logistic regression analysis adjusted for the effects of sex and genomic ancestry proportions with a multiple comparison p value set at ≤0.01. SNP-SNP interactions with rs1546124, rs8061351, rs2326398 and rs4783099 in CRISPLD2 were performed with the model-based multifactor dimensionality reduction test complemented with a 1000 permutation-based strategy. Although the association between FOS rs1046117 and risk of NSCL±P reached only nominal p values, NSCLO risk was significantly higher in carriers of the FOS rs1046117 C allele (OR: 1.28, 95% CI: 1.10-1.64, p = 0.004), TC heterozygous genotype (OR: 1.59, 95% CI: 1.16-2.18, p = 0.003), and in the dominant model (OR: 1.50, 95% CI: 1.10-2.02, p = 0.007). Individually, no significant associations between cleft risk and the SNPs in CASP8 and MMP2 were observed. SNP-SNP interactions involving CRISPLD2 variants and rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) yielded several significant p values, mostly driven by FOS rs1046117 and CASP8 rs3769825 in NSCL±P, FOS rs1046117 in NSCLO and CRISPLD2 rs8061351 in NSCLP. Our study is the first in the Brazilian population to reveal the association of FOS rs1046117 with NSCLO risk, and to support that CRISPLD2, CASP8, FOS and MMP2 interactions may be related to the pathogenesis of this common craniofacial malformation.

Matrix Metalloproteinase Gene Polymorphisms Are Associated with Breast Cancer in the Caucasian Women of Russia.

We conducted this study to explore the association between matrix metalloproteinase (MMP) gene polymorphisms and breast cancer (BC) risk in the Caucasian women of Russia. In total, 358 affected (BC) and 746 unaffected (cancer-free) women were included in this case-control retrospective study. From BC-related genes in previous studies, ten single nucleotide polymorphisms (SNPs) in five MMP genes (MMP1, 2, 3, 8, 9) were genotyped. The BC risk was calculated by logistic regression (to evaluate the SNPs’ independent effects) and model-based multifactor dimensionality reduction (MB-MDR) (to identify SNP–SNP interactions) methods. The allelic variants’ distribution of c.836 A > G (rs17576) and c. 1721 C > G (rs2250889) MMP9 was significantly different between BC and cancer-free women: for G minor alleles, these SNPs manifested disorder protective effects (OR 0.82 and OR 0.67–0.71, respectively, pperm ≤ 0.035). Eleven haplotypes of six SNPs MMP9 were involved in BC risk (nine haplotypes) and protective (two haplotypes) effects. All 10 SNPs of the MMP genes examined were associated with BC within the 13 SNP–SNP interaction simulated models, with a pivotal role of the two-locus (rs17577 × rs3918242) MMP9 epistatic interaction (defined as 1.81% BC entropy within more than 60% of the genetic models). Under in silico bioinformatics, BC susceptibility MMP polymorphic loci are located in functionally active genome regions and impact genes expression and splicing “regulators” in the mammary gland. The biological pathways of BC MMP candidate genes are mainly realized due to metalloendopeptidase activity and extracellular matrix organization (structure, disassembly, metabolic process, etc.). In conclusion, our data show that MMP gene polymorphisms are related to BC susceptibility in the Caucasian women of Russia.

Role of intergenic interactions among folate cycle genes in the development of fetal growth retardation

Objective: Metabolic disturbances in the folate cycle in mothers can lead to fetal growth retardation (FGR). This study was to analyze the role of intergenic interactions among maternal folate cycle genes in the development of FGR. Methods: This case-control study recruited 365 women in the third trimester of pregnancy, including 122 FGR patients and 243 controls. The women were genotyped for 5 polymorphisms of the 4 folate cycle genes: MTR (rs1805087), MTRR (rs1801394), serine hydroxymethyl transferase (SHMT1; rs1979277), and TYMS (rs699517 and rs2790). The SNP × SNP interactions in the two-, three-, and four-locus models were analyzed using the multifactor dimensionality reduction method and a modification of it (the model-based multifactor dimensionality reduction method). Results: Four loci of maternal folate cycle genes (rs1805087 MTR, rs2790 TYMS, rs1801394 MTRR, and rs1979277 SHMT1) were associated with FGR in 3 significant models of single nucleotide polymorphism (SNP) × SNP interactions (two-, three-, and four-locus models) (P <0.05). The highest contribution to FGR was made by polymorphic loci rs1979277 SHMT1 (1.70% of entropy), rs1805087 MTR (0.96%), and interactions between rs1979277 SHMT1 × rs1805087 MTR (-1.11%) and rs1801394 MTRR × rs1979277 SHMT1 (−0.64%). The four-locus maternal genotype combination AG rs1801394 MTRR × AA rs1805087 MTR × CT rs1979277 SHMT1 × AG rs2790 TYMS was associated with an increased risk of FGR (β = 2.69, P = 0.012). FGR-associated SNPs were correlated with the expression of 16 genes (MTR, MTRR, SHMT1, ALKBH5, CTD-2303H24.2, ENOSF1, FAM106A, FOXO3B, LGALS9C, LLGL1, MIEF2, NOS2P2, RP11-806L2.6, SMCR8, TOP3A, and USP32P2) in various tissues and organs related to FGR pathophysiology. Conclusion: SNP × SNP interactions of maternal folate cycle genes (MTR, MTRR, SHMT1, and TYMS) are associated with the development of FGR.

A comparative study on the unified model based multifactor dimensionality reduction methods for identifying gene-gene interactions associated with the survival phenotype

BackgroundFor gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely employed to reduce multi-levels of gene-gene interactions into high- or low-risk groups using a binary attribute. For the survival phenotype, the Cox-MDR method has been proposed using a martingale residual of a Cox model since Surv-MDR was first proposed using a log-rank test statistic. Recently, the KM-MDR method was proposed using the Kaplan-Meier median survival time as a classifier. All three methods used the cross-validation procedure to identify single nucleotide polymorphism (SNP) using SNP interactions among all possible SNP pairs. Furthermore, these methods require the permutation test to verify the significance of the selected SNP pairs. However, the unified model-based multifactor dimensionality reduction method (UM-MDR) overcomes this shortcoming of MDR by unifying the significance testing with the MDR algorithm within the framework of the regression model. Neither cross-validation nor permutation testing is required to identify SNP by SNP interactions in the UM-MDR method. The UM-MDR method comprises two steps: in the first step, multi-level genotypes are classified into high- or low-risk groups, and an indicator variable for the high-risk group is defined. In the second step, the significance of the indicator variable of the high-risk group is tested in the regression model included with other adjusting covariates. The Cox-UMMDR method was recently proposed by combining Cox-MDR with UM-MDR to identify gene-gene interactions associated with the survival phenotype. In this study, we propose two simple methods either by combining KM-MDR with UM-MDR, called KM-UMMDR or by modifying Cox-UMMDR by adjusting for the covariate effect in step 1, rather than in step 2, a process called Cox2-UMMDR. The KM-UMMDR method allows the covariate effect to be adjusted for in the regression model of step 2, although KM-MDR cannot adjust for the covariate effect in the classification procedure of step 1. In contrast, Cox2-UMMDR differs from Cox-UMMDR in the sense that the martingale residuals are obtained from a Cox model by adjusting for the covariate effect in step 1 of Cox2-UMMDR whereas Cox-UMMDR adjusts for the covariate effect in the regression model in step 2. We performed simulation studies to compare the power of several methods such as KM-UMMDR, Cox-UMMDR, Cox2-UMMDR, Cox-MDR, and KM-MDR by considering the effect of covariates and the marginal effect of SNPs. We also analyzed a real example of Korean leukemia patient data for illustration and a short discussion is provided.ResultsIn the simulation study, two different scenarios are considered: the first scenario compares the power of the cases with and without the covariate effect. The second scenario is to compare the power of cases with the main effect of SNPs versus without the main effect of SNPs. From the simulation results, Cox-UMMDR performs the best across all scenarios among KM-UMMDR, Cox2-UMMDR, Cox-MDR and KM-MDR. As expected, both Cox-UMMDR and Cox-MDR perform better than KM-UMMDR and KM-MDR when a covariate effect exists because the former adjusts for the covariate effect but the latter cannot. However, Cox2-UMMDR behaves similarly to KM-UMMDR and KM-MDR even though there is a covariate effect. This implies that the covariate effect would be more efficiently adjusted for in the regression model of the second step rather than under the classification procedure of the first step. When there is a main effect of any SNP, Cox-UMMDR, Cox2-UMMDR and KM-UMMDR perform better than Cox-MDR and KM-MDR if the main effects of SNPs are properly adjusted for in the regression model. From the simulation results of two different scenarios, Cox-UMMDR seems to be the most robust when there is either any covariate effect adjusting for or any SNP that has a main effect on the survival phenotype. In addition, the power of all methods decreased as the censoring fraction increased from 0.1 to 0.3, as heritability increased. The power of all methods seems to be greater under MAF = 0.2 than under MAF = 0.4. For illustration, both KM-UMMDR and Cox2-UMMDR were applied to identify SNP by SNP interactions with the survival phenotype to a real dataset of Korean leukemia patients.ConclusionBoth KM-UMMDR and Cox2-UMMDR were easily implemented by combining KM-MDR and Cox-MDR with UM-MDR, respectively, to detect significant gene-gene interactions associated with survival time without cross-validation and permutation testing. The simulation results demonstrate the utility of KM-UMMDR, Cox2-UMMDR and Cox-UMMDR, which outperforms Cox-MDR and KM-MDR when some SNPs with only marginal effects might mask the detection of causal epistasis. In addition, Cox-UMMDR, Cox2-UMMDR and Cox-MDR performed better than KM-UMMDR and KM-MDR when there were potentially confounding covariate effects.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

BackgroundIn genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.MethodsTo identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC.ResultsSimulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.ConclusionWe demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Empowering individual trait prediction using interactions for precision medicine

BackgroundOne component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction.ResultsUsing a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package (https://github.com/imbs-hl/MBMDRClassifieR).ConclusionsThe explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

A Novel Exon Polymorphism of TP53 Associated with Recurrent Pregnancy Loss in a Chinese Han Population.

To identify recurrent pregnancy loss (RPL)-related genetic variants in exons of TP53 gene in a population of Chinese Han women. This study is a case control study. The cases comprised 90 Chinese Han women with RPL. Another 90 women with at least one child and not more than one miscarriage were recruited as the controls. All exons of TP53 were amplified from genomic DNA and sequenced. A total of five single-nucleotide polymorphisms (SNPs) were identified in both RPL and control women, namely rs1642785 G>C, rs1042522 G>C, rs4968187 G>A, rs17884306 G>A, and rs55817367 A>G. A significant difference was only observed for rs17884306 between cases and controls. The wild type G allele was associated with an increased risk of RPL. AA+GA genetic variants significantly decreased the risk of RPL compared with GG variant (odds ratio [OR] = 0.315, 95% confidence interval [CI]: 0.125 - 0.793, p = 0.014). Linkage disequilibrium exits between rs17884306 and rs1642785 and A-C double mutant haplotype showed significantly lower risk of RPL compared with G-G wild type haplotype (OR = 0.303, 95% CI: 0.117 - 0.786, p = 0.014). Model based-multifactor dimensionality reduction indicated that the influence of rs17884306 had interaction with the genotypes of four other loci (all p < 0.05). However, rs17884306 G>A did not cause amino acid substitution. Our study showed that rs17884306 c.826G>A was a novel polymorphism associated with RPL in Chinese Han women. Moreover, the influence of this SNP on RPL is not associated with p53 amino acid sequence.

Association Analysis of TP53 rs1042522, MDM2 rs2279744, rs3730485, MDM4 rs4245739 Variants and Acute Myeloid Leukemia Susceptibility, Risk Stratification Scores, and Clinical Features: An Exploratory Study.

This study aimed to explore the associations between the TP53 rs1042522 (TP53 Arg72Pro), MDM2 rs2279744 (MDM2 309T>G), rs3730485 (MDM2 del1518), MDM4 rs4245739 (MDM4 34091 C>A) variants and odds of developing acute myeloid leukemia (AML) in a cohort of 809 adult subjects, consisting of 406 healthy controls and 403 AML patients. Model-based multifactor dimensionality reduction (MB-MDR) framework was used to identify the interactions of the mentioned variants and their association with AML risk. Associations of the mentioned variants with clinical features of AML, somatic mutations, and response to treatment were also evaluated. Significant associations between TP53 rs1042522 and MDM4 rs4245739 variants and AML susceptibility were noticed. MB-MDR and logistic regression analysis revealed an interaction between MDM2 rs2279744 and TP53 rs1042522, between MDM4 rs4245739 and MDM2 rs3730485, as well as significant associations with AML susceptibility. Several associations between the mentioned variants and clinical features of AML and somatic mutations were also noticed. Individually, the variant genotypes of TP53 rs1042522 and MDM4 rs4245739 were associated with AML susceptibility, but their interaction with MDM2 rs2279744 and rs3730485 modulated the risk for AML. The variant genotypes of TP53 rs1042522 were associated with adverse molecular and cytogenetic risk and also with NPM1 mutations.

ERAP1 polymorphisms interactions and their association with Behçet's disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR).

Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is endoplasmic reticulum aminopeptidase-1 (ERAP1). In this study, we aimed to investigate the interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Model-based multifactor dimensionality reduction (MB-MDR). We have included 748 BD patients and 776 healthy controls. A peripheral blood sample was collected, and eleven SNPs were assessed. Furthermore, we have applied the MB-MDR method to evaluate the interactions of ERAP1 gene polymorphisms. The TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β = 12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third-order (β = 12.74 and β = 12.73, respectively). To the best of our knowledge, this is the first study investigating the interaction of a particular gene's SNPs in BD patients by applying a novel data mining method. However, future studies investigating the interactions of various genes could clarify this issue.

Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies

BackgroundIn Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs.ResultsUsing real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block.ConclusionsOur results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

Interaction Between AGTR1 and PPARγ Gene Polymorphisms on the Risk of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is an important public health issue worldwide. Several recent studies have reported that peroxisome proliferator-activated receptor-γ (PPARγ) and angiotensin II type 1 receptor (AGTR1) variants are associated with NAFLD occurrence, but the results have been inconsistent. The aim of this study was to analyze the interactions between PPARγ and AGTR1 polymorphisms and their associations with NAFLD in Chinese adults. Seven single nucleotide polymorphisms (SNPs) of the PPARγ gene and 5 SNPs of the AGTR1 gene were selected and genotyped in 1591 unrelated Chinese adults. The SNPAssoc package of R was used to examine the relationships between the selected SNPs and NAFLD. After adjusting the covariance, the results from the overdominant model showed that participants carrying the T/C genotype of rs2638360 in AGTR1 have a decreased risk of NAFLD compared with those with T/T-C/C genotypes (odds ratio: 0.70, 95% confidence interval: 0.49-1.00). However, our results showed that none of the selected PPARγ variants were significantly associated with the risk of NAFLD after applying a false discovery rate correction. Among the 12 selected SNPs from PPARγ and AGTR1, model-based multifactor dimensionality reduction (MB-MDR) analyses for gene-gene interactions revealed that all the models were significantly associated with the increased risk of NAFLD (p < 0.05) except the 2-, 10-, 11-, and 12-locus models. Further, among the 10 SNPs negatively associated with NAFLD, the four-locus model (rs13431696 and rs3856806 in PPARγ, and rs5182, rs1492100 in ATGR1) and the five-locus model (rs9817428, rs1175543, rs13433696, and rs2920502 in PPARγ, and rs1492100 in ATGR1) were closely related with NAFLD susceptibility (p = 0.019 and p = 0.048, respectively). Our present study suggests that interactions among multiple AGTR1 and PPARγ polymorphisms are associated with the risk of NAFLD in the Chinese population.

Relationships of SLC2A4, RBP4, PCK1, and PI3K Gene Polymorphisms with Gestational Diabetes Mellitus in a Chinese Population.

Background Solute carrier family 2 member 4- (SLC2A4-) retinol binding protein-4- (RBP4-) phosphoenolpyruvate carboxykinase 1 (PCK1)/phosphoinositide 3-kinase (PI3K) is an adipocyte derived “signalling pathway” that may contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). We explored whether single nucleotide polymorphisms (SNPs) of these “signalling pathway” genes are associated with gestational diabetes mellitus (GDM). Methods Case-control studies were conducted to compare GDM and control groups. A total of 334 cases and 367 controls were recruited. Seventeen candidate SNPs of the pathway were selected. Chi-square tests, logistic regression, and linear regression were used to estimate the relationships of SNPs with GDM risk and oral glucose tolerance test (OGTT), fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) levels. Model-based multifactor dimensionality reduction was used to estimate the adjusted interactions between genes. Regression and interaction analyses were adjusted by maternal age, prepregnancy BMI, and weekly BMI growth. The Bonferroni correction was applied for multiple comparisons. Results RBP4 rs7091052 was significantly associated with GDM risk. SLC2A4 rs5435, RBP4 rs7091052, PCK1 rs1042531 and rs2236745, and PIK3R1 (coding gene of the PI3K P85 subunit) rs34309 were associated with OGTT, fasting insulin, and HOMA-IR levels in the linear regression analysis. The gene-gene interaction analysis showed that, compared with pregnant women with other genotype combinations, women with SLC2A4 rs5435 (CC/CT), RBP4 rs7091052 (CC), PCK1 rs1042531 (TT/TG) and rs2236745 (TT), and PIK3R1 rs34309 (AA) had lower GDM risk. Conclusion SLC2A4, RBP4, PCK1, and PIK3R1 genes may be involved in the pathogenesis of GDM.

Unified Cox model based multifactor dimensionality reduction method for gene-gene interaction analysis of the survival phenotype

BackgroundOne strategy for addressing missing heritability in genome-wide association study is gene-gene interaction analysis, which, unlike a single gene approach, involves high-dimensionality. The multifactor dimensionality reduction method (MDR) has been widely applied to reduce multi-levels of genotypes into high or low risk groups. The Cox-MDR method has been proposed to detect gene-gene interactions associated with the survival phenotype by using the martingale residuals from a Cox model. However, this method requires a cross-validation procedure to find the best SNP pair among all possible pairs and the permutation procedure should be followed for the significance of gene-gene interactions. Recently, the unified model based multifactor dimensionality reduction method (UM-MDR) has been proposed to unify the significance testing with the MDR algorithm within the regression model framework, in which neither cross-validation nor permutation testing are needed. In this paper, we proposed a simple approach, called Cox UM-MDR, which combines Cox-MDR with the key procedure of UM-MDR to identify gene-gene interactions associated with the survival phenotype.ResultsThe simulation study was performed to compare Cox UM-MDR with Cox-MDR with and without the marginal effects of SNPs. We found that Cox UM-MDR has similar power to Cox-MDR without marginal effects, whereas it outperforms Cox-MDR with marginal effects and more robust to heavy censoring. We also applied Cox UM-MDR to a dataset of leukemia patients and detected gene-gene interactions with regard to the survival time.ConclusionCox UM-MDR is easily implemented by combining Cox-MDR with UM-MDR to detect the significant gene-gene interactions associated with the survival time without cross-validation and permutation testing. The simulation results are shown to demonstrate the utility of the proposed method, which achieves at least the same power as Cox-MDR in most scenarios, and outperforms Cox-MDR when some SNPs having only marginal effects might mask the detection of the causal epistasis.

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.

Interactions Between PPARG and AGTR1 Gene Polymorphisms on the Risk of Hypertension in Chinese Han Population.

To explore the interactions between PPARG and AGTR1 polymorphisms and their associations with hypertension in the Chinese Han population. Seven single nucleotide polymorphisms (SNPs) of the PPARG gene and five SNPs of the AGTR1 gene were selected and genotyped in 1591 unrelated Chinese Han adults. The SNPAssoc package of R was used to analyze the associations between the selected SNPs and hypertension. The potential gene-gene interactions between PPARG and AGTR1 genes were tested by model-based multifactor dimensionality reduction (MB-MDR). The frequencies of the C allele of rs3856806 and the G allele of rs13433696 in the PPARG gene were significantly lower in hypertensive subjects, whereas the A allele of rs9817428 in the PPARG gene was much higher in hypertensives. In addition, individuals with T allele of rs2933249 in the AGTR1 gene displayed a significantly decreased risk of hypertension. MB-MDR analyses suggested that the two-locus model (rs9817428 and rs2933249) and the three-locus model (rs9817428, rs3856806, and rs2933249) were significantly associated with a decreased risk of hypertension. Moreover, among the eight SNPs not individually associated with hypertension (rs12631819, rs2920502, rs1175543, and rs2972164 in the PPARG gene, and rs2638360, rs1492100, rs5182, and rs275646 in the AGTR1 gene), the two-locus model involving rs12631819 and rs5182 demonstrated increased susceptibility to hypertension, and the five-locus model involving rs12631819, rs2920502, rs2972164, rs5182, and rs2638360 demonstrated a significantly decreased risk of hypertension. Polymorphisms in both the PPARG and AGTR1 genes were found to be significantly associated with hypertension. Moreover, there were significant gene-gene interactions identified between the PPARG and AGTR1 genes in relation to hypertension susceptibility in the Chinese Han population.

Evidence of an epistatic effect between Dysbindin-1 and Neuritin-1 genes on the risk for schizophrenia spectrum disorders

BackgroundThe interest in studying gene–gene interactions is increasing for psychiatric diseases such as schizophrenia-spectrum disorders (SSD), where multiple genes are involved. Dysbindin-1 (DTNBP1) and Neuritin-1 (NRN1) genes have been previously associated with SSD and both are involved in synaptic plasticity. We aimed to study whether these genes show an epistatic effect on the risk for SSD. MethodsThe sample comprised 388 SSD patients and 397 healthy subjects. Interaction was tested between: (i) three DTNBP1 SNPs (rs2619537, rs2743864, rs1047631) related to changes in gene expression; and (ii) an haplotype in NRN1 previously associated with the risk for SSD (rs645649-rs582262: HAP-risk C-C). ResultsAn interaction between DTNBP1 rs2743864 and NRN1 HAP-risk was detected by using the model based multifactor dimensionality reduction (MB-MDR) approach (P=0.0049, after permutation procedure), meaning that the risk for SSD is significantly higher in those subjects carrying both the A allele of rs2743864 and the HAP-risk C-C. This interaction was confirmed by using a logistic regression model (P=0.033, OR (95%CI)=2.699 (1.08–6.71), R2=0.162). DiscussionOur results suggest that DTNBP1 and NRN1 genes show a joint effect on the risk for SSD. Although the precise mechanism underlying this effect is unclear, the fact that these genes have been involved in synaptic maturation, connectivity and glutamate signalling suggests that our findings could be of value as a link to the schizophrenia aetiology.

Identification of interactions using model-based multifactor dimensionality reduction.

BackgroundCommon complex traits may involve multiple genetic and environmental factors and their interactions. Many methods have been proposed to identify these interaction effects, among them several machine learning and data mining methods. These are attractive for identifying interactions because they do not rely on specific genetic model assumptions. To handle the computational burden arising from an exhaustive search, including all possible combinations of factors, filter methods try to select promising factors in advance.MethodsModel-based multifactor dimensionality reduction (MB-MDR), a semiparametric machine learning method allowing adjustment for confounding variables and lower level effects, is applied to Genetic Analysis Workshop 19 (GAW19) data to identify interaction effects on different traits. Several filtering methods based on the nearest neighbor algorithm are assessed in terms of compatibility with MB-MDR.ResultsSingle nucleotide polymorphism (SNP) rs859400 shows a significant interaction effect (corrected p value <0.05) with age on systolic blood pressure (SBP). We identified 23 SNP–SNP interaction effects on hypertension status (HS), 42 interaction effects on SBP, and 26 interaction effects on diastolic blood pressure (DBP). Several of these SNPs are in strong linkage disequilibrium (LD). Three of the interaction effects on HS are identified in filtered subsets.ConclusionsThe considered filtering methods seem not to be appropriate to use with MB-MDR. LD pruning is further quality control to be incorporated, which can reduce the combinatorial burden by removing redundant SNPs.

A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGI effects. However, there are several disadvantages of the existing MDR-based approaches, such as the lack of an efficient way of evaluating the significance of multi-locus models and the high computational burden due to intensive permutation. Furthermore, the MDR method does not distinguish marginal effects from pure interaction effects. We propose a two-step unified model based MDR approach (UM-MDR), in which, the significance of a multi-locus model, even a high-order model, can be easily obtained through a regression framework with a semi-parametric correction procedure for controlling Type I error rates. In comparison to the conventional permutation approach, the proposed semi-parametric correction procedure avoids heavy computation in order to achieve the significance of a multi-locus model. The proposed UM-MDR approach is flexible in the sense that it is able to incorporate different types of traits and evaluate significances of the existing MDR extensions. The simulation studies and the analysis of a real example are provided to demonstrate the utility of the proposed method. UM-MDR can achieve at least the same power as MDR for most scenarios, and it outperforms MDR especially when there are some single nucleotide polymorphisms that only have marginal effects, which masks the detection of causal epistasis for the existing MDR approaches. UM-MDR provides a very good supplement of existing MDR method due to its efficiency in achieving significance for every multi-locus model, its power and its flexibility of handling different types of traits. A R package "umMDR" and other source codes are freely available at http://statgen.snu.ac.kr/software/umMDR/ CONTACT: tspark@stats.snu.ac.kr Supplementary data are available at Bioinformatics online.