ARID5B, IKZF1, GATA3, CEBPE, and CDKN2A germline polymorphisms and predisposition to childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is the most frequent type of pediatric cancer. Germline single nucleotide polymorphisms (SNPs), including ARID5B (rs10821936 T/C), IKZF1 (rs4132601 T/G), GATA3 (rs3824662 G/T), CEBPE (rs2239633 G/A), and CDKN2A (rs3731217 A/C) have been linked to pediatric ALL in different populations. Hitherto, no previous studies have tested the relationship between these SNPs and pediatric ALL in Gaza strip. Therefore, we investigated the association between these polymorphisms and the occurrence of childhood ALL in this part of Palestine. This case-control study recruited 100 healthy controls and 78 ALL patients. Allele-specific PCR (AS-PCR) technique was used for SNPs genotyping. Relevant statistical tests were used and the multifactor dimensionality reduction (MDR) approach was applied in the analysis of gene-gene interactions. Minor alleles of ARID5B rs10821936 T/C (p = 0.007) and IKZF1 rs4132601 T/G (p = 0.045) were significantly higher in ALL patients. The homozygous (TT) genotype of GATA3 rs3824662 G/T (p = 0.038), (CC) of ARID5B rs10821936 T/C (p = 0.008), and (AC and CC) genotypes of CDKN2A rs3731217 A/C (p < 0.0001) were significantly higher in ALL cases. On MDR analysis, the best model for ALL risk was the five-factor model combination of the examined SNPs (CVC = 10/10; TBA = 0.632; p < 0.0001). This work demonstrates the association of ARID5B rs10821936 T/C, IKZF1 rs4132601 T/G, GATA3 rs3824662 G/T, and CDKN2A rs3731217 A/C polymorphisms with increased risk of pediatric ALL among a patient cohort from Gaza Strip. Further studies with a larger sample size are needed in order to confirm these findings and test the value of these SNPs in prognosis and treatment sensitivity.

Association of adipocytokine pathway gene polymorphisms with NAFLD in obese children. / è„‚è‚ªç»†èƒžå› å­é€šè·¯åŸºå› å¤šæ€æ€§ä¸Žè‚¥èƒ–å„¿ç«¥éžé ’ç²¾æ€§è„‚è‚ªæ€§è‚ç— çš„å ³è”.

OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) has significant genetic susceptibility. Adipocytokines play a crucial role in NAFLD development by participating in insulin resistance and hepatic steatosis. However, the association between adipocytokine pathway genes and NAFLD remains unclear. This study aims to explore the association of gene polymorphisms in the adipocytokine pathway and their interactions with NAFLD in obese children. METHODS: A case-control study was conducted, dividing obese children into NAFLD and control groups. Peripheral venous blood (2 mL) was collected from each participant for DNA extraction. A total of 14 single nucleotide polymorphisms (SNP) in the adipocytokine pathway were genotyped using multiplex PCR and high-throughput sequencing. Univariate and multivariate Logistic regression analyses were used to assess the association between SNP and NAFLD in obese children. Dominant models were used to analyze additive and multiplicative interactions via crossover analysis and Logistic regression. Generalized multifactor dimensionality reduction (GMDR) was used to detect gene-gene interactions among the 14 SNPs and their association with NAFLD in obese children. RESULTS: A total of 1 022 children were included, with 511 in the NAFLD group and 511 in the control group. After adjusting for age, gender, and BMI, multivariate Logistic regression showed that PPARG rs1801282 was associated with NAFLD in the obese children in 3 genetic models: heterozygote model (CG vs CC, OR=0.58, 95% CI 0.36 to 0.95, P=0.029), dominant model (GG+CG vs CC, OR=0.62, 95% CI 0.38 to 1.00, P=0.049), and overdominant model (CC+GG vs CG, OR=1.72, 95% CI 1.06 to 2.80, P=0.028). PRKAG2 rs12703159 was associated with NAFLD in 4 genetic models: heterozygous model (CT vs CC, OR=1.51, 95% CI 1.10 to 2.07, P=0.011), dominant model (CT+TT vs CC, OR=1.50, 95% CI 1.10 to 2.03, P=0.010), overdominant model (CC+TT vs CT, OR=0.67, 95% CI 0.49 to 0.92, P=0.012), and additive model (CC vs CT vs TT, OR=1.40, 95% CI 1.07 to 1.83, P=0.015). No significant multiplicative or additive interaction between PPARG rs1801282 and PRKAG2 rs12703159 was found in association with NAFLD. GMDR analysis, adjusted for age, gender, and BMI, revealed no statistically significant interactions among the 14 SNPs (all P>0.05). CONCLUSIONS: Mutations in PPARG rs1801282 and PRKAG2 rs12703159 are associated with NAFLD in obese children. However, no gene-gene interactions among the SNP are found to be associated with NAFLD in obese children.

Suggestive evidence of the genetic association of TMOD1 and PTCSC2 polymorphisms with thyroid carcinoma in the Chinese Han population.

BACKGROUND: The purpose of this study was to survey the associations of six single nucleotide polymorphisms (SNPs) in the TMOD1 and PTCSC2 genes with thyroid carcinoma (TC). METHOD: Peripheral blood samples were obtained from 510 patients with TC and 509 normal controls. Six SNPs were genotyped by the Agena MassARRAY platform. Logistic regression was used to evaluate the association between SNPs and TC susceptibility by calculating odds ratios (ORs) and 95% confidence intervals (CIs). SNP-SNP interactions were analyzed by multifactor dimensionality reduction (MDR). RESULTS: Our study showed that rs925489 (OR = 1.45, p = 0.011) and rs965513 (OR = 1.40, p = 0.021) were significantly associated with an increased risk of TC. Rs10982622 decreased TC risk (OR = 0.74, p = 0.025). Further stratification analysis showed that rs10982622 reduced the susceptibility to TC in patients aged ≤ 45 years (OR = 0.69, p = 0.019) and in females (OR = 0.61, p = 0.014). Rs925489 increased TC risk in people aged > 45 years (OR = 1.54, p = 0.044) and in males (OR = 2.34, p = 0.003). In addition, rs965513 was related to an increased risk of TC in males (OR = 2.14, p = 0.007). Additionally, haplotypes in the block (rs925489|rs965513) significantly increased TC risk (p < 0.05). The best predictive model for TC was the combination of rs1052270, rs10982622, rs1475545, rs16924016, and rs925489. CONCLUSION: TMOD1 and PTCSC2 polymorphisms were separately correlated with a remarkable decrease and increase in TC risk based on the analysis.

Associations between WNT signaling pathway-related gene polymorphisms and risks of osteoporosis development in Chinese postmenopausal women: a case-control study.

BACKGROUND: The WNT signaling pathway is involved in the regulation of bone homeostasis, and the effect of WNT signaling pathway-related gene (WNT16 and LRP5) polymorphisms on osteoporosis risk among Chinese postmenopausal women is still unknown. Hence, we performed a case-control study to assess the association of WNT signaling pathway-related gene polymorphisms and osteoporosis risk. METHODS: A total of 1026 women (515 osteoporosis patients and 511 controls) of postmenopausal age who were randomly sampled from Xi'an 630 Hospital (Shaanxi Province, China) were involved in this study. Seven genetic polymorphisms in WNT16 (rs3779381, rs3801387, rs917727 and rs7776725) and LRP5 (rs2291467, rs11228240 and rs12272917) were selected and genotyped using the Agena MassARRAY iPLEX system. The association of the genetic polymorphisms and osteoporosis risk was assessed by odds ratios and 95% confidence intervals. The multifactor dimensionality reduction (MDR) method was conducted to analyze single nucleotide polymorphism (SNP)-SNP interaction. RESULTS: We found that LRP5 polymorphisms (rs2291467, rs11228240 and rs12272917) were significantly associated with a decreased risk of osteoporosis in homozygote, recessive and additive models (p < 0.05). Stratification analysis showed that LRP5 polymorphisms (rs2291467, rs11228240 and rs12272917) significantly decreased the osteoporosis risk in the subgroup of body mass index (BMI) ≤ 24 (p < 0.05) and that individuals carrying a heterozygote genotype of WNT16 polymorphisms (rs3779381, rs3801387, rs917727 and rs7776725) had a higher osteoporosis risk in the subgroup of BMI > 24 (p < 0.05). Two haplotypes (haplotype 1: rs3779381, rs3801387, rs917727 and rs7776725; haplotype 2: rs2291467 and rs11228240) were observed, yet only Trs2291467Trs11228240 and Crs2291467Crs11228240 had a strong association with a decreased risk of osteoporosis (p < 0.05). Additionally, MDR analysis revealed that LRP5 rs2291467 was the best model in single-locus MDR analysis. A seven-locus model including rs3779381-AG, rs7776725-TC, rs3801387-GA and rs917727-TC in WNT16 and rs11228240-CC, rs12272917-TC and rs2291467-CC in LRP5 was the best model in multiple-loci MDR analysis (p < 0.001). These two best models were the most significantly associated with osteoporosis risk. CONCLUSIONS: Our findings suggested that WNT16 and LRP5 genetic polymorphisms are associated with osteoporosis risk among Chinese postmenopausal women.

Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.

BACKGROUND: Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene-gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling's T2 statistic to evaluate interaction models, but it is well known that Hotelling's T2 statistic is highly sensitive to heavily skewed distributions and outliers. RESULTS: We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR . CONCLUSIONS: Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.

Evaluation of 17 genetic variants in association with leukemia in the north Indian population using MassARRAY Sequenom.

Leukemia is a heterogeneous disorder, characterized by elevated proliferation of white blood cells. In this study, we explored the association of 17 genetic variants with leukemia patients in the Jammu and Kashmir region of north India. The variants were genotyped by using a high-throughput Agena MassARRAY platform in 758 individuals (166 cases and 592 controls). Of the 17 single-nucleotide polymorphisms (SNPs) studied, five SNPs were showing significant association with the high risk of leukemia in the north Indian population, which includes rs10069690 of telomere reverse transcriptase (TERT) with OR = 0.34 (95% CI, 0.20-0.58; p = .0008), rs2972392 (​​​PSCA) with OR 1.86 (95% CI, 1.04-3.81; p = .035), rs4986764 (BRIP1) with OR 1.34 (95% CI, 1.00-1.80; p = .04), rs6990097 (TNKS) with OR 1.81 (95% CI, 1.2-2.6; p = .001) and rs12190287 (TCF21) with OR 2.87 (95% CI, 1.72-4.7; p = .0001) by allelic association using Plink and analyzed by SPSS. This is the first study to explore these variants with leukemia in the studied population.

Association of vitamin D receptor gene polymorphisms with gestational diabetes mellitus-a case control study in Wuhan, China.

BACKGROUND: Gestational diabetes mellitus (GDM) increased risk of perinatal complications for both the women and the fetuses. The association between the vitamin D receptor (VDR) gene polymorphism and GDM has not been thoroughly investigated in Chinese pregnant women. Therefore, we aimed to determine whether VDR gene single nucleotide polymorphisms (SNPs) rs154410, rs7975232, rs731236, rs2228570 and rs739837 contribute to GDM risk in Wuhan, China. Moreover, we aimed to explore their combined effects on the risk of GDM. METHODS: Pregnant women who had prenatal examinations at 24 to 28 weeks' gestation in our hospital from January 15, 2018 to March 31, 2019 were included in this case-control study. After exclusion, a total of 1684 pregnant women (826 GDM patients and 858 non-diabetic controls) were recruited. The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of candidate SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test and logistic regression were performed to the data with SPSS Software to evaluate differences in genotype distribution and associations with GDM risk. Multifactor dimensionality reduction method was used to explore the gene-gene interactions on the risk of GDM. RESULTS: Differences in age, pre-pregnancy BMI, family history of diabetes and previous history of GDM between the case and control groups were statistically significant (P < 0.05), whereas no significant differences were found in height, gravidity, parity, and age of menarche (P > 0.05). There were no significant differences at genotype distributions of the examined VDR gene SNPs (P > 0.05). After adjusting by age, pre-pregnancy BMI, family history of diabetes, the results of logistic regression analysis showed no associations of the five SNPs with GDM in all the four genotype models(P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the five examined VDR gene SNPs. CONCLUSIONS: The VDR gene SNPs rs154410, rs7975232, rs731236, rs2228570 and rs739837 showed neither significant associations nor gene-gene interactions with GDM in Wuhan, China.

Combined Genotype Effects of TP53 and PAI-1 Polymorphisms in Breast Cancer Susceptibility: Multifactor Dimensionality Reduction and in silico Analysis.

INTRODUCTION: Breast cancer is a heterogeneous and multifactorial disease. TP53 and PAI-1 as important tumor suppressor genes are involved in the development, invasion, and metastasis of many cancers. This study's objective was to demonstrate the combined genotype effects of these 2 genes by investigating their single nucleotide polymorphisms. METHODS: In this case-control study, 200 individuals with breast cancer and 179 healthy individuals were studied. The genotypes were determined using the tetra-ARMS method. For data analysis, MDR, online javstat statistics package, and SPSS v.24 software were used. Also, in silico studies on the estimated effects of each of these polymorphisms were performed. RESULTS: We showed a novel gene-gene interaction of these 2 genes and demonstrated a strong synergistic interaction for TP53/PAI-1, moderate synergistic interaction for PAI-1/age, and correlation for TP53/age. On the other hand, there was no association between the allelic and genotype frequency alone and in combination, with case-control status, using the parametric method, between TP53 and PAI-1. DISCUSSION/CONCLUSION: Our findings suggest that the polymorphism of codon 72 of the TP53 gene was significantly associated with tumor stage (p < 0.023). In conclusion, we showed a gene-gene interaction between TP53 and PAI-1, in combination, using the MDR method.

DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of complex diseases. Multifactor dimensionality reduction (MDR) is a decent solution for epistasis detection. Existing MDR-based methods still suffer from high computational costs or poor performance. In this paper, we propose a new solution that integrates a dual screening strategy with MDR, termed as DualWMDR. Particularly, the first screening employs an adaptive clustering algorithm with part mutual information (PMI) to group single nucleotide polymorphisms (SNPs) and exclude noisy SNPs; the second screening takes into account both the single-locus effect and interaction effect to select dominant SNPs, which effectively alleviates the negative impact of main effects and provides a much smaller but accurate candidate set for MDR. After that, MDR uses the weighted classification evaluation to improve its performance in epistasis identification on the candidate set. The results on diverse simulation datasets show that DualWMDR outperforms existing competitive methods, and the results on three real genome-wide datasets: the age-related macular degeneration (AMD) dataset, breast cancer (BC), and celiac disease (CD) datasets from the Wellcome Trust Case Control Consortium, again corroborate the effectiveness of DualWMDR.

Multifactor dimensionality reduction reveals a strong gene-gene interaction between STC1 and COL11A1 genes as a possible risk factor of knee osteoarthritis.

Articular cartilage is an avascular tissue with a structure that allows it to support and cushion the overload of the surfaces in contact. It maintains its metabolic functions due to the contribution of different signaling pathways. However, several factors play a role in its deterioration, allowing to the development of osteoarthritis (OA), and one of the major factors is genetic. Our goal was to identify gene-gene interactions (epistasis) between five signaling pathways involved in the articular cartilage metabolism as possible indicators of OA risk. We applied the Multifactor-Dimensionality Reduction (MDR) method to identify and characterize the epistasis between 115 SNPs located in 73 genes related to HIF-1α, Wnt/ß-catenin, cartilage extracellular matrix metabolism, oxidative stress, and uric acid transporters. Ninety three patients diagnosed with primary knee OA and 150 healthy controls were included in the study. Genotyping was performed with the OpenArray system, the statistical analysis was carried out with the STATA software v14, and epistasis was analyzed with the MDR software v3.0.2. The MDR analysis revealed that the best interaction model was between polymorphisms rs17786744 of the STC1 gene and rs2615977 of the COL11A1 gene, with an entropy value of 4.44%, CVC 8/10, OR 5.60, 95% CI 3.27-9.59, p < 0.0001. Under this interaction model, we identified high and low risk genotypes involved in OA development. Our results suggest complex interactions between STC1 and COL11A1 genes that might have an impact on genetic susceptibility to develop OA. Further studies are required to confirm it.

Genetic interaction analysis of VEGF-A rs3025039 and VEGFR-2 rs2071559 identifies a genetic profile at higher risk to develop nodular goiter.

CONTEXT: Nodular goiter in patients from areas of iodine deficiency is due to the growth of follicular and endothelial cells, involving different vascular-related growth factors in its pathogenesis. OBJECTIVE: The aim of our study was to examine the association of known single polymorphisms of vascular endothelial growth factor-A [VEGF-A], VEGF receptor-2 [VEGFR-2] and hypoxia-inducible factor-1α [HIF-1α] genes or their genetic interactions with the risk of nodular goiter development. PATIENTS AND METHODS: 116 normal subjects, without any thyroid disease, and 108 subjects with nodular goiter [subjects with goiter and at least one thyroid nodule of > 1 cm of maximum size and in absence of signs of autoimmunity] were selected from a homogeneous population living in a mild iodine deficiency geographic area. Analyses were performed on germline DNA obtained from blood samples and VEGF-A rs3025039, VEGFR-2 rs2071559, and HIF-1αrs11549465 SNPs were investigated by real-time PCR technique. The multifactor dimensionality reduction [MDR] methodology was applied to investigate the genetic interaction between SNPs. Hardy-Weinberg equilibrium was performed. RESULTS: None of the studied polymorphisms were individually associated with a higher risk to develop nodular goiter [P > 0.05]. The combination of the VEGF-A rs3025039 and VEGFR-2 rs2071559 polymorphisms had the highest accuracy of 0.58 [P = 0.018] and the interaction of some genotypes was significantly associated with the risk of nodular goiter development. CONCLUSIONS: Our results support a genetic interaction between the VEGF-A rs3025039 and VEGFR-2 rs2071559 polymorphisms as a predictor of the risk to develop nodular goiter in subjects coming from an area with mild iodine deficiency.

Contribution of CYP24A1 variants in coronary heart disease among the Chinese population.

BACKGROUND: Cytochrome P450 (CYPs) participate in the mechanisms of cardiovascular disease. The purpose of this research was to evaluate the contributions of CYP24A1 variants to coronary heart disease (CHD) among the Chinese Han population. METHODS: This study included 505 CHD cases and 508 controls. Four variants of CYP24A1 (rs2762934, rs1570669, rs6068816 and rs2296241) were chosen and genotyped by the Agena MassARRAY system among the Chinese population. The linkage between CYP24A1 variants and CHD risk were assessed by logistic regression to compute the odds ratio (OR) and 95% confidence interval (CI). Then, multifactor dimensionality reduction (MDR) was applied to analyze the interactions of CYP24A1 variants. RESULTS: The results of this study showed that CYP24A1 rs6068816 significantly enhanced CHD risk in multiple genetic models (allele: P = 0.014; codominant: P = 0.015; dominant: P = 0.043; recessive: P = 0.040; additive: P = 0.013), whereas rs2296241 was likely to protect individuals from CHD (codominant: P = 0.019; recessive: P = 0.013; additive: P = 0.033). Stratification analysis revealed that CYP24A1 polymorphisms had strong relationships with CHD risk that were dependent on age, sex, Gensini grade and smoking status (P <  0.05). Moreover, a four-locus model (rs2762934, rs1570669, rs6068816 and rs2296241) had significant impact on CHD risk in MDR analysis. CONCLUSION: It revealed that CYP24A1 variants were significantly linked with CHD susceptibility in the Chinese population.

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.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.

Epistasis between ADIPOQ rs1501299 and PON1 rs662 polymorphisms is potentially associated with the development of knee osteoarthritis.

Overweight produces oxidative stress (OS) on the articular cartilage, with the subsequent risk of developing knee osteoarthritis (OA). Associations between genetic polymorphisms related to OS and OA have been reported, but it is currently unknown whether there exist interactions among them that affect OA development. To identify and evaluate interactions between multiple SNPs related to OS in Mexican knee OA patients. Ninety-two knee OA patients were included in the study, which were compared to 147 healthy controls. Nine variants of six genes (PEPD, AGER, IL6, ADIPOQ, PON1, and CA6) related to OS were genotyped in both study groups through the OpenArray system. Epistasis was analyzed with the multifactor dimensionality reduction (MDR) method. The MDR analysis revealed a significant interaction (p = 0.0107) between polymorphisms rs1501299 (ADIPOQ) and rs662 (PON1), with an entropy value of 9.84%; in addition, high and low risk genotypes were identified between these two polymorphisms. The effect of the interaction between rs1501299 (ADIPOQ) and rs662 (PON1) polymorphisms seems to play an important role in OA pathogenesis; so the epistasis analysis may provide an excellent tool for identifying individuals at high risk for developing OA.

Non-additive effects of ACVR2A in preeclampsia in a Philippine population.

BACKGROUND: Multiple interrelated pathways contribute to the pathogenesis of preeclampsia, and variants in susceptibility genes may play a role among Filipinos, an ethnically distinct group with high prevalence of the disease. The objective of this study was to examine the association between variants in maternal candidate genes and the development of preeclampsia in a Philippine population. METHODS: A case-control study involving 29 single nucleotide polymorphisms (SNPs) in 21 candidate genes was conducted in 150 patients with preeclampsia (cases) and 175 women with uncomplicated normal pregnancies (controls). Genotyping for the GRK4 and DRD1 gene variants was carried out using the TaqMan Assay, and all other variants were assayed using the Sequenom MassARRAY Iplex Platform. PLINK was used for SNP association testing. Multilocus association analysis was performed using multifactor dimensionality reduction (MDR) analysis. RESULTS: Among the clinical factors, older age (P <  1 × 10-4), higher BMI (P <  1 × 10-4), having a new partner (P = 0.006), and increased time interval from previous pregnancy (P = 0.018) associated with preeclampsia. The MDR algorithm identified the genetic variant ACVR2A rs1014064 as interacting with age and BMI in association with preeclampsia among Filipino women. CONCLUSIONS: The MDR algorithm identified an interaction between age, BMI and ACVR2A rs1014064, indicating that context among genetic variants and demographic/clinical factors may be crucial to understanding the pathogenesis of preeclampsia among Filipino women.

Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification.

BACKGROUND: Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. RESULTS: Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. CONCLUSION: The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.

A roadmap to multifactor dimensionality reduction methods.

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Capturing pair-wise epistatic effects associated with three agronomic traits in barley.

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.

Machine learning algorithm-based risk prediction model of coronary artery disease.

In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction models using ensemble machine learning algorithms (EMLA), multifactor dimensionality reduction (MDR) and recursive partitioning (RP). The EMLA model showed better performance than other models in disease (89.3%) and stenosis prediction (82.5%). This model depicted hypertension and alcohol intake as the key predictors of CAD risk followed by cSHMT C1420T, GCPII C1561T, diabetes, GSTT1, CYP1A1 m2, TYMs 5'-UTR 28 bp tandem repeat and MTRR A66G. MDR and RP models are in agreement in projecting increasing age, hypertension and cSHMTC1420T as the key determinants interacting in modulating CAD risk. Receiver operating characteristic curves exhibited clinical utility of the developed models in the following order: EMLA (C = 0.96) > RP (C = 0.83) > MDR (C = 0.80). The stenosis prediction model showed that xenobiotic pathway genetic variants i.e. CYP1A1 m2 and GSTT1 are the key determinants of percentage of stenosis. Diabetes, diet, alcohol intake, hypertension and MTRR A66G are the other determinants of stenosis. These eleven variables contribute towards 82.5% stenosis. To conclude, the EMLA model exhibited higher predictability both in terms of disease prediction and stenosis prediction. This can be attributed to higher number of iterations in EMLA model that can increase the prediction accuracy.