A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

Association Between CYP2D7 and TCF20 Polymorphisms and Coronary Heart Disease

One of the causes of coronary heart disease (CHD) is genetic factors. In this study, we explored the relationship between CYP2D7 and TCF20 gene polymorphisms and the risk of CHD in the Han Chinese population. Three single nucleotide polymorphisms (CYP2D7 rs1800754, CYP2D7 rs2743461, and TCF20 rs760648) were selected and genotyped from 490 cases and 480 controls. The odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the association between CYP2D7 and TCF20 polymorphisms and the risk of CHD. The association between clinical indicators and polymorphisms was analyzed using one-way ANOVA and Tukey's HSD. The SNP-SNP interactions were obtained by performing multifactor dimensionality reduction (MDR). CYP2D7 rs1800754 and rs2743461 were closely associated with increased risk of CHD (alleles: p = 0.014, p = 0.031). Stratified analysis showed that CYP2D7 rs1800754 and rs2743461 were associated with an increased risk of CHD in men, age > 60 years, BMI ≥ 24, and smoking. Rs1800754 is also associated with an increased risk of CHD associated with alcohol consumption. In addition, TCF20 rs760648 was associated with a reduced risk of CHD in patients aged ≤ 60 years and with CALs. A significant association was found between CYP2D7 rs1800754 and rs2743461 genotypes and levels of UREA, Cr, and LDL-C; TCF20 rs760648 genotypes and levels of RBC. The MDR analysis showed that the three-locus interaction model was the best in the multi-locus model. In conclusion, CYP2D7 rs1800754 and rs2743461 polymorphisms were associated with CHD risk.

The rs6576457 G > A variant in the MKRN3 gene promoter significantly increases the risk of central precocious puberty and lung cancer in Hubei Chinese population

Makorin RING finger protein 3 (MKRN3) is a key inhibitor of the hypothalamic-pituitary-gonadal (HPG) axis. The association between MKRN3 gene variants and central precocious puberty (CPP) has been repeatedly examined. In a recent study, MKRN3 has been assigned a role of tumor suppressor in lung carcinogenesis. Therefore, it is hypothesized that MKRN3 may be the link between CPP and lung cancer (LC), and certain MKRN3 gene variants may affect individuals' susceptibility to CPP and LC. The rs12441287, rs6576457 and rs2239669 in the MKRN3 gene were selected as the target variants. Sanger sequencing was applied to genotype them in two sets of case-control cohorts, namely 384 CPP girls and 422 healthy girls, 550 LC patients and 800 healthy controls. The results showed that rs6576457 but not rs12441287 or rs2239669 was significantly associated with the risk of CPP and LC. Their association with CPP risk was further confirmed in the following meta-analysis. Subsequent functional assays revealed that the rs6576457 genotypes were correlated with differentially expressed MKRN3, and the rs6576457 alleles affected the transcription repressor Oct-1 binding affinity to the MKRN3 promoter. Collectively, the MKRN3 gene rs6576457 may participate in the CPP pathology and LC tumorigenesis in the Hubei Chinese population. However, the present findings should be validated in additional investigations with larger samples from different ethnic populations.

Polymorphic variants of the hOGG1, APEX1, XPD, SOD2, and CAT genes involved in DNA repair processes and antioxidant defense and their association with breast cancer risk

Breast cancer is one of the leading causes of mortality among women. The most frequently encountered tumors are luminal tumors. Associations of polymorphisms in the hOGG1 (rs1052133), APEX1 (rs1130409), XPD (rs13181), SOD2 (rs4880), and CAT (rs1001179) genes were studied in 313 nonsmoking postmenopausal patients with luminal B subtype breast cancer. The control group consisted of 233 healthy nonsmoking postmenopausal women. Statistically significant associations of the XPD and APEX1 gene polymorphisms with the risk of developing luminal B Her2-negative subtype of breast cancer were observed in a log-additive inheritance model, while the CAT gene polymorphism showed an association in a dominant inheritance model (OR = 1.41; CI 95 %: 1.08-1.85; Padj.= 0.011; OR = 1.39; CI 95 %: 1.07-1.81; Padj = 0.013 и OR = 1.70; CI 95 %: 1.19-2.43; Padj = 0.004, respectively). In the group of elderly women (aged 60-74 years), an association of the CAT gene polymorphism with the risk of developing luminal B subtype of breast cancer was found in a log-additive inheritance model (OR = 1.87; 95 % CI: 1.22-2.85; Padj = 0.0024). Using MDR analysis, the most optimal statistically significant 3-locus model of gene-gene interactions in the development of luminal B Her2-negative subtype breast cancer was found. MDR analysis also showed a close interaction and mutual enhancement of effects between the APEX1 and SOD2 loci and the independence of the effects of these loci from the CAT locus in the formation of luminal B subtype breast cancer.

Metabotropic glutamate receptor genetic variants and peripheral receptor expression affects trait scores of autistic probands

Glutamate (Glu) is important for memory and learning. Hence, Glu imbalance is speculated to affect autism spectrum disorder (ASD) pathophysiology. The action of Glu is mediated through receptors and we analyzed four metabotropic Glu receptors (mGluR/GRM) in Indo-Caucasoid families with ASD probands and controls. The trait scores of the ASD probands were assessed using the Childhood Autism Rating Scale2-ST. Peripheral blood was collected, genomic DNA isolated, and GRM5 rs905646, GRM6 rs762724 & rs2067011, and GRM7 rs3792452 were analyzed by PCR/RFLP or Taqman assay. Expression of mGluRs was measured in the peripheral blood by qPCR. Significantly higher frequencies of rs2067011 'A' allele/ AA' genotype were detected in the probands. rs905646 'A 'exhibited significantly higher parental transmission. Genetic variants showed independent as well as interactive effects in the probands. Receptor expression was down-regulated in the probands, especially in the presence of rs905646 'AA', rs762724 'TT', rs2067011 'GG', and rs3792452 'CC'. Trait scores were higher in the presence of rs762724 'T' and rs2067011 'G'. Therefore, in the presence of risk genetic variants, down-regulated mGluR expression may increase autistic trait scores. Since our investigation was confined to the peripheral system, in-depth exploration involving peripheral as well as central nervous systems may validate our observation.

Maternal Age at Menarche Genes Determines Fetal Growth Restriction Risk

We aimed to explore the potential link of maternal age at menarche (mAAM) gene polymorphisms with risk of the fetal growth restriction (FGR). This case (FGR)-control (FGR free) study included 904 women (273 FGR and 631 control) in the third trimester of gestation examined/treated in the Departments of Obstetrics. For single nucleotide polymorphism (SNP) multiplex genotyping, 50 candidate loci of mAAM were chosen. The relationship of mAAM SNPs and FGR was appreciated by regression procedures (logistic/model-based multifactor dimensionality reduction [MB-MDR]) with subsequent in silico assessment of the assumed functionality pithy of FGR-related loci. Three mAAM-appertain loci were FGR-linked to genes such as KISS1 (rs7538038) (effect allele G-odds ratio (OR)allelic = 0.63/pperm = 0.0003; ORadditive = 0.61/pperm = 0.001; ORdominant = 0.56/pperm = 0.001), NKX2-1 (rs999460) (effect allele A-ORallelic = 1.37/pperm = 0.003; ORadditive = 1.45/pperm = 0.002; ORrecessive = 2.41/pperm = 0.0002), GPRC5B (rs12444979) (effect allele T-ORallelic = 1.67/pperm = 0.0003; ORdominant = 1.59/pperm = 0.011; ORadditive = 1.56/pperm = 0.009). The haplotype ACA FSHB gene (rs555621*rs11031010*rs1782507) was FRG-correlated (OR = 0.71/pperm = 0.05). Ten FGR-implicated interworking models were founded for 13 SNPs (pperm ≤ 0.001). The rs999460 NKX2-1 and rs12444979 GPRC5B interplays significantly influenced the FGR risk (these SNPs were present in 50% of models). FGR-related mAAM-appertain 15 polymorphic variants and 350 linked SNPs were functionally momentous in relation to 39 genes participating in the regulation of hormone levels, the ovulation cycle process, male gonad development and vitamin D metabolism. Thus, this study showed, for the first time, that the mAAM-appertain genes determine FGR risk.

Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia

In our work, the associations of GWAS (genome-wide associative studies) impact for sex-hormone-binding globulin (SHBG)-level SNPs with the risk of breast cancer (BC) in the cohort of Caucasian women of Russia were assessed. The work was performed on a sample of 1498 women (358 BC patients and 1140 control (non BC) subjects). SHBG correlated in previously GWAS nine polymorphisms such as rs780093 GCKR, rs17496332 PRMT6, rs3779195 BAIAP2L1, rs10454142 PPP1R21, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs440837 ZBTB10, rs12150660 SHBG, and rs8023580 NR2F2 have been genotyped. BC risk effects of allelic and non-allelic SHBG-linked gene SNPs interactions were detected by regression analysis. The risk genetic factor for BC developing is an SHBG-lowering allele variant C rs10454142 PPP1R21 ([additive genetic model] OR = 1.31; 95%CI = 1.08-1.65; pperm = 0.024; power = 85.26%), which determines 0.32% of the cancer variance. Eight of the nine studied SHBG-related SNPs have been involved in cancer susceptibility as part of nine different non-allelic gene interaction models, the greatest contribution to which is made by rs10454142 PPP1R21 (included in all nine models, 100%) and four more SNPs-rs7910927 JMJD1C (five models, 55.56%), rs17496332 PRMT6 (four models, 44.44%), rs780093 GCKR (four models, 44.44%), and rs440837 ZBTB10 (four models, 44.44%). For SHBG-related loci, pronounced functionality in the organism (including breast, liver, fibroblasts, etc.) was predicted in silico, having a direct relationship through many pathways with cancer pathophysiology. In conclusion, our results demonstrated the involvement of SHBG-correlated genes polymorphisms in BC risk in Caucasian women in Russia.

COMT and SCN9A gene variants do not contribute to chronic low back pain in Mexican-Mestizo patients

BACKGROUND

Chronic low back pain (CLBP) is a complex condition in which genetic factors play a role in its susceptibility. Catechol-O-methyltransferase (COMT) and sodium channel NaV1.7 (SCN9A) genes are implicated in pain perception. The aim is to analyze the association of COMT and SCN9A with CLBP and their interaction, in a Mexican-Mestizo population.

METHODS

A case-control study was conducted. Cases corresponded to adults of both sexes with CLBP. Controls were adults with no CLBP. Variants of SCN9A and COMT were genotyped. Allelic and genotypic frequencies and Hardy-Weinberg equilibrium (HWE) were calculated. Association was tested under codominant, dominant, and recessive models. Multifactor dimensionality reduction was developed to detect epistasis.

RESULTS

Gene variants were in HWE, and there was no association under different inheritance models in the whole sample. In women, in codominant and dominant models, a trend to a high risk was observed for AA of rs4680 of COMT (OR = 1.7 [0.5-5.3] and 1.6 [0.7-3.4]) and for TT of rs4633 (OR = 1.6 [0.7-3.7] and 1.6 [0.7-3.4]). In men, a trend to low risk was observed for AG genotype of rs4680 in the same models (OR = 0.6 [0.2-1.7] and 0.7 [0.3-1.7]), and for TC genotype of rs4633 in the codominant model (OR = 0.6 [0.2-1.7]). In the interaction analysis, a model of the SCN9A and COMT variants showed a CVC of 10/10; however, the TA was 0.4141.

CONCLUSION

COMT and SCN9A variants are not associated with CLBP in the analyzed Mexican-Mestizo population.

Synergistic effects of BTN3A1, SHP2, CD274, and STAT3 gene polymorphisms on the risk of systemic lupus erythematosus: a multifactorial dimensional reduction analysis

OBJECTIVE

Systemic lupus erythematosus is a complex autoimmune disorder, and evidence supports the significance of genetic polymorphisms in SLE genetic susceptibility. The aim of this study was to assess the effects of BTN3A1 (butyrophilin 3A1), SHP2 (Src homology-2 containing protein tyrosine phosphatase), CD274 (programmed cell death 1 ligand 1), and STAT3 (signal transducer-activator of transcription 3) gene interactions on SLE risk.

MATERIALS AND METHODS

Two hundred and ninety patients diagnosed with SLE and 370 healthy controls were recruited. A multifactor dimensionality reduction (MDR) approach was used to determine the epistasis among single nucleotide polymorphisms (SNPs) on the BTN3A1 (rs742090), SHP2 (rs58116261), CD174 (rs702275), and STAT3 (rs8078731) genes. The best risk prediction model was identified in terms of precision and cross-validation consistency.

RESULTS

Allele A and genotype AA were negatively related to genetic susceptibility of SLE for BTN3A1 rs742090 (OR = 0.788 (0.625-0.993), P = 0.044; OR = 0.604 (0.372-0.981), P = 0.040). For STAT3 rs8078731, allele A and genotype AA were positively related to the risk of SLE (OR = 1.307 (1.032-1.654), P = 0.026; OR = 1.752 (1.020-3.010), P = 0.041). MDR analysis revealed the most significant interaction between BTN3A1 rs742090 and SHP2 rs58116261. The best risk prediction model was a combination of BTN3A1 rs742090, SHP2 rs58116261, and STAT3 rs8078731 (accuracy = 0.5866, consistency = 10/10, OR = 1.9870 (1.5964-2.4731), P = 0.001).

CONCLUSION

These data indicate that risk prediction models formed by gene interactions (BTN3A1, SHP2, STAT3) can identify susceptible populations of SLE. Key Points • BTN3A1 rs742090 polymorphism was a protective factor for systemic lupus erythematosus, while STAT3 rs8078731 polymorphism was a risk factor. • There was a strong synergistic effect of BTN3A1 rs742090 and SHP2 rs58116261, and interaction among BTN3A1 rs742090, SHP2 rs58116261, and STAT3 rs8078731 constructed the best model to show association with SLE risk.

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Prostate cancer (PCa) is a major public health problem worldwide. Recent studies have suggested that ghrelin and its receptor could be involved in the susceptibility to several cancers such as PCa, leading to their use as an important predictive way for the clinical progression and prognosis of cancer. However, conflicting results of single nucleotide polymorphisms (SNPs) with ghrelin (GHRL) and its receptor (GHSR) genes were demonstrated in different studies. Thus, the present case-control study was undertaken to investigate the association of GHRL and GHSR polymorphisms with the susceptibility to sporadic PCa. A cohort of 120 PCa patients and 95 healthy subjects were enrolled in this study. Genotyping of six SNPs was performed: three tag SNPs in GHRL (rs696217, rs4684677, rs3491141) and three tag SNPs in the GHSR (rs2922126, rs572169, rs2948694) using TaqMan. The allele and genotype distribution, as well as haplotypes frequencies and linked disequilibrium (LD), were established. Multifactor dimensionality reduction (MDR) analysis was used to study gene-gene interactions between the six SNPs. Our results showed no significant association of the target polymorphisms with PCa (p > 0.05). Nevertheless, SNPs are often just markers that help identify or delimit specific genomic regions that may harbour functional variants rather than the variants causing the disease. Furthermore, we found that one GHSR rs2922126, namely the TT genotype, was significantly more frequent in PCa patients than in controls (p = 0.040). These data suggest that this genotype could be a PCa susceptibility genotype. MDR analyses revealed that the rs2922126 and rs572169 combination was the best model, with 81.08% accuracy (p = 0.0001) for predicting susceptibility to PCa. The results also showed a precision of 98.1% (p < 0.0001) and a PR-AUC of 1.00. Our findings provide new insights into the influence of GHRL and GHSR polymorphisms and significant evidence for gene-gene interactions in PCa susceptibility, and they may guide clinical decision-making to prevent overtreatment and enhance patients' quality of life.

Glutamate receptor genetic variants affected peripheral glutamatergic transmission and treatment induced improvement of Indian ADHD probands

Attention deficit hyperactivity disorder (ADHD), a childhood-onset neurobehavioral disorder, often perturbs scholastic achievement and peer-relationship. The pivotal role of glutamate (Glu) in learning and memory indicated an influence of Glu in ADHD, leading to the exploration of Glu in different brain regions of ADHD subjects. We for the first time analyzed GluR genetic variations, Glu levels, as well as expression of Glu receptors (GluR) in the peripheral blood of eastern Indian ADHD probands to find out the relevance of Glu in ADHD prognosis. After obtaining informed written consent for participation, peripheral blood was collected for analyzing the genetic variants, Glu level, and expression of target genes. Since ADHD probands are often treated with methylphenidate or atomoxetine for providing symptomatic remediation, we have also tested post-therapeutic improvement in the ADHD trait scores in the presence of different GluR genotypes. Two variants, GRM7 rs3749380 "T" and GRIA1 rs2195450 "C", exhibited associations with ADHD (P ≤ 0.05). A few GluR genetic variants showed significant association with higher trait severity, low IQ, lower plasma Glu level, down-regulated GluR mRNA expression, and poor response to medications. This indicates that down-regulated glutamatergic system may have an effect on ADHD etiology and treatment efficacy warranting further in-depth investigation.

Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis

The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary "bridge" between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.

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 p_perm 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) (β_additive = 0.202/p_perm = 0.015), KDM3B (rs757647) (β_recessive = 0.323/p_perm = 0.005), INHBA (rs1079866) (β_additive = 0.110/p_perm = 0.014) and NKX2-1 (rs999460) (β_recessive = -0.176/p_perm = 0.015). Ten BW-significant models of interSNPs interactions (p_perm ≤ 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.

A potential relationship between MMP-9 rs2250889 and ischemic stroke susceptibility

Purpose

Ischemic stroke (IS), a serious cerebrovascular disease, greatly affects people's health and life. Genetic factors are indispensable for the occurrence of IS. As a biomarker for IS, the MMP-9 gene is widely involved in the pathophysiological process of IS. This study attempts to find out the relationship between MMP-9 polymorphisms and IS susceptibility.

Methods

A total of 700 IS patients and 700 healthy controls were recruited. The single nucleotide polymorphism (SNP) markers of the MMP-9 gene were genotyped by the MassARRAY analyzer. Multifactor dimensionality reduction (MDR) was applied to generate SNP-SNP interaction. Furthermore, the relationship between genetic variations (allele and genotype) of the MMP-9 gene and IS susceptibility was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CIs).

Results

Our results demonstrated that rs2250889 could significantly increase the susceptibility to IS in the codominant, dominant, overdominant, and log-additive models (p < 0.05). Further stratification analysis showed that compared with the control group, rs2250889 was associated with IS risk in different case groups (age, female, smoking, and non-drinking) (p < 0.05). Based on MDR analysis, rs2250889 was the best model for predicting IS risk (cross-validation consistency: 10/10, OR = 1.56 (1.26-1.94), p < 0.001).

Conclusion

Our study preliminarily confirmed that SNP rs2250889 was significantly associated with susceptibility to IS.

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction

BACKGROUND

Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare organizations. This research aims to identify the interaction between Ultrasound scanners (US) maintenance variables as a function of maintenance indicators: US in service or decommissioned, excessive number of failures, and failure rate. Knowing those interactions, specific maintenance measures will be developed to improve the reliability of the US.

RESEARCH DESIGN AND METHODS

Multifactor Dimensionality Reduction (MDR) method was eployed to analyze data from 222 US and their four-year maintenance history. Models were developed based on the variables with the greatest influence on maintenance indicators, where US were classified according to the associated risk.

RESULTS

US with more than one major failure or at least one major component replacement had up to 496.4% more failures than the average. Failure rate increased by up to 188.7% over the average for those US with more than three moderate failures, three replacements, or both.

CONCLUSIONS

This study identifies and quantifies the causes of risk to establish a specific maintenance plan for US. It helps to better understand the degradation of US to optimize their operation and maintenance.

Post-treatment symptomatic improvement of the eastern Indian ADHD probands is influenced by CYP2D6 genetic variations

OBJECTIVES

Symptomatic remediation from attention deficit hyperactivity disorder (ADHD)-associated traits is achieved by treatment with methylphenidate (MPH)/atomoxetine (ATX). We have analyzed the association of functional CYP2D6 variations, rs1065852, rs3892097, rs1135840, and rs1058164, with ADHD in the Indian subjects.

METHODS

Subjects were recruited following the Diagnostic and Statistical Manual for Mental Disorders. Trait scores were obtained from the Conner's Parents Rating Scale-Revised. After obtaining informed consent, blood was collected for DNA isolation, and genotyping was performed by PCR or TaqMan-based methods. Probands were treated with MPH or ATX based on age, symptoms, and drug availability. Treatment outcome was assessed using a structured questionnaire. Data obtained was analyzed to identify the association of CYP2D6 variations and the SLC6A3 rs28363170 with the treatment outcome.

RESULTS

The frequency of rs1135840 "G" and rs1065852 "G" was higher in the male ADHD probands. Bias in parental transmission (p=0.007) and association with higher trait scores were observed for rs1065852 "A". Independent influence of rs1065852 on ADHD was also observed. Probands carrying rs1065852 'GG', rs1135840 'CG', and rs28363170 10R exhibited significant symptomatic improvement with MPH, while probands with rs1135840 'CC' and rs28363170 9R showed improvement after ATX treatment.

CONCLUSIONS

ADHD probands having specific CYP2D6 genetic variations respond differentially to pharmaceutical intervention.

Epigenetic Evaluation of the TBX20 Gene and Environmental Risk Factors in Mexican Paediatric Patients with Congenital Septal Defects

The TBX20 gene has a key role during cardiogenesis, and it has been related to epigenetic mechanisms in congenital heart disease (CHD). The purpose of this study was to assess the association between DNA methylation status and congenital septal defects. The DNA methylation of seven CpG sites in the TBX20 gene promoter was analyzed through pyrosequencing as a quantitative method in 48 patients with congenital septal defects and 104 individuals with patent ductus arteriosus (PDA). The average methylation was higher in patients than in PDA (p < 0.001). High methylation levels were associated with a higher risk of congenital septal defects (OR = 4.59, 95% CI = 1.57-13.44, p = 0.005). The ROC curve analysis indicated that methylation of the TBX20 gene could be considered a risk marker for congenital septal defects (AUC = 0.682; 95% CI = 0.58-0.77; p < 0.001). The analysis of environmental risk factors in patients with septal defects and PDA showed an association between the consumption of vitamins (OR = 0.10; 95% CI = 0.01-0.98; p = 0.048) and maternal infections (OR = 3.10; 95% CI = 1.26-7.60; p = 0.013). These results suggest that differences in DNA methylation of the TBX20 gene can be associated with septal defects.

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

Dimensionality reduction approach for many-objective epistasis analysis

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Gene variants previously associated with reduced soft-tissue injury risk: Part 2 - Polygenic associations with elite status in Rugby

Part 1 of this genetic association series highlighted several genetic variants independently associated with elite status in rugby. However, it is highly likely that the genetic influence on elite status is polygenic due to the interaction of multiple genes. Therefore, the aim of the present study was to investigate whether polygenic profiles of elite rugby athletes differed from non-athletes utilising 13 genetic polymorphisms previously associated with tendon/ligament injury. Total genotype score (TGS) was calculated and multifactor dimensionality reduction (MDR) was used to calculate SNP-SNP epistasis interactions. Based on our elite rugby data from Part 1, mean TGS was significantly higher in elite rugby athletes (52.1 ± 10.7) than non-athletes (48.7 ± 10.8). There were more elite rugby athletes (54%) within the upper TGS quartile, and fewer (46%) within the lower quartile, compared to non-athletes (31% and 69%, respectively; P = 5·10^-5), and the TGS was able to distinguish between elite rugby athletes and non-athletes (area under the curve = 0.59; 95% confidence interval 0.55-0.63; P = 9·10^-7). Furthermore, MDR identified a three-SNP model of COL5A1 rs12722, COL5A1 rs3196378 and MIR608 rs4919510 that was best able to predict elite athlete status, with a greater frequency of the CC-CC-CC genotype combination in elite rugby athletes (9.8%) than non-athletes (5.3%). We propose that elite rugby athletes possess "preferable" musculoskeletal soft-tissue injury-associated polygenic profiles that have helped them achieve success in the high injury risk environment of rugby. These data may, in future, have implications for the individual management of musculoskeletal soft-tissue injury.Highlights Elite rugby athletes have preferable polygenic profiles to non-athletes in terms of genetic variants previously associated with musculoskeletal soft-tissue injury.The total genotype score was able to distinguish between elite rugby athletes and non-athletes.COL5A1 rs12722, COL5A1 rs3196378 and MIR608 rs4919510 produced the best model for predicting elite athlete status.We propose that elite rugby athletes may have an inherited advantage to achieving elite status due to an increased resistance to soft-tissue injury.

Common genetic variants associated with melanoma risk or naevus count in patients with wildtype MC1R melanoma

BACKGROUND

Hypomorphic MC1R variants are the most prevalent genetic determinants of melanoma risk in the white population. However, the genetic background of patients with wildtype (WT) MC1R melanoma is poorly studied.

OBJECTIVES

To analyse the role of candidate common genetic variants on the melanoma risk and naevus count in Spanish patients with WT MC1R melanoma.

METHODS

We examined 753 individuals with WT MC1R from Spain (497 patients and 256 controls). We used OpenArray reverse-transcriptase polymerase chain reaction to genotype a panel of 221 common genetic variants involved in melanoma, naevogenesis, hormonal pathways and proinflammatory pathways. Genetic models were tested using multivariate logistic regression models. Nonparametric multifactor dimensionality reduction (MDR) was used to detect gene-gene interactions within each biological subgroup of variants.

RESULTS

We found that variant rs12913832 in the HERC2 gene, which is associated with blue eye colour, increased melanoma risk in individuals with WT MC1R [odds ratio (OR) 1·97, 95% confidence interval (CI) 1·48-2·63; adjusted P < 0·001; corrected P < 0·001]. We also observed a trend between the rs3798577 variant in the oestrogen receptor alpha gene (ESR1) and a lower naevus count, which was restricted to female patients with WT MC1R (OR 0·51, 95% CI 0·33-0·79; adjusted P = 0·002; corrected P = 0·11). This sex-dependent association was statistically significant in a larger cohort of patients with melanoma regardless of their MC1R status (n = 1497; OR 0·71, 95% CI 0·57-0·88; adjusted P = 0·002), reinforcing the hypothesis of an association between hormonal pathways and susceptibility to melanocytic proliferation. Last, the MDR analysis revealed four genetic combinations associated with melanoma risk or naevus count in patients with WT MC1R.

CONCLUSIONS

Our data suggest that epistatic interaction among common variants related to melanocyte biology or proinflammatory pathways might influence melanocytic proliferation in individuals with WT MC1R. What is already known about this topic? Genetic variants in the MC1R gene are the most prevalent melanoma genetic risk factor in the white population. Still, 20-40% of cases of melanoma occur in individuals with wildtype MC1R. Multiple genetic variants have a pleiotropic effect in melanoma and naevogenesis. Additional variants in unexplored pathways might also have a role in melanocytic proliferation in these patients. Epidemiological evidence suggests an association of melanocytic proliferation with hormonal pathways and proinflammatory pathways. What does this study add? Variant rs12913832 in the HERC2 gene, which is associated with blue eye colour, increases the melanoma risk in individuals with wildtype MC1R. Variant rs3798577 in the oestrogen receptor gene is associated with naevus count regardless of the MC1R status in female patients with melanoma. We report epistatic interactions among common genetic variants with a role in modulating the risk of melanoma or the number of naevi in individuals with wildtype MC1R. What is the translational message? We report a potential role of hormonal signalling pathways in melanocytic proliferation, providing a basis for better understanding of sex-based differences observed at the epidemiological level. We show that gene-gene interactions among common genetic variants might be responsible for an increased risk for melanoma development in individuals with a low-risk phenotype, such as darkly pigmented hair and skin.

The Enigmatic Etiology of Oculo-Auriculo-Vertebral Spectrum (OAVS): An Exploratory Gene Variant Interaction Approach in Candidate Genes

The clinical diagnosis of oculo-auriculo-vertebral spectrum (OAVS) is established when microtia is present in association with hemifacial hypoplasia (HH) and/or ocular, vertebral, and/or renal malformations. Genetic and non-genetic factors have been associated with microtia/OAVS. Although the etiology remains unknown in most patients, some cases may have an autosomal dominant, autosomal recessive, or multifactorial inheritance. Among the possible genetic factors, gene−gene interactions may play important roles in the etiology of complex diseases, but the literature lacks related reports in OAVS patients. Therefore, we performed a gene−variant interaction analysis within five microtia/OAVS candidate genes (HOXA2, TCOF1, SALL1, EYA1 and TBX1) in 49 unrelated OAVS Mexican patients (25 familial and 24 sporadic cases). A statistically significant intergenic interaction (p-value < 0.001) was identified between variants p.(Pro1099Arg) TCOF1 (rs1136103) and p.(Leu858=) SALL1 (rs1965024). This intergenic interaction may suggest that the products of these genes could participate in pathways related to craniofacial alterations, such as the retinoic acid (RA) pathway. The absence of clearly pathogenic variants in any of the analyzed genes does not support a monogenic etiology for microtia/OAVS involving these genes in our patients. Our findings could suggest that in addition to high-throughput genomic approaches, future gene−gene interaction analyses could contribute to improving our understanding of the etiology of microtia/OAVS.

Synergistic influence of cytokine gene polymorphisms over the risk of dementia: A multifactor dimensionality reduction analysis

OBJECTIVE

Evidence supports the important role of neuroinflammation in some types of dementia. This study aimed to evaluate the effect of epistasis of gene cytokines such as interleukin (IL)-α, IL-6, tumor necrosis factor (TNFα), and interferon-gamma (IFN-γ) on the susceptibility to the development of dementia.

MATERIALS AND METHODS

In the study, 221 patients diagnosed with dementia and 710 controls were included. The multifactor-dimensionality reduction (MDR) analysis was performed to identify the epistasis between SNP located in genes of IL-α (rs1800587), IL-6 (rs1800796), TNFα (rs361525 and rs1800629), and IFNγ (rs2069705). The best risk prediction model was identified based on precision and cross-validation consistency.

RESULTS

Multifactor-dimensionality reduction analysis detected a significant model with the genes TNFα, IFNγ, IL1α, and IL6 (prediction success: 72%, p < 0.0001). When risk factors were analyzed with these polymorphisms, the model achieved a similar prediction for dementia as the genes-only model.

CONCLUSION

These data indicate that gene-gene interactions form significant models to identify populations susceptible to dementia.

Specific dopaminergic genetic variants influence impulsivity, cognitive deficit, and disease severity of Indian ADHD probands

BACKGROUND

Impulsivity (Imp), being one of the cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD), often leads to inappropriate responses to stimuli. Since the dopaminergic system is the primary target for pharmaceutical intervention in ADHD, we investigated the association between ADHD-related Imp and functional gene variants of the dopamine transporter (SLC6A3) and catechol-O-methyltransferase involved in dopamine clearance.

METHODS AND RESULTS

Indo-Caucasoid families with ADHD probands (N = 217) were recruited based on the Diagnostic and Statistical Manual of Mental Disorders (DSM). Imp of the probands was assessed using the Domain Specific Imp Scale for Children and DSM. Peripheral blood was collected after obtaining informed written consent for participation, genomic DNA was isolated, and target sites were genotyped by DNA sequencing. The association of genetic variants with Imp was examined by the Quantitative trait analysis (QTA) and Analysis of variance (ANOVA). Post-Hoc analysis following QTA and ANOVA showed significant associations of rs2254408, rs2981359, and rs2239393 with different domains of Imp (P < 0.05). Various haplotypic combinations also showed statistically significant associations with Imp (P < 0.05). Multifactor dimensionality reduction models revealed strong effects of the variants on Imp. ADHD probands harboring the risk alleles exhibited a deficit in performance during cognitive assessment. Longitudinal follow-up revealed a significant association of rs2254408 with trait persistence.

CONCLUSION

The present study indicates the influence of the studied genetic variants on ADHD-associated imp, executive deficit, and disease persistence. Thus, these variants may be helpful as predictors for the success of individual therapeutic sessions during cognitive training.

ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors

With the development of high-throughput genotyping technology, single nucleotide polymorphism (SNP)-SNP interactions (SSIs) detection has become an essential way for understanding disease susceptibility. Various methods have been proposed to detect SSIs. However, given the disease complexity and bias of individual SSI detectors, these single-detector-based methods are generally unscalable for real genome-wide data and with unfavorable results. We propose a novel ensemble learning-based approach (ELSSI) that can significantly reduce the bias of individual detectors and their computational load. ELSSI randomly divides SNPs into different subsets and evaluates them by multi-type detectors in parallel. Particularly, ELSSI introduces a four-stage pipeline (generate, score, switch and filter) to iteratively generate new SNP combination subsets from SNP subsets, score the combination subset by individual detectors, switch high-score combinations to other detectors for re-scoring, then filter out combinations with low scores. This pipeline makes ELSSI able to detect high-order SSIs from large genome-wide datasets. Experimental results on various simulated and real genome-wide datasets show the superior efficacy of ELSSI to state-of-the-art methods in detecting SSIs, especially for high-order ones. ELSSI is applicable with moderate PCs on the Internet and flexible to assemble new detectors. The code of ELSSI is available at https://www.sdu-idea.cn/codes.php?name=ELSSI.

CASP1 Gene Polymorphisms and BAT1-NFKBIL-LTA-CASP1 Gene-Gene Interactions Are Associated with Restenosis after Coronary Stenting

In the present study, we evaluated the association of the BAT1, NFKBIL, LTA, and CASP1 single nucleotide polymorphisms and the gene−gene interactions with risk of developing restenosis after coronary stenting. The allele and genotype determination of the polymorphisms (BAT1 rs2239527 C/G, NFKBIL1 rs2071592 T/A, LTA rs1800683 G/A, CASP1 rs501192 A/G, and CASP1 rs580253 A/G) were performed by 5’exonuclease TaqMan assays in 219 patients: 66 patients with restenosis and 153 without restenosis. The distribution of rs2239527 C/G, rs2071592 T/A, and rs1800683 G/A polymorphisms was similar in patients with and without restenosis. Nonetheless, under recessive (OR = 2.73, pCRes = 0.031) and additive models (OR = 1.65, pCAdd = 0.039), the AA genotype of the rs501192 A/G polymorphism increased the restenosis risk. Under co-dominant, dominant, recessive, and additive models, the AA genotype of the rs580253 A/G was associated with a high restenosis risk (OR = 5.38, pCCo-Dom = 0.003; OR = 2.12, pCDom = 0.031; OR = 4.32, pCRes = 0.001; and OR = 2.16, 95%CI: 1.33−3.52, pCAdd = 0.001, respectively). In addition, we identified an interaction associated with restenosis susceptibility: BAT1-NFKBIL1-LTA-CASP1 (OR = 9.92, p < 0.001). In summary, our findings demonstrate that the rs501192 A/G and rs580253 A/G polymorphisms, as well as the gene−gene interactions between BAT1-NFKBIL1-LTA-CASP1, are associated with an increased restenosis risk after coronary stenting.

Biomarker interaction selection and disease detection based on multivariate gain ratio

Background

Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It’s the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction.

Results

We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93.13\%$$\end{document}93.13% than I-score of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.73\%$$\end{document}91.73% in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.80\%$$\end{document}89.80% based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.32\%$$\end{document}97.32% compared to I-score with accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.11\%$$\end{document}89.11%. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset.

Conclusions

MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.

Concussion-Associated Polygenic Profiles of Elite Male Rugby Athletes

Due to the high-velocity collision-based nature of elite rugby league and union, the risk of sustaining a concussion is high. Occurrence of and outcomes following a concussion are probably affected by the interaction of multiple genes in a polygenic manner. This study investigated whether suspected concussion-associated polygenic profiles of elite rugby athletes differed from non-athletes and between rugby union forwards and backs. We hypothesised that a total genotype score (TGS) using eight concussion-associated polymorphisms would be higher in elite rugby athletes than non-athletes, indicating selection for protection against incurring or suffering prolonged effects of, concussion in the relatively high-risk environment of competitive rugby. In addition, multifactor dimensionality reduction was used to identify genetic interactions. Contrary to our hypothesis, TGS did not differ between elite rugby athletes and non-athletes (p ≥ 0.065), nor between rugby union forwards and backs (p = 0.668). Accordingly, the TGS could not discriminate between elite rugby athletes and non-athletes (AUC ~0.5), suggesting that, for the eight polymorphisms investigated, elite rugby athletes do not have a more ‘preferable’ concussion-associated polygenic profile than non-athletes. However, the COMT (rs4680) and MAPT (rs10445337) GC allele combination was more common in rugby athletes (31.7%; p < 0.001) and rugby union athletes (31.8%; p < 0.001) than non-athletes (24.5%). Our results thus suggest a genetic interaction between COMT (rs4680) and MAPT (rs10445337) assists rugby athletes in achieving elite status. These findings need exploration vis-à-vis sport-related concussion injury data and could have implications for the management of inter-individual differences in concussion risk.

A pharmacogenetic interaction analysis of bevacizumab with paclitaxel in advanced breast cancer patients

To investigate pharmacogenetic interactions among VEGF-A, VEGFR-2, IL-8, HIF-1α, EPAS-1, and TSP-1 SNPs and their role on progression-free survival (PFS) in metastatic breast cancer (MBC) patients treated with bevacizumab plus first-line paclitaxel or with paclitaxel alone. Analyses were performed on germline DNA, and SNPs were investigated by real-time PCR technique. The multifactor dimensionality reduction (MDR) methodology was applied to investigate the interaction between SNPs. The present study was an explorative, ambidirectional cohort study: 307 patients from 11 Oncology Units were evaluated retrospectively from 2009 to 2016, then followed prospectively (NCT01935102). Two hundred and fifteen patients were treated with paclitaxel and bevacizumab, whereas 92 patients with paclitaxel alone. In the bevacizumab plus paclitaxel group, the MDR software provided two pharmacogenetic interaction profiles consisting of the combination between specific VEGF-A rs833061 and VEGFR-2 rs1870377 genotypes. Median PFS for favorable genetic profile was 16.8 vs. the 10.6 months of unfavorable genetic profile (p = 0.0011). Cox proportional hazards model showed an adjusted hazard ratio of 0.64 (95% CI, 0.5–0.9; p = 0.004). Median OS for the favorable genetic profile was 39.6 vs. 28 months of unfavorable genetic profile (p = 0.0103). Cox proportional hazards model revealed an adjusted hazard ratio of 0.71 (95% CI, 0.5–1.01; p = 0.058). In the 92 patients treated with paclitaxel alone, the results showed no effect of the favorable genetic profile, as compared to the unfavorable genetic profile, either on the PFS (p = 0.509) and on the OS (p = 0.732). The pharmacogenetic statistical interaction between VEGF-A rs833061 and VEGFR-2 rs1870377 genotypes may identify a population of bevacizumab-treated patients with a better PFS.

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

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

Polymorphisms in DNA Repair and Xenobiotic Biotransformation Enzyme Genes and Lung Cancer Risk in Coal Mine Workers

Background: Currently coal mining employs over 7 million miners globally. This occupational setting is associated with exposure to dust particles, heavy metals, polycyclic aromatic hydrocarbons and radioactive radon, significantly increasing the risk of lung cancer (LC). The susceptibility for LC is modified by genetic variations in xenobiotic detoxification and DNA repair capacity. The aim of this study was to investigate the association between GSTM1 (deletion), APEX1 (rs1130409), XPD (rs13181) and NBS1 (rs1805794) gene polymorphisms and LC risk in patients who worked in coal mines. Methods: The study included 639 residents of the coal region of Western Siberia (Kemerovo region, Russia): 395 underground miners and 244 healthy men who do not work in industrial enterprises. Genotyping was performed using real-time and allele-specific PCR. Results: The results show that polymorphisms of APEX1 (recessive model: OR_adj = 1.87; CI 95%: 1.01–3.48) and XPD (log additive model: OR_adj = 2.25; CI 95%: 1.59–3.19) genes were associated with increased LC risk. GSTM1 large deletion l was linked with decreased risk of LC formation (OR_adj = 0.59, CI 95%: 0.36–0.98). The multifactor dimensionality reduction method for 3-loci model of gene–gene interactions showed that the GSTM1 (large deletion)—APEX1 (rs1130409)—XPD (rs13181) model was related with a risk of LC development. Conclusions: The results of this study highlight an association between gene polymorphism combinations and LC risks in coal mine workers.

Comprehensive Statistical and Bioinformatics Analysis in the Deciphering of Putative Mechanisms by Which Lipid-Associated GWAS Loci Contribute to Coronary Artery Disease

The study was designed to evaluate putative mechanisms by which lipid-associated loci identified by genome-wide association studies (GWAS) are involved in the molecular pathogenesis of coronary artery disease (CAD) using a comprehensive statistical and bioinformatics analysis. A total of 1700 unrelated individuals of Slavic origin from the Central Russia, including 991 CAD patients and 709 healthy controls were examined. Sixteen lipid-associated GWAS loci were selected from European studies and genotyped using the MassArray-4 system. The polymorphisms were associated with plasma lipids such as total cholesterol (rs12328675, rs4846914, rs55730499, and rs838880), LDL-cholesterol (rs3764261, rs55730499, rs1689800, and rs838880), HDL-cholesterol (rs3764261) as well as carotid intima-media thickness/CIMT (rs12328675, rs11220463, and rs1689800). Polymorphisms such as rs4420638 of APOC1 (p = 0.009), rs55730499 of LPA (p = 0.0007), rs3136441 of F2 (p < 0.0001), and rs6065906 of PLTP (p = 0.002) showed significant associations with the risk of CAD, regardless of sex, age, and body mass index. A majority of the observed associations were successfully replicated in large independent cohorts. Bioinformatics analysis allowed establishing (1) phenotype-specific and shared epistatic gene–gene and gene–smoking interactions contributing to all studied cardiovascular phenotypes; (2) lipid-associated GWAS loci might be allele-specific binding sites for transcription factors from gene regulatory networks controlling multifaceted molecular mechanisms of atherosclerosis.

Whole exome sequencing identifies the potential role of genes involved in p53 pathway in Nasopharyngeal Carcinoma from Northeast India

Nasopharyngeal Carcinoma (NPC) found to be dependent on geographical and racial variation and is more prevalent in Northeast (NE) India. WES-based study was conducted in three states (tribes); Nagaland (Naga), Mizoram (Mizo) and Manipur (Manipuri), which provided an overview of germline variants involved inthemajor signaling pathways. Validation and recurrence assessment of WES data confirmed the risk effect of STEAP3_rs138941861 and JAG1_rs2273059, and the protective role of PARP4_rs17080653 and TGFBR1_rs11568778 variants, where STEAP3_rs138941861conferring Arg290His substitution was the only exonic non-synonymous variant and to be located in proximity to the linking region between the transmembrane and oxidoreductasedomainsof STEAP3 protein, andaffectedits structural and functional dynamics by altering the Electrostatic Potential around this connecting region. Moreover, these significantly associated variants having deleterious effect were observed to have interactions in p53 signaling pathway which emphasizes the importance of this pathway in the causation of NPC.

What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics

Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.

Association Analysis Between the Functional Single Nucleotide Variants in miR-146a, miR-196a-2, miR-499a, and miR-612 With Acute Lymphoblastic Leukemia

Background

Acute lymphoblastic leukemia (ALL) is characterized by an abnormal proliferation of immature lymphocytes, in whose development involves both environmental and genetic factors. It is well known that single nucleotide polymorphisms (SNPs) in coding and noncoding genes contribute to the susceptibility to ALL. This study aims to determine whether SNPs in miR-146a, miR-196a-2, miR-499a, and miR-612 genes are associated with the risk to ALL in pediatric Mexican population.

Methods

A multicenter case-control study was carried out including patients with de novo diagnosis of ALL and healthy subjects as control group. The DNA samples were obtained from saliva and peripheral blood, and the genotyping of rs2910164, rs12803915, rs11614913, and rs3746444 was performed using the 5′exonuclease technique. Gene-gene interaction was evaluated by the multifactor dimensionality reduction (MDR) software.

Results

miR-499a rs3746444 showed significant differences among cases and controls. The rs3746444G allele was found as a risk factor to ALL (OR, 1.6 [95% CI, 1.05–2.5]; p = 0.028). The homozygous GG genotype of rs3746444 confers higher risk to ALL than the AA genotype (OR, 5.3 [95% CI, 1.23–23.4]; p = 0.01). Moreover, GG genotype highly increases the risk to ALL in male group (OR, 17.6 [95% CI, 1.04–298.9]; p = 0.00393). In addition, an association in a gender-dependent manner among SNPs located in miR-146a and miR-196a-2 genes and ALL susceptibility was found.

Conclusion

Our findings suggest that SNP located in miR-499a, miR-146a, and miR-196a-2 genes confer risk to ALL in Mexican children. Experimental analysis to decipher the role of these SNPs in human hematopoiesis could improve our understanding of the molecular mechanism underlying the development of ALL.

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.

An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been some computational methods to study tumor heterogeneity from the level of genome, transcriptome, and histology, but these methods still have certain limitations. In this study, we proposed an epistasis and heterogeneity analysis method based on genomic single nucleotide polymorphism (SNP) data. First of all, a maximum correlation and maximum consistence criteria was designed based on Bayesian network score K2 and information entropy for evaluating genomic epistasis. As the number of SNPs increases, the epistasis combination space increases sharply, resulting in a combination explosion phenomenon. Therefore, we next use an improved genetic algorithm to search the SNP epistatic combination space for identifying potential feasible epistasis solutions. Multiple epistasis solutions represent different pathogenic gene combinations, which may lead to different tumor subtypes, that is, heterogeneity. Finally, the XGBoost classifier is trained with feature SNPs selected that constitute multiple sets of epistatic solutions to verify that considering tumor heterogeneity is beneficial to improve the accuracy of tumor subtype prediction. In order to demonstrate the effectiveness of our method, the power of multiple epistatic recognition and the accuracy of tumor subtype classification measures are evaluated. Extensive simulation results show that our method has better power and prediction accuracy than previous methods.

Genome-wide association studies reveal the coordinated regulatory networks underlying photosynthesis and wood formation in Populus

Photosynthesis and wood formation underlie the ability of trees to provide renewable resources and perform ecological functions; however, the genetic basis and regulatory pathways coordinating these two linked processes remain unclear. Here, we used a systems genetics strategy, integrating genome-wide association studies, transcriptomic analyses, and transgenic experiments, to investigate the genetic architecture of photosynthesis and wood properties among 435 unrelated individuals of Populus tomentosa, and unravel the coordinated regulatory networks resulting in two trait categories. We detected 222 significant single-nucleotide polymorphisms, annotated to 177 candidate genes, for 10 traits of photosynthesis and wood properties. Epistasis uncovered 74 epistatic interactions for phenotypes. Strikingly, we deciphered the coordinated regulation patterns of pleiotropic genes underlying phenotypic variations for two trait categories. Furthermore, expression quantitative trait nucleotide mapping and coexpression analysis were integrated to unravel the potential transcriptional regulatory networks of candidate genes coordinating photosynthesis and wood properties. Finally, heterologous expression of two pleiotropic genes, PtoMYB62 and PtoMYB80, in Arabidopsis thaliana demonstrated that they control regulatory networks balancing photosynthesis and stem secondary cell wall components, respectively. Our study provides insights into the regulatory mechanisms coordinating photosynthesis and wood formation in poplar, and should facilitate genetic breeding in trees via molecular design.

Predictive value of ERCC2, ABCC2 and MMP2 of response and long-term survival in locally advanced head and neck cancer patients treated with chemoradiotherapy

PURPOSE

Genetic variants in genes involved in the distribution, metabolism, accumulation or repair of lesions are likely to influence the response of drugs used in the treatment of Head and Neck Cancer (HNC). We examine the effect of 36 SNPs on clinical outcomes in patients with locally advanced HNC who were receiving platinum-based chemoradiotherapy (CRT).

METHODS

These SNPs were genotyped in 110 patients using the iPLEX Gold assay on the MassARRAY method in blood DNA samples and used Kaplan-Meier and Cox regression analyses to compare genotype groups with the survival.

RESULTS

Two SNPs, rs717620 (ABCC2) and rs12934241 (MMP2) were strongly associated with overall survival (OS) and disease-free survival (DFS). At a median follow-up of 64.4 months, the allele A of rs717620 (ABCC2) had an increased risk of disease progression {hazard ratio [HR] = 1.79, p = 0.0018} and death (HR = 2.0, p = 0.00027). ABCC2 was associated with OS after a Bonferroni adjustment for multiple testing. The MMP2 rs12934241-T allele was associated with an increased risk of worse OS and DFS (p = 0.0098 and p = 0.0015, respectively). One SNP of ABCB1 and three SNPs located in the ERCC2 gene showed an association with response in the subgroup of HNC patients treated with definitive CRT.

CONCLUSIONS

Our findings highlight the potential usefulness of SNPs in different genes involved in drug metabolism and repair DNA to predict the response and survival to CRT. ABCC2 is a potential predictor of OS in patients with HNC.

Polymorphism of Proteasomal Genes Can Be a Risk Factor for Systemic Autoimmune Diseases in Children

The study aimed to assess the involvement of three proteasomal genes, PSMA6 , PSMC6 , and PSMA3 , in autoimmune pathogenesis by analyzing associations between single nucleotide polymorphisms and systemic rheumatic diseases with a different autoimmune component: juvenile idiopathic arthritis (JIA), the juvenile form of systemic lupus erythematosus, and Kawasaki's disease (KD). Our results showed that the PSMA6 (rs1048990) polymorphism can be a risk factor for JIA (false discovery rate q ≤ 0.090), while PSMA3 (rs2348071) has a tendency to be nonspecific and is shared with JIA and other autoimmune diseases, including KD, an illness with very low autoimmune activity and high autoinflammation.

Adhesion G protein-coupled receptor L3 gene variants: Statistically significant association observed in the male Indo-caucasoid Attention deficit hyperactivity disorder probands

Primary symptoms of Attention Deficit Hyperactivity Disorder (ADHD) are age inappropriate inattention, hyperactivity and impulsivity. Caucasoid individuals showed increased susceptibility to ADHD and disruptive behaviour in presence of Adhesion G-protein-coupled receptor L3 (ADGRL3) gene variants. We investigated ADGRL3 rs1868790, rs6551665, rs2345039 in Indo-Caucasoid families with ADHD probands (N = 249) and controls (N = 350). Behavioural traits, executive function, and IQ of probands were measured through Conner's Parent Rating Scale-Revised, Parental Account of Children's Symptoms, Barkley Deficit in Executive Functioning-Child & Adolescent Scale, and Wechsler Intelligence Scale for Children-III respectively. After obtaining informed written consent, peripheral blood was collected for genomic DNA isolation and target sites were analyzed by PCR based methods or TaqMan assay. Case-control analysis showed higher frequency of rs2345039 'C' allele, 'CC' genotype and A-A-C haplotype in the ADHD probands, principally due to higher occurrence of the 'C' allele and A-A-C haplotype in the male probands (P < 0.05). Mother of the probands also showed higher occurrence of the 'C' allele and "CC" genotype (P < 0.01). Executive function was better in presence of rs2345039 "GG" (P = 0.04) while IQ score was higher in presence of rs6551665 "AA" (P = 0.06). Linkage disequilibrium between rs6551665 and rs2345039 was stronger in the ADHD cases, chiefly in the male probands. Multifactor dimensionality reduction analysis showed strong interaction between rs6551665 and rs2345039 in the male probands while in the female probands rs1868790 and rs6551665 revealed non-linear interaction. Based on these observations, we infer that ADGRL3 may have a role in the aetiology of ADHD in this population warranting further in depth investigation.

Association of DNA repair gene XPC Ala499Val (rs2228000 C>T) and Lys939Gln (rs2228001 A>C) polymorphisms with the risk of chronic myeloid leukemia: A case-control study in a South Indian population

BACKGROUND

Xeroderma pigmentosum complementation group C (XPC), a DNA repair protein, plays an important role in the maintenance of genomic integrity and is essential for the nucleotide excision repair pathway. Polymorphisms in the XPC gene may alter DNA repair leading to genetic instability and oncogenesis. The present study aimed to assess the relationship between the XPC Ala499Val (rs2228000 C>T) and Lys939Gln (rs2228001 A>C) non-synonymous polymorphisms and susceptibility to chronic myeloid leukemia (CML) pathogenesis, disease progression and the response to targeted therapeutic regimen, imatinib mesylate.

METHODS

This case-control study included 212 cases and 212 controls, and the genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism assays.

RESULTS

Our results showed significant association of variant CT (odds ratio = 1.92, 95% confidence interval = 1.21-3.06, p = 0.003) and TT (odds ratio = 2.84, 95% confidence interval = 1.22-6.71, p = 0.007) genotypes in patients with the XPC Ala499Val polymorphism and CML risk. In addition, these genotypes were associated with CML progression to advanced phases (p = 0.006), splenomegaly (p = 0.017) and abnormal lactate dehydrogenase levels (p = 0.03). XPC Lys939Gln was found to correlate with a poor response to therapy, showing borderline significant association with minor cytogenetic response (p = 0.08) and a poor molecular response (p = 0.06). Significant association of the Ala499Val and Lys939Gln polymorphisms with prognosis was observed (Hasford high risk, p = 0.031 and p = 0.019, respectively). Haplotype analysis showed a strong correlation of variant TC haplotype with poor therapy responses (minor cytogenetic response, p = 0.019; poor molecular response, p < 0.0001).

CONCLUSIONS

In conclusion, our results suggest that XPC Ala499Val is a high-penetrance CML susceptibility polymorphism. Both polymorphisms studied are considered as genetic markers with respect to assessing disease progression, therapy response and prognosis in CML patients.

Optimized permutation testing for information theoretic measures of multi-gene interactions

Background

Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large.

Results

In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 10³ for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples.

Conclusions

The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.

Brief Survey on Machine Learning in Epistasis

In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interaction can have a strong effect on the function of the two genes. These changes then can alter the consequences of the biological processes, influencing the organism's phenotype. Machine learning is an area of computer science that develops statistical methods able to recognize patterns from data. A typical machine learning algorithm consists of a training phase, where the model learns to recognize specific trends in the data, and a test phase, where the trained model applies its learned intelligence to recognize trends in external data. Scientists have applied machine learning to epistasis problems multiple times, especially to identify gene-gene interactions from genome-wide association study (GWAS) data. In this brief survey, we report and describe the main scientific articles published in data mining and epistasis. Our article confirms the effectiveness of machine learning in this genetics subfield.

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

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

Polymorphisms of the matrix metalloproteinase genes are associated with essential hypertension in a Caucasian population of Central Russia

This study aimed to determine possible association of eight polymorphisms of seven MMP genes with essential hypertension (EH) in a Caucasian population of Central Russia. Eight SNPs of the MMP1, MMP2, MMP3, MMP7, MMP8, MMP9, and MMP12 genes and their gene–gene (epistatic) interactions were analyzed for association with EH in a cohort of 939 patients and 466 controls using logistic regression and assuming additive, recessive, and dominant genetic models. The functional significance of the polymorphisms associated with EH and 114 variants linked to them (r² ≥ 0.8) was analyzed in silico. Allele G of rs11568818 MMP7 was associated with EH according to all three genetic models (OR = 0.58–0.70, p_perm = 0.01–0.03). The above eight SNPs were associated with the disorder within 12 most significant epistatic models (OR = 1.49–1.93, p_perm < 0.02). Loci rs1320632 MMP8 and rs11568818 MMP7 contributed to the largest number of the models (12 and 10, respectively). The EH-associated loci and 114 SNPs linked to them had non-synonymous, regulatory, and eQTL significance for 15 genes, which contributed to the pathways related to metalloendopeptidase activity, collagen degradation, and extracellular matrix disassembly. In summary, eight studied SNPs of MMPs genes were associated with EH in the Caucasian population of Central Russia.

Asymptotic Distributions of Empirical Interaction Information

Interaction Information is one of the most promising interaction strength measures with many desirable properties. However, its use for interaction detection was hindered by the fact that apart from the simple case of overall independence, asymptotic distribution of its estimate has not been known. In the paper we provide asymptotic distributions of its empirical versions which are needed for formal testing of interactions. We prove that for three-dimensional nominal vector normalized empirical interaction information converges to the normal law unless the distribution coincides with its Kirkwood approximation. In the opposite case the convergence is to the distribution of weighted centred chi square random variables. This case is of special importance as it roughly corresponds to interaction information being zero and the asymptotic distribution can be used for construction of formal tests for interaction detection. The result generalizes result in Han (Inf Control 46(1):26–45 1980) for the case when all coordinate random variables are independent. The derivation relies on studying structure of covariance matrix of asymptotic distribution and its eigenvalues. For the case of 3 × 3 × 2 contingency table corresponding to study of two interacting Single Nucleotide Polymorphisms (SNPs) for prediction of binary outcome, we provide complete description of the asymptotic law and construct approximate critical regions for testing of interactions when two SNPs are possibly dependent. We show in numerical experiments that the test based on the derived asymptotic distribution is easy to implement and yields actual significance levels consistently closer to the nominal ones than the test based on chi square reference distribution.

A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions

BACKGROUND

Non-additive interactions among genes are frequently associated with a number of phenotypes, including known complex diseases such as Alzheimer's, diabetes, and cardiovascular disease. Detecting interactions requires careful selection of analytical methods, and some machine learning algorithms are unable or underpowered to detect or model feature interactions that exhibit non-additivity. The Random Forest method is often employed in these efforts due to its ability to detect and model non-additive interactions. In addition, Random Forest has the built-in ability to estimate feature importance scores, a characteristic that allows the model to be interpreted with the order and effect size of the feature association with the outcome. This characteristic is very important for epidemiological and clinical studies where results of predictive modeling could be used to define the future direction of the research efforts. An alternative way to interpret the model is with a permutation feature importance metric which employs a permutation approach to calculate a feature contribution coefficient in units of the decrease in the model's performance and with the Shapely additive explanations which employ cooperative game theory approach. Currently, it is unclear which Random Forest feature importance metric provides a superior estimation of the true informative contribution of features in genetic association analysis.

RESULTS

To address this issue, and to improve interpretability of Random Forest predictions, we compared different methods for feature importance estimation in real and simulated datasets with non-additive interactions. As a result, we detected a discrepancy between the metrics for the real-world datasets and further established that the permutation feature importance metric provides more precise feature importance rank estimation for the simulated datasets with non-additive interactions.

CONCLUSIONS

By analyzing both real and simulated data, we established that the permutation feature importance metric provides more precise feature importance rank estimation in the presence of non-additive interactions.

Genotype-Environment Interaction Analysis of NQO1, CYP2E1, and NAT2 Polymorphisms and the Risk of Childhood Acute Lymphoblastic Leukemia: A Report From the Mexican Interinstitutional Group for the Identification of the Causes of Childhood Leukemia

Background: Acute lymphoblastic leukemia (ALL) is the main type of cancer in children. In Mexico and other Hispanic populations, the incidence of this neoplasm is one of the highest reported worldwide. Functional polymorphisms of various enzymes involved in the metabolism of xenobiotics have been associated with an increased risk of developing ALL, and the risk is different by ethnicity. The aims of the present study were to identify whether NQO1, CYP2E1, and NAT2 polymorphisms or some genotype-environmental interactions were associated with ALL risk in Mexican children.

Methods: We conducted a case-control study including 478 pediatric patients diagnosed with ALL and 284 controls (children without leukemia). Ancestry composition of a subset of cases and controls was assessed using 32 ancestry informative markers. Genetic-environmental interactions for the exposure to hydrocarbons were assessed by logistic regression analysis.

Results: The polymorphisms rs1801280 (OR 1.54, 95% CI 1.21–1.93), rs1799929 (OR 1.96, 95% CI 1.55–2.49), and rs1208 (OR 1.44, 95% CI 1.14–1.81) were found to increase the risk of ALL; being the risks higher under a recessive model (OR 2.20, 95% CI 1.30–1.71, OR 3.87, 95% CI 2.20–6.80, and OR 2.26, 95% CI 1.32–3.87, respectively). Gene-environment interaction analysis showed that NAT2 rs1799929 TT genotype confers high risk to ALL under exposure to fertilizers, insecticides, hydrocarbon derivatives, and parental tobacco smoking. No associations among NQO1, CYP2E1, and ALL were observed.

Conclusion: Our study provides evidence for the association between NAT2 polymorphisms/gene-environment interactions, and the risk of childhood ALL in Mexican children.

Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease

As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene-gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene-gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene-gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene-gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene-gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.