MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.

BACKGROUND: Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization. RESULTS: MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. CONCLUSION: These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

BACKGROUND: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. RESULTS: Using cross-correlation to capture gene-gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene-gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. CONCLUSION: Static gene-gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns.

Computational multigene interactions in virus growth and infection spread.

Viruses persist in nature owing to their extreme genetic heterogeneity and large population sizes, which enable them to evade host immune defenses, escape antiviral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness are yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene-gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus, a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to eleven deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multicycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multicycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation in high- and low-host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene-gene interactions can differently affect how viruses grow and spread.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

MICA and NLRP3 gene polymorphisms interact synergistically affecting the risk of ankylosing spondylitis.

Ankylosing spondylitis (AS) is an autoinflammatory disease that affects the sacroiliac joints, causing stiffness and pain in the back. MICA is a ligand of the NKG2D receptor, and an increase in its expression affects the immune response in various diseases. NLRP3 is a multiprotein complex that promotes the release of IL-1ß, but its role in AS has been minimally explored. The objective of this study was to analyze the association and interaction of polymorphic variants of the MICA and NLRP3 genes in patients with AS. In this case-control study, patients with AS were included and compared with healthy controls of Mexican origin. The polymorphisms rs4349859 and rs116488202 of MICA and rs3806268 and rs10754558 of NLRP3 were genotyped using TaqMan probes. Associations were determined using logistic regression models, while interactions were analyzed by the multifactorial dimensionality reduction (MDR) method. A P value < 0.05 was considered statistically significant. The minor allele of rs4349859 (A) and rs116488202 (T) of MICA polymorphisms showed risk associations with AS (OR = 9.22, 95% CI = 4.26-20.0, P < 0.001; OR = 9.36, 95% CI = 4.17-21.0, P < 0.001), while the minor allele of the rs3806268 (A) polymorphism of NLRP3 was associated with protection (OR = 0.55, 95% CI = 0.33-0.91, P = 0.019). MDR analysis revealed synergistic interactions between the MICA and NLRP3 polymorphisms (P = 0.012). In addition, high- and low-risk genotypes were identified among these variants. The study findings suggest that the MICA rs4349859 A allele and rs116488202 T allele are associated with AS risk. An interaction between MICA and NLRP3 was observed which could increase the genetic risk in AS.

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

Polymorphism of Estrogen Receptor Genes and Its Interactions With Neurodevelopmental Genes in Attention Deficit Hyperactivity Disorder Among Chinese Han Descent.

OBJECTIVE: Attention deficit hyperactivity disorder (ADHD) is a polygenic neurodevelopmental disorder with significant gender differences. The sexual dimorphism of ADHD may be associated with estrogen acting through estrogen receptors (ESR). This study investigates the impact of ESR gene polymorphism and its interactions with neurodevelopmental genes on ADHD susceptibility. METHODS: The study compared genotyping data of single nucleotide polymorphisms in ESR1 and ESR2 in 1,035 ADHD cases and 962 controls. The gene-gene interactions between ESR genes and three neurodevelopmental genes (brain-derived neurotrophic factor [BDNF], synaptosomal-associated protein of 25 kDa gene [SNAP25], and cadherin-13 [CDH13]) in ADHD were investigated using generalized multifactor dimensionality reduction and verified by logistic regression analysis. RESULTS: The G allele of rs960070/ESR2 (empirical p=0.0076) and the A allele of rs8017441/ESR2 (empirical p=0.0426) were found significantly higher in ADHD cases than in the controls but not in male or female subgroups. Though no difference was found in all subjects or females, the A allele of rs9340817/ESR1 (empirical p=0.0344) was found significantly higher in ADHD cases than controls in males. We also found genetic interaction models between ESR2 gene, neurodevelopmental genes and ADHD susceptibility in males (ESR2 rs960070/BDNF rs6265/BDNF rs2049046/SNAP25 rs362987/CDH13 rs6565113) and females (ESR2 rs960070/BDNF rs6265/BDNF rs2049046) separately, though it was negative in overall subjects. CONCLUSION: The ESR gene polymorphism associates with ADHD among Chinese Han children, with interactions between ESR genes and neurodevelopmental genes potentially influencing the susceptibility of ADHD.

Analytical Approaches to Uncover Genetic Associations for Rare Outcomes: Lessons from West Nile Neuroinvasive Disease.

West Nile viral infection causes severe neuroinvasive disease in less than 1% of infected humans. There are no targeted therapeutics for this serious and potentially fatal disease, and to date no vaccine has been approved for humans. With climate change expected to result in rising incidence of West Nile and other related vector-borne viral infections, there is an increasing need to identify those at risk for serious disease and potential leads for therapeutic and vaccine development. Genetic variation, particularly in genes whose products are either directly or indirectly connected to immune response to infections, is a critical avenue of investigation to identify those at higher risk of clinically apparent West Nile infection. Given the small percent of infections that progress to severe disease and the relatively low numbers of reported infections, it is challenging to conduct well-powered studies to identify genetic factors associated with more severe outcomes. In this chapter, we outline several approaches with the objective to take full advantage of all available data in order to identify genetic factors which lead to increased risk of severe West Nile neuroinvasive disease. These methods are generalizable to other conditions with limited cohort size and rare outcomes.

Drug-Drug-Gene Interactions in Cardiovascular Medicine.

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

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.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene-gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene-gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene-phenotype relations.

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia.

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene-gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.

ABCB1 and OPRM1 single-nucleotide polymorphisms collectively modulate chronic shoulder pain and dysfunction in South African breast cancer survivors.

Background: Chronic shoulder pain/disability is a well-recognized side effect of treatment for breast cancer, with ∼40% of patients experiencing this, despite receiving pain management. To manage acute and chronic pain, several opioids are commonly prescribed. Pharmacogenomics have implicated genes within the opioid signaling pathway, including ABCB1 and OPRM1, to contribute to an individual's variable response to opioids. Aim: To evaluate ABCB1 (rs1045642 G>A, rs1128503 G>A) and OPRM1 (rs1799971 A>G, rs540825 T>A) single-nucleotide polymorphisms (SNPs) in chronic shoulder pain/disability in BCS. Materials & methods: TaqManTM assays were used to genotype ABCB1 and OPRM1 SNPs within the BCS (N = 252) cohort. The Shoulder Pain and Disability Index was used to evaluate pain and disability features associated with shoulder pathologies. Participants end scores for each feature (pain, disability and combined [pain and disability]) were categorized into no-low (>30%) and moderate-high (≥30%) scores. Statistical analysis was applied, and significance was accepted at p < 0.05. Results: Of participants, 27.0, 19.0 and 22.0% reported moderate-high pain, disability and combined (pain and disability) scores, respectively. ABCB1:rs1045642-(A/A) genotype was significantly associated with disability (p = 0.028: no-low [14.9%] vs mod-high [4.3%]) and combined (pain and disability) (p = 0.011: no-low [15.9%] vs mod-high [5.7%]). The ABCB1:rs1045642-(A) allele was significantly associated with disability (p = 0.015: no-low [37.9%] vs mod-high [23.9%]) and combined (pain and disability) (p = 0.003: no-low [38.5%] vs mod-high [23.6%]). The inferred ABCB1 (rs1045642 G>A - rs1128503 G>A): A-G (p = 0.029; odds ratio [OR]: 0.0; 95% CI: 0.0-0.0) and the OPRM1 (rs1799971 A>G - rs540825 T>A): G-T (p = 0.019; OR: 0.33; 95% CI: 0.14-0.75) haplotypes were associated with disability and pain, respectively. Gene-gene interactions showed the ABCB1 (rs1045642 G>A) - OPRM1 (rs540825 T>A) combinations, (A-T) (p = 0.019; OR: 0.62; 95% CI: 0.33-1.16) and (G-A) (p = 0.021; OR: 1.57; 95% CI: 0.30-3.10) were associated with disability. Conclusion: The study implicated ABCB1 with shoulder pain and disability; and haplotype analyses identified specific genetic intervals within ABCB1 and OPRM1 to associate with chronic shoulder pain and disability. Evidence suggests that potentially gene-gene interactions between ABCB1 and OPRM1 contribute to chronic shoulder pain and disability experienced in this SA cohort.

The missing heritability in type 1 diabetes.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Melatonin Receptor 1B Genetic Variants on Susceptibility to Gestational Diabetes Mellitus: A Hospital-Based Case-Control Study in Wuhan, Central China.

Purpose: The aim of the study was to find out the associations of Melatonin receptor 1B (MTNR1B) genetic variants with gestational diabetes mellitus (GDM) in Wuhan of central China. Patients and Methods: A hospital-based case-control study that included 1679 women was carried out to explore the associations of MTNR1B single nucleotide polymorphisms (SNPs) with GDM risk, which were analyzed through logistic regression analysis by adjusting age, pre-pregnancy BMI and family history of diabetes. Multifactor dimensionality reduction was applied to determine gene-gene interactions between SNPs. Results: MTNR1B SNPs rs10830962, rs10830963, rs1387153, rs7936247 and rs4753426 were significantly associated with GDM risk (P<0.05). The rs10830962/G, rs10830963/G, rs1387153/T, and rs7936247/T were risk variants, whereas rs4753426/T was protective variant for GDM development. Fasting plasma glucose (FPG) and 1h-plasma glucose (PG) were significantly different among genotypes at rs10830962 and rs10830963, whereas 2h-PG levels were not. Gene-gene interactions were not found among the five SNPs on GDM risk. Conclusion: MTNR1B genetic variants have significant associations but no gene-gene interactions with GDM risk in central Chinese population. Furthermore, MTNR1B SNPs have significant relationships with glycemic traits.

The Impact of Genetic Polymorphisms in Glutamate-Cysteine Ligase, a Key Enzyme of Glutathione Biosynthesis, on Ischemic Stroke Risk and Brain Infarct Size.

The purpose of this pilot study was to explore whether polymorphisms in genes encoding the catalytic (GCLC) and modifier (GCLM) subunits of glutamate-cysteine ligase, a rate-limiting enzyme in glutathione synthesis, play a role in the development of ischemic stroke (IS) and the extent of brain damage. A total of 1288 unrelated Russians, including 600 IS patients and 688 age- and sex-matched healthy subjects, were enrolled for the study. Nine common single nucleotide polymorphisms (SNPs) of the GCLC and GCLM genes were genotyped using the MassArray-4 system. SNP rs2301022 of GCLM was strongly associated with a decreased risk of ischemic stroke regardless of sex and age (OR = 0.39, 95%CI 0.24−0.62, p < 0.0001). Two common haplotypes of GCLM possessed protective effects against ischemic stroke risk (p < 0.01), but exclusively in nonsmoker patients. Infarct size was increased by polymorphisms rs636933 and rs761142 of GCLC. The mbmdr method enabled identifying epistatic interactions of GCLC and GCLM gene polymorphisms with known IS susceptibility genes that, along with environmental risk factors, jointly contribute to the disease risk and brain infarct size. Understanding the impact of genes and environmental factors on glutathione metabolism will allow the development of effective strategies for the treatment of ischemic stroke and disease prevention.

Insights into Interactions of Human Cytochrome P450 17A1: A Review.

Cytochrome P450s are a widespread and vast superfamily of hemeprotein monooxygenases that metabolize physiologically essential chemicals necessary for most species' survival, ranging from protists to plants to humans. They catalyze the synthesis of steroid hormones, cholesterol, bile acids, and arachidonate metabolites and the degradation of endogenous compounds, such as steroids, fatty acids, and other catabolizing compounds as an energy source and detoxifying xenobiotics, such as drugs, procarcinogens, and carcinogens. The human CYP17A1 is one of the cytochrome P450 genes located at the 10q chromosome. The gene expression occurs in the adrenals and gonads, with minor amounts in the brain, placenta, and heart. This P450c17 cytochrome gene is a critical steroidogenesis regulator which performs two distinct activities: 17 alpha-hydroxylase activity (converting pregnenolone to 17- hydroxypregnenolone and progesterone to 17-hydroxyprogesterone; these precursors are further processed to provide glucocorticoids and sex hormones) and 17, 20-lyase activity (which converts 17-hydroxypregnenolone to DHEA). Dozens of mutations within CYP17A1 are found to cause 17-alpha-hydroxylase and 17, 20-lyase deficiency. This condition affects the function of certain hormone-producing glands, resulting in high blood pressure levels (hypertension), abnormal sexual development, and other deficiency diseases. This review highlights the changes in CYP17A1 associated with gene-gene interaction, drug-gene interaction, chemical-gene interaction, and its biochemical reactions; they have some insights to correlate with the fascinating functional characteristics of this human steroidogenic gene. The findings of our theoretical results will be helpful to further the design of specific inhibitors of CYP17A1.

The Relationship Between Chemokine and Chemokine Receptor Genes Polymorphisms and Chronic Obstructive Pulmonary Disease Susceptibility in Tatar Population from Russia: A Case Control Study.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease affecting primarily distal respiratory pathways and lung parenchyma. This study aimed to determine possible genetic association of chemokine and chemokine receptor genes polymorphisms with COPD in a Tatar population from Russia. SNPs of CCL20, CCR6, CXCL8, CXCR1, CXCR2, CCL8, CCL23, CCR2, and CX3CL1 genes and their gene-gene interactions were analyzed for association with COPD in cohort of 601 patients and 617 controls. As a result statistically significant associations with COPD in the study group under the biologically plausible assumption of additive genetic model were identified in CCL20 (rs6749704) (P = 0.00001, OR 1.55), CCR6 (rs3093024) (P = 0.0003, OR 0.74), CCL8 (rs3138035) (P = 0.0001, OR 0.67), CX3CL1 (rs170364) (P = 0.023, OR 1.21), CXCL8 (rs4073) (P = 0.007, OR 1.23), CXCR2 (rs2230054) (P = 0.0002, OR 1.32). Following SNPs CCL20 (rs6749704), CX3CL1 (rs170364), CCL8 (rs3138035), CXCL8 (rs4073), CXCR2 (rs2230054) showed statistically significant association with COPD only in smokers. The association of CCR6 (rs3093024) with COPD was confirmed both in smokers and in non-smokers. A relationship between smoking index and CCL20 (rs6749704) (P = 0.04), CCR6 (rs3093024) (P = 0.007), CCL8 (rs3138035) (P = 0.0043), and CX3CL1 (rs170364) (P = 0.04) was revealed. A significant genotype-dependent variation of Forced Vital Capacity was observed for CCL23 (rs854655) (P = 0.04). Forced Expiratory Volume in 1 s / Forced Vital Capacity ratio was affected by CCL23 (rs854655) (P = 0.05) and CXCR2 (rs1126579) (P = 0.02). Using the APSampler algorithm, we obtained nine gene-gene combinations that remained significantly associated with COPD; loci CCR2 (rs1799864) and CCL8 (rs3138035) were involved in the largest number of the combinations. Our results indicate that CCL20 (rs6749704), CCR6 (rs3093024), CCR2 (rs1799864), CCL8 (rs3138035), CXCL8 (rs4073), CXCR1 (rs2234671), CXCR2 (rs2230054), and CX3CL1 (rs170364) polymorphisms are strongly associated with COPD in Tatar population from Russia, alone and in combinations. For the first time combination of the corresponding SNPs were considered and as a result 8 SNP patterns were associated with increased risk of COPD.

Polymorphisms in COMT and OPRM1 Collectively Contribute to Chronic Shoulder Pain and Disability in South African Breast Cancer Survivors'.

Chronic shoulder pain and disability is a common adverse effect experienced by >40% of breast cancer survivors (BCS). Pain management protocols for acute and chronic pain include the use of opioids and opioid derivatives. Furthermore, pain-modulating genes, such as COMT and OPRM1, have been linked to the aetiology of chronic pain. This study aimed to investigate the association between genetic variants of major pain modulator genes and chronic pain/disability in BCS. Assessment of pain, disability and combined (pain and disability) symptoms were determined using the Shoulder Pain and Disability Index (SPADI). Participants were grouped according to their scores such as no-low (<30%) and moderate-high (≥30%) groups of pain, disability and combined (pain and disability). Genotyping of the COMT rs6269 (A > G), rs4633 (C > T), rs4818 (C > G) and the functional rs4680(G > A) SNPs within the BCS (N = 252) cohort were conducted using TaqMan® SNP assays. Genotype, allele, haplotype, and allele-allele combination frequencies were evaluated. Statistical analysis was applied, with significance accepted at p < 0.05. The COMT rs4680:A/A genotype was significantly associated with moderate-high pain (p = 0.024, OR: 3.23, 95% CI: 1.33-7.81) and combined (pain and disability) (p = 0.015, OR: 3.81, 95% CI: 1.47-9.85). The rs4680:A allele was also significantly associated with moderate-high pain (p = 0.035, OR: 1.58, 95% CI: 1.03-2.43) and combined (pain and disability) (p = 0.017, OR: 1.71, 95% CI: 1.07-2.71). For the inferred COMT (rs6269 A > G-rs4680 G > A) haplotype analyses, the G-G (p = 0.026, OR: 0.67, 95% CI: 0.38-1.18) and A-A (p = 0.007, OR: 2.09, 95% CI: 0.89-4.88) haplotypes were significantly associated with reduced and increased likelihoods of reporting moderate-high pain, respectively. The inferred A-A (p = 0.003, OR: 2.18, 95% CI: 0.92-5.17) haplotype was also significantly associated with combined (pain and disability). Gene-gene interaction analyses further showed allele-allele combinations for COMT (rs4680 G > A)-OPRM1 (rs1799971 A > G) and COMT (rs4680 G > A)-OPRM1(rs540825 T > A) were associated with reporting pain and combined (pain and disability) symptoms, p < 0.05. The findings of this study suggest that COMT and OPRM1 SNPs play a role in the development of chronic shoulder pain/disability in BCS in a unique South African cohort from the Western Cape.

Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.

Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.