Gene Interaction Model Research Articles

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.

Molecular Screening of the Thrombophilic Variants Performed at G-141 Laboratory among Saudi Infertile Women

Infertility is a major issue at present and is a common disease that exists in both male and female reproductive systems, described as failure to attain pregnancy. The most important physiological phenomenon for establishing clinical pharmacy is defined as female infertility (FI). Obesity enhances the risks for many chronic disorders, especially causing a high risk for women’s reproductive health. The relationship between infertile women and thrombophilia is characterized by abnormal blood coagulation. Among the thrombophilic variants, Factor V Leiden (FVL), prothrombin (PT) and methyl tetrahydrofolate reductase (MTHFR) in genes such as G1691A (rs6020), G20210A (rs1799963) and C677T (rs1801133) are commonly studied in the majority of human diseases. In this case–control study, we investigated the role of thrombophilic variants such as G1691A, G20210A and C677T in the FVL, PII and MTHFR genes in Saudi infertile women. Based on sample size calculation, 100 female infertile and 100 control (fertile) women were selected based on inclusion and exclusion criteria. Genotyping was performed with polymerase chain reaction and followed with precise restriction enzymes, which can accurately detect the nucleotide amendment variants in G1691A, G20210A and C677T. The required statistics were applied between the case (infertile) and control (fertile) women to document the role of the G1691A, G20210A and C677T variants in Saudi infertile women. In this study, age, weight and BMI were found to be high in the control women in comparison to the infertile women. None of the genotypes, genetic models or allele frequencies were associated with G1691A, G20210A or C677T SNPs (p > 0.05). Furthermore, the regression model and ANOVA analysis also showed negative statistical associations. The combination of genotypes and allele frequencies among G1691A, G20210A and C677T SNPs showed positive associations in the recessive model (p = 0.0006). Finally, the GMDR model showed moderate associations with the gene–gene interaction, dendrogram and depletion models. Finally, this study confirmed that thrombophilic SNPs have no role and may not be involved in Saudi infertile women.

Multifactor dimensionality reduction reveals the effect of interaction between ERAP1 and IFIH1 polymorphisms in psoriasis susceptibility genes

Background: Psoriasis is a common immune-mediated hyperproliferative skin dysfunction with known genetic predisposition. Gene-gene interaction (e.g., between HLA-C and ERAP1) in the psoriasis context has been reported in various populations. As ERAP1 has been recognized as a psoriasis susceptibility gene and plays a critical role in antigen presentation, we performed this study to identify interactions between ERAP1 and other psoriasis susceptibility gene variants. Methods: We validated psoriasis susceptibility gene variants in an independent cohort of 5,414 patients with psoriasis and 5,556 controls. Multifactor dimensionality reduction (MDR) analysis was performed to identify the interaction between variants significantly associated with psoriasis in the validation cohort and ERAP1 variants. We then conducted a meta-analysis of those variants with datasets from exome sequencing, target sequencing, and validation analyses and used MDR to identify the best gene-gene interaction model, including variants that were significant in the meta-analysis and ERAP1 variants. Results: We found that 19 of the replicated variants were identified with p < 0.05 and detected six single-nucleotide polymorphisms of psoriasis susceptibility genes in the meta-analysis. MDR analysis revealed that the best predictive model was that between the rs27044 polymorphism of ERAP1 and the rs7590692 polymorphism of IFIH1 (cross-validation consistency = 9/10, test accuracy = 0.53, odds ratio = 1.32 (95% CI, 1.09-1.59), p < 0.01). Conclusion: Our findings suggest that the interaction between ERAP1 and IFIH1 affects the development of psoriasis. This hypothesis needs to be tested in basic biological studies.

Understanding the Dynamics of Blast Resistance in Rice-Magnaporthe oryzae Interactions.

Rice is a global food grain crop for more than one-third of the human population and a source for food and nutritional security. Rice production is subjected to various stresses; blast disease caused by Magnaporthe oryzae is one of the major biotic stresses that has the potential to destroy total crop under severe conditions. In the present review, we discuss the importance of rice and blast disease in the present and future global context, genomics and molecular biology of blast pathogen and rice, and the molecular interplay between rice–M. oryzae interaction governed by different gene interaction models. We also elaborated in detail on M. oryzae effector and Avr genes, and the role of noncoding RNAs in disease development. Further, rice blast resistance QTLs; resistance (R) genes; and alleles identified, cloned, and characterized are discussed. We also discuss the utilization of QTLs and R genes for blast resistance through conventional breeding and transgenic approaches. Finally, we review the demonstrated examples and potential applications of the latest genome-editing tools in understanding and managing blast disease in rice.

The relationship between polymorphism of insulin-like growth factor I gene and susceptibility to type 2 diabetes in Uygur population, Xinjiang, China

BackgroundType 2 diabetes (T2DM) susceptibility varies among different populations and is affected by gene single nucleotide polymorphism (SNP). Insulin-like growth factor (IGF)-1 gene, which has many SNP loci, is involved in T2DM pathogenesis. However, the relationship of IGF-1 gene polymorphism with T2DM in Uyghur population is less studied.ObjectiveTo investigate the relationship between T2DM susceptibility and polymorphism of IGF-1 gene in Uyghur population of Xinjiang, China.MethodsThis study enrolled 220 cases (122 males (55.46%) and 98 females (44.54%); mean age of 53.40 ± 10.94 years) of T2DM patients (T2DM group) and 229 (124 males (54.15%) and 105 females (45.85%); mean age of 51.64 ± 10.48 years) healthy controls (control group). Biochemical indexes were determined. IGF-1 gene polymorphism was analyzed by SNP genotyping.ResultsThe levels of TG, HDL, LDL, BUN, and Cr were statistically significant between the T2DM group and the control group. In terms of IGF-1 polymorphism, T2DM group had higher frequency of AA genotype (OR = 2.40, 95% CI = 1.19–4.84) and allele A (OR = 1.55, 95% CI = 1.17–2.06) of rs35767 loci, suggesting that rs35767 is related to the occurrence of T2DM. A total of 5 gene interaction models was obtained through analyzing the interaction of 5 SNP loci with the GMDR method. Among them, the two-factor model that included rs35767 locus and rs5742694 locus had statistical difference with a large cross-validation consistency (10/10). The combination of GG/CC, GA/AA, AA/AA, and AA/AC genotype was in high-risk group, whereas the combination of GG/AA, GG/AC, GA/AC and GA/CC genotype was in the low-risk group. The risk of T2DM in the high-risk group was 2.165 times than that of the low-risk group (OR = 2.165, 95% CI = 1.478–3.171).ConclusionTG, HDL, LDL, BUN, and Cr are influencing factors of T2DM in Uyghur population. The rs35767 locus of IGF-1 gene may be associated with T2DM in Uyghur population. The high-risk group composing of rs35767 locus and rs5742694 locus has a higher risk of T2DM.

Arabidopsis thaliana computationally-generated next-state gene interaction models

The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the mea-surements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic techniques, must lend themselves to repeatable and sound practices. This paper holistically focuses on the process of producing gene interaction models based on transcript abundance data from Arabidopsis thaliana after stimulation by a plant hormone.

Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data.

Background:Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions – approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, penalisation and penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.Methods:Five large genomic datasets were extracted from Gene Expression Omnibus. ‘Gold-standard’ regression models were trained on subspaces of these datasets (, ). Penalised regression models were trained on small samples from these subspaces () and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty ‘preselection’ according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation.Results:-penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models. -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions. also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics.Conclusions:This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of penalisation for structural selection and penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

BackgroundMetformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor.ObjectiveThe objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure.MethodsPhysiologically based pharmacokinetic models were developed in PK-Sim® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development.ResultsThe established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species.ConclusionsWhole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.Electronic supplementary materialThe online version of this article (10.1007/s40262-020-00896-w) contains supplementary material, which is available to authorized users.

Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer.

Non-genetic transcriptional variability is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes and survive therapy. How might these rare states arise and disappear within the population? It is unclear whether the canonical models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified mechanisms. We show that a minimal model of transcriptional bursting and gene interactions can give rise to rare coordinated high expression states. These states occur more frequently in networks with low connectivity and depend on three parameters. While entry into these states is initiated by a long transcriptional burst that also triggers entry of other genes, the exit occurs through independent inactivation of individual genes. Together, we demonstrate that established principles of gene regulation are sufficient to describe this behavior and argue for its more general existence. A record of this paper's transparent peer review process is included in the Supplemental Information.

Production of a predominantly male tilapia progeny using two Malawian tilapias, Oreochromis shiranus and Oreochromis karongae

A study was conducted at National Aquaculture Centre, Domasi, Malawi, to compare sex ratios and growth of fry from interspecific hybridization of two Malawian tilapias Oreochromis karongae x O. shiranus and hormonal sex reversal using 17α-methyl testosterone of hapa spawned and hatchery spawned O. shiranus fry. The swim up fry from all the matings including the control groups were raised using a feed containing 38 % crude protein at 3 % body weight per day. However, for the fry from hapa and hatchery spawned O. shiranus, the feed contained 17α-methyl testosterone at 60 mg/kg. After 28 days, sex was determined from 80 fish using Aceto-carmine technique. ANOVA showed no significant differences (P > 0.05) in the proportion of males 88 %, 93 %, and 95 % from the hybrids, pure hapa and pure hatchery fingerlings, respectively. However, these proportions were significantly different (P < 0.05) from that obtained from the control (53 %). The sex ratio (7.3:1) (male:female) of the hybrids significantly deviated (P < 0.05) from the expected sex ratios under the gene interaction model. The mean body weight of the hybrids (11.4 ± 0.2 g) was significantly different (P < 0.05) from the control (7.73 ± 0.95). The results are discussed with respect to commercial production of predominantly male fish to control precocious breeding in ponds in Malawi.

Abstract LB-209: Novel pathway prediction for colorectal cancer to liver metastasis through bioinformatics

Abstract Background: Liver metastases are the most common cause of colorectal cancer (CRC) related deaths, with no reliable clinical predictors. Therefore, identifying potential candidate predictors is crucial for clinical application and management. Methodology: The Gene Expression Omnibus (GEO) datasets GSE49355, GSE41258 and GSE81558 for genes and GSE54088 and GSE56350 for miRNAs were used to identify common differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between paired primary CRC tissues and the corresponding liver metastatic tissues. Then, DEMs targets were identified from DEGs. These were verified in GEO datasets comprising gene, miRNA and miRNA exosome profiles of CRC patients with no distant metastases (M0) and distant metastases (M1); the interaction networks and pathways were also mapped. Results: There were 49 upregulated and 13 downregulated DEGs, and 16 downregulated and 14 upregulated DEMs; between DEGs and DEMs targets, there were five upregulated and four downregulated genes. MiR-20a was significantly high in M1, with the combination of MiR-20a, -495, -576-5p and -449a offering a superior prediction value (sensitivity of 83.3%, specificity of 63.8%, AUC of 0.725). In miRNA exosomes, MiR-576-5p was significantly low in M1, while MiR-20a and -449a were highly expressed, and combination of miR-20a, -495, -576-5p, -449a, -619 and -382 provided best predictive value (sensitivity=75.0%, specificity=88.2%, AUC=0.875). The miRNA target CDH2 was significantly highly expressed in M1 while KNG1 and MMP2 were lowly expressed. The miRNA to gene interaction model was demonstrated, and enriched pathways were; regulation of IGF transport and uptake by IGFBPs, extracellular matrix organization, signal transduction and immune system. Conclusion: This model predicts novel pathway for predicting CRC to liver metastases. This may be clinically applicable. Citation Format: Precious Takondwa Makondi, Chien-Yu Huang, Yu-Jia Chang. Novel pathway prediction for colorectal cancer to liver metastasis through bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-209.

Evidence for the genetic basis and epistatic interactions underlying ocean- and river-maturing ecotypes of Pacific Lamprey (Entosphenus tridentatus) returning to the Klamath River, California.

Surveys of genomic variation have improved our understanding of the relationship between fitness-related phenotypes and their underlying genetic basis. In some cases, single large-effect genes have been found to underlie important traits; however, complex traits are expected to be under polygenic control and elucidation of multiple gene interactions may be required to fully understand the genetic basis of the trait. In this study, we investigated the genetic basis of the ocean- and river-maturing ecotypes in anadromous Pacific lamprey (Entosphenus tridentatus). In Pacific lamprey, the ocean-maturing ecotype is distinguished by advanced maturity of females (e.g., large egg mass) at the onset of freshwater migration relative to immature females of the river-maturing ecotype. We examined a total of 219 adult Pacific lamprey that were collected at-entry to the Klamath River over a 12-month period. Each individual was genotyped at 308 SNPs representing known neutral and adaptive loci and measured at morphological traits, including egg mass as an indicator of ocean- and river-maturing ecotype for females. The two ecotypes did not exhibit genetic structure at 148 neutral loci, indicating that ecotypic diversity exists within a single population. In contrast, we identified the genetic basis of maturation ecotypes in Pacific lamprey as polygenic, involving two unlinked gene regions that have a complex epistatic relationship. Importantly, these gene regions appear to show stronger effects when considered in gene interaction models than if just considered additive, illustrating the importance of considering epistatic effects and gene networks when researching the genetic basis of complex traits in Pacific lamprey and other species.

A Simple Approach for Representation of Gene Regulatory Networks (GRN)

Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions, however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.

Interleukin-10 family cytokines pathway: genetic variants and psoriasis.

Interleukin (IL)-10 family cytokines IL-10, IL-19, IL-20 and IL-24 have been implicated in autoimmune diseases and we have previously reported that genetic variants in the IL10 gene cluster were associated with psoriasis. To analyse the relationship between genetic polymorphisms in the IL10 gene cluster and psoriasis. This study also explores whether there are gene-gene interactions among these genetic polymorphisms. A total of 377 patients with psoriasis and 403 matched healthy controls were enrolled to carry out a case-control study for 48 single-nucleotide polymorphisms (SNPs) of the IL10 gene cluster. Genotyping for the SNPs was conducted on the Applied Biosystems 3730 DNA Analyzer using SNPlex® technology. Generalized multifactor dimensionality reduction (GMDR) analysis was applied to discover a likely gene-gene interaction model among the SNPs. The results showed that the allele distributions of IL10 gene cluster SNPs are significantly different between the case and control groups. Carriers of the IL10 T allele (rs1554286) and the IL20 T allele (rs1400986) conferred protection from psoriasis [odds ratio (OR) = 0·63, corrected P-value (Pc) = 0·007; OR = 0·62, Pc = 0·038, respectively]. GMDR analysis displayed a significant gene-gene interaction between IL10 (rs1554286) and IL20 (rs1518108) variants. The strongest protective effect was found with the block 1 haplotype ACATA in the IL10 gene (Pc = 0·004). This study presents a novel finding that the combination of the two SNPs, IL10 (rs1554286) and IL20 (rs1518108), is associated with a reduced risk of psoriasis. Our results indicate that genetic variants of the immunomodulatory IL10 and IL20 genes may offer a protective effect in Europeans from Russia. Independent studies are required to verify the results and find a possible functional explanation.

RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

BackgroundDistinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other’s expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range.ResultsWe used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other’s fluctuations in a miRNA-dependent manner in single cells. We show that miRNA–target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10–1000 copies of targets per cell.ConclusionsOur results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.

Study of β-carotene enhancing ‘Or’ gene effects on yield and contributing traits in mid-season Indian cauliflower (Brassica oleracea var. botrytis L.)

Nature of gene action of EC 625883, an orange curd colour genotype, on yield and contributing traits incauliflower was studied by using five generation model (P1, P2, F1, F2 and F3) in three cross combinations, viz., DC-309 × EC 625883, CC-35 × EC 625883 and DC 18–19 × EC 625883 for ten quantitative traits. The orange curdcolour Or gene donor homozygous line EC 625883 and three divergent recipient mid season (November-January)maturity white curd lines, namely, DC-309, CC-35, and DC 18–19 were involved in hybridization. Generationmean analysis using scaling test indicated that epistasis gene interaction model fitted well for most of the traitsunder study in all the three cross combinations. The complementary type of gene interaction was observed fornumber of days to marketable curd maturity, total plant weight, marketable curd weight and net curd weight. Thepresence of complementary type of interactions and prevalence of high magnitude of non-additive gene effectssuggested exploitation of heterosis breeding for improvement in cauliflower using Or gene for enhancing of β-carotene and micronutrient simultaneously.

Associations between gene polymorphisms of the apelin–APJ system and the risk of hypertension

The aim of this study was to assess the associations between single nucleotide polymorphisms (SNPs) of the apelin and APJ (apelin receptor) genes and the risk of hypertension in people living in the south-east coastal area of China. A cross-sectional study involving 1031 participants was performed. Genotypes of the apelin (rs3115757, rs56204867 and rs3761581) and APJ (rs7119375 and rs9943582) genes were determined by the TaqMan® MGB probe method. For male patients, the frequencies of mutant alleles in the three apelin gene SNPs were significantly different between the hypertension and control groups (all p < 0.05), while no significant difference was obtained for frequencies of mutant alleles in the two APJ gene SNPs (p > 0.05). For females, the frequencies of mutant alleles in all five SNPs were not significantly different between the hypertension and control groups (all p > 0.05). After adjusting for several factors, the risk of developing hypertension increased significantly in patients, regardless of gender, carrying rs3115757-C, rs56204867-C or rs3761581-A allele (all p < 0.05). The optimal gene–gene interaction model for both males and females with regard to hypertension was apelin rs3761581–apelin rs3115757–APJ rs7119375. In conclusion, gene polymorphisms of the apelin–APJ system are associated with susceptibility to hypertension.

Identifying genetic loci affecting antidepressant drug response in depression using drug-gene interaction models.

Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified. Within the present review, we will discuss the methodological challenges and limitations of the studies that have been conducted on this topic to date (e.g., 'treated-only design', statistical power) and we will discuss how specifically drug-gene interaction models can be used to be better able to identify genetic variants associated with antidepressant drug response in depression.

Epistasis and covariance: how gene interaction translates into genomic relationship.

Models based on additive marker effects and on epistatic interactions can be translated into genomic relationship models. This equivalence allows to perform predictions based on complex gene interaction models and reduces computational effort significantly. In the theory of genome-assisted prediction, the equivalence of a linear model based on independent and identically normally distributed marker effects and a model based on multivariate Gaussian distributed breeding values with genomic relationship as covariance matrix is well known. In this work, we demonstrate equivalences of marker effect models incorporating epistatic interactions and corresponding mixed models based on relationship matrices and show how to exploit these equivalences computationally for genome-assisted prediction. In particular, we show how models with epistatic interactions of higher order (e.g., three-factor interactions) translate into linear models with certain covariance matrices and demonstrate how to construct epistatic relationship matrices for the linear mixed model, if we restrict the model to interactions defined a priori. We illustrate the practical relevance of our results with a publicly available data set on grain yield of wheat lines growing in four different environments. For this purpose, we select important interactions in one environment and use this knowledge on the network of interactions to increase predictive ability of grain yield under other environmental conditions. Our results provide a guide for building relationship matrices based on knowledge on the structure of trait-related gene networks.

Identifying genetic loci affecting antidepressant drug response in depression using drug–gene interaction models

Identifying genetic loci affecting antidepressant drug response in depression using drug–gene interaction models