Analysis of epistasis for diabetic nephropathy among type 2 diabetic patients

The rs705708 A allele of the ERBB3 gene is associated with lower prevalence of diabetic retinopathy and arterial hypertension and with improved renal function in type 1 diabetic patients

INTRODUCTION

The Erb-b2 receptor tyrosine kinase 3 (ERBB3) is involved in autoimmune processes related to type 1 diabetes mellitus (T1DM) pathogenesis. Accordingly, some studies have suggested that single nucleotide polymorphisms (SNPs) in the ERBB3 gene confer risk for T1DM. Proliferation-associated protein 2G4 (PA2G4) is another candidate gene for this disease because it regulates cell proliferation and adaptive immunity. Moreover, PA2G4 regulates ERBB3. To date, no study has evaluated the association of PA2G4 SNPs and T1DM.

AIM

To evaluate the association of ERBB3 rs705708 (G/A) and PA2G4 rs773120 (C/T) SNPs with T1DM and its clinical and laboratory characteristics.

METHODS

This case-control study included 976 white subjects from Southern Brazil, categorized into 501 cases with T1DM and 475 non-diabetic controls. The ERBB3 and PA2G4 SNPs were genotyped by allelic discrimination-real-time PCR.

RESULTS

ERBB3 rs705708 and PA2G4 rs773120 SNPs were not associated with T1DM considering different inheritance models and also when controlling for covariables. However, T1DM patients carrying the ERBB3 rs705708 A allele developed T1DM at an earlier age vs. G/G patients. Interestingly, in the T1DM group, the rs705708 A allele was associated with lower prevalence of diabetic retinopathy and arterial hypertension as well as with improved renal function (higher estimated glomerular filtration rate and lower urinary albumin excretion levels) compared to G/G patients.

CONCLUSIONS

Although no association was observed between the ERBB3 rs705708 and PA2G4 rs773120 SNPs and T1DM, the rs705708 A allele was associated, for the first time in literature, with lower prevalence of diabetic retinopathy and arterial hypertension. Additionally, this SNP was associated with improved renal function.

Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection

Genome-wide association studies (GWAS) explain a fraction of the underlying heritability of genetic diseases. Investigating epistatic interactions between two or more loci help to close this gap. Unfortunately, the sheer number of loci combinations to process and hypotheses prohibit the process both computationally and statistically. Epistasis test prioritization algorithms rank likely epistatic single nucleotide polymorphism (SNP) pairs to limit the number of tests. However, they still suffer from very low precision. It was shown in the literature that selecting SNPs that are individually correlated with the phenotype and also diverse with respect to genomic location leads to better phenotype prediction due to genetic complementation. Here, we propose that an algorithm that pairs SNPs from such diverse regions and ranks them can improve prediction power. We propose an epistasis test prioritization algorithm that optimizes a submodular set function to select a diverse and complementary set of genomic regions that span the underlying genome. The SNP pairs from these regions are then further ranked w.r.t. their co-coverage of the case cohort. We compare our algorithm with the state of the art on three GWAS and show that (1) we substantially improve precision (from 0.003 to 0.652) while maintaining the significance of selected pairs, (2) decrease the number of tests by 25-fold, and (3) decrease the runtime by 4-fold. We also show that promoting SNPs from regulatory/coding regions improves the performance (up to 0.8). Potpourri is available at http:/ciceklab.cs.bilkent.edu.tr/potpourri.

Multiple-microarray analysis for identification of hub genes involved in tubulointerstial injury in diabetic nephropathy

Diabetic nephropathy (DN) is a primary cause of renal failure. However, studies providing renal gene expression profiles of diabetic tubulointerstitial injury are scarce and its molecular mechanisms still await clarification. To identify vital genes involved in the diabetic tubulointerstitial injury, three microarray data sets from gene expression omnibus (GEO) were downloaded. A total of 127 differentially expressed genes (DEGs) were identified by limma package. Gene set enrichment analysis (GSEA) plots showed that sister chromatid cohesion was the most significant enriched gene set positively correlated with the DN group while retinoid X receptor binding was the most significant enriched gene set positively correlated with the control group. Enriched Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs mostly included extracellular matrix organization, extracellular space, extracellular matrix structural constituent, and Staphylococcus aureus infection. Twenty hub genes from three significant modules were ascertained by Cytoscape. Correlation analysis and subgroup analysis between hub genes and clinical features of DN showed that ALB, ANXA1, APOH, C3, CCL19, COL1A2, COL3A1, COL4A1, COL6A3, CXCL6, DCN, EGF, HRG, KNG1, LUM, SERPINA3, SPARC, SRGN, and TIMP1 may involve in diabetic tubulointerstitial injury. ConnectivityMap analysis indicated the most significant three compounds are 5182598, thapsigargin and 5224221. In conclusion, this study may provide new insights into the molecular mechanisms underlying diabetic tubulointerstitial injury as well as potential targets for diagnosis and therapeutics of DN.

ISN Forefronts Symposium 2015: Nuclear Receptors and Diabetic Nephropathy

Diabetic nephropathy (DN) is the major reason for end stage renal disease in the western world. Patients with DN developed more severe cardiovascular complications with worse prognosis. In spite of tight blood pressure and glucose control through applying angiotensin II receptor antagonism, angiotensin receptor inhibitors and even direct renin inhibitors, the progression and development of DN has continued to accelerate. Nuclear receptors are, with few exceptions, ligand-depended transcription factors some of which modulate genes involved in the transportation and metabolism of carbohydrate or lipid, and inflammation. Considering the diverse biological functions of nuclear receptors, efforts have been made to explore their contributions to the pathogenesis of DN and potential therapeutic strategies. This review is mainly focused on the association between various nuclear receptors and the pathogenesis of DN, the potential beneficial effects of targeting these receptors for preventing the progress of DN, and the important role that nuclear receptors may play in future therapeutic strategies for DN.

Epistatic effects of multiple receptor genes on pathophysiology of asthma - its limits and potential for clinical application

To date, genome-wide association studies (GWAS) permit a comprehensive scan of the genome in an unbiased manner, with high sensitivity, and thereby have the potential to identify candidate genes for the prevalence or development of multifactorial diseases such as bronchial asthma. However, most studies have only managed to explain a small additional percentage of hereditability estimates, and often fail to show consistent results among studies despite large sample sizes. Epistasis is defined as the interaction between multiple different genes affecting phenotypes. By applying epistatic analysis to clinical genetic research, we can analyze interactions among more than 2 molecules (genes) considering the whole system of the human body, illuminating dynamic molecular mechanisms. An increasing number of genetic studies have investigated epistatic effects on the risk for development of asthma. The present review highlights a concept of epistasis to overcome traditional genetic studies in humans and provides an update of evidence on epistatic effects on asthma. Furthermore, we review concerns regarding recent trends in epistatic analyses from the perspective of clinical physicians. These concerns include biological plausibility of genes identified by computational statistics, and definition of the diagnostic label of 'physician-diagnosed asthma'. In terms of these issues, further application of epistatic analysis will prompt identification of susceptibility of diseases and lead to the development of a new generation of pharmacological strategies to treat asthma.

MG132 ameliorates kidney lesions by inhibiting the degradation of Smad7 in streptozotocin-induced diabetic nephropathy

BACKGROUND

Smad7 is the main negative regulatory protein in the transforming growth factor-β (TGF-β) downstream signaling pathway, which plays an important role in diabetic nephropathy (DN) and may be related to the ubiquitin proteasome pathway (UPP).

AIM

We investigated the role of UPP in regulating TGF-β/SMAD signaling and explored the therapeutic effect of the ubiquitin proteasome inhibitor MG132 on DN.

METHODS

Wistar rats were randomly divided into a diabetes group and a normal control group. Rats in the diabetes group were injected intraperitoneally with streptozotocin. Diabetic rats were then randomly divided into a diabetic nephropathy group (DN group), an MG132 high concentration (MH) group, and an MG132 low concentration (ML) group. After 8 weeks of treatment, 24-hour urinary microalbumin (UAlb), urinary protein/urinary creatinine (Up/Ucr) values, ALT, AST, Bcr, kidney damage, TGF-β, Smad7, fibronectin (FN), and Smurf2 were detected.

RESULTS

The body mass and Smad7 protein expression decreased in DN group, but kidney weight, kidney weight index, UAlb, Up/Ucr, FN and Smurf2 mRNA expression, and TGF-β protein expression increased. However, these changes diminished following treatment with MG132, and a more pronounced effect was evident in MH group compared to ML group.

CONCLUSION

MG132 alleviates kidney damage by inhibiting Smad7 ubiquitin degradation and TGF-β activation in DN.

Association between retinoid-X receptor-gamma genetic polymorphisms and diabetic retinopathy

Retinoid-X receptor (RXR) is one of the members of the nuclear hormone receptor superfamily. It forms heterodimers with many nuclear receptors, such as the peroxisome proliferative-activated receptor, which has been proposed to be involved in diabetic complications, including retinopathy. A recent study revealed that RXR-alpha has antioxidant properties and is associated with diabetic retinopathy. We found that the RXR-gamma gene is involved in the pathogenesis of diabetic nephropathy. We also hypothesized that the RXR-gamma gene has a role in the development of diabetic retinopathy. We examined 213 diabetic patients, who were divided into retinopathy or no retinopathy groups. Nine selected single nucleotide polymorphisms (SNPs) in the RXR-gamma gene were evaluated. The diabetic retinopathy group had longer diabetes duration, higher body mass indexes, and higher systolic blood pressure, as well as higher concentrations of fasting plasma glucose, blood urine nitrogen, and creatine. One SNP--rs3818569 of the RXR-gamma gene was found to be associated with increased risk for diabetic retinopathy in both allele and genotype frequencies (P = 0.0023 and 0.0057, respectively). Analysis with multivariate logistic regression revealed that the dominant RXR-gamma GG genotype is a risk factors for the development of diabetic retinopathy (odds ratio = 2.388; 95% confidence interval = 1.17-4.875). We conclude that the RXR-gamma rs3818569 SNP is associated with diabetic retinopathy development in the Taiwanese population.

Lack of Association between Glutathione S-Transferase-M1, -T1, and -P1 Polymorphisms and Olanzapine-Induced Weight Gain in Korean Schizophrenic Patients

OBJECTIVE

Oxidative stress may be an important pathogenic mechanism in the obesity and metabolic syndrome. The aims of this study was to assess the possible association between the oxidative stress related Glutathione S-Transferase genes (GST-M1, GST-T1, and GST-P1) variants and the olanzapine-induced weight gain in Korean schizophrenic patients.

METHODS

We categorized 78 schizophrenic patients into two groups the more than 7% weight gain from baseline (weight gain >/=7%) and the less weight gain (weight gain <7%) groups according to weight change between before and after long-term olanzapine treatment (440+/-288 days). All participants were genotyped for the GST-M1, GST-T1 and GST-P1 genes. Differences in allele frequencies between cohorts with different body weight changes were evaluated by a chi-square analysis and Fisher's exact test. The multifactor dimensionality reduction (MDR) approach was used to analyze gene-gene interactions.

RESULTS

Mean body weight gain was 5.42 kg. There was no difference in the null genotype distribution of GST-M1 and -T1 between subjects with body weight gain >/=7% compared to subjects with body weight gain <7% (p>0.05). No significant difference in GST-P1 genotype and allele frequencies were observed between the groups (p>0.05). MDR analysis did not show a significant interaction between the three GST gene variants and susceptibility to weight gain (p>0.05).

CONCLUSION

These findings do not support a relationship between the genetic variants of three GST genes (GST-M1, -T1 and -P1) and weight gain in Korean schizophrenic patients receiving olanzapine treatment.

Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses

BACKGROUND

Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.

RESULTS

The proposed 2LOmb algorithm performs an omnibus permutation test on ensembles of two-locus analyses. The algorithm consists of four main steps: two-locus analysis, a permutation test, global p-value determination and a progressive search for the best ensemble. 2LOmb is benchmarked against an exhaustive two-locus analysis technique, a set association approach, a correlation-based feature selection (CFS) technique and a tuned ReliefF (TuRF) technique. The simulation results indicate that 2LOmb produces a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs) and a low number of output SNPs in purely epistatic two-, three- and four-locus interaction problems. The interaction models constructed from the 2LOmb outputs via a multifactor dimensionality reduction (MDR) method are also included for the confirmation of epistasis detection. 2LOmb is subsequently applied to a type 2 diabetes mellitus (T2D) data set, which is obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium (WTCCC). After primarily screening for SNPs that locate within or near 372 candidate genes and exhibit no marginal single-locus effects, the T2D data set is reduced to 7,065 SNPs from 370 genes. The 2LOmb search in the reduced T2D data reveals that four intronic SNPs in PGM1 (phosphoglucomutase 1), two intronic SNPs in LMX1A (LIM homeobox transcription factor 1, alpha), two intronic SNPs in PARK2 (Parkinson disease (autosomal recessive, juvenile) 2, parkin) and three intronic SNPs in GYS2 (glycogen synthase 2 (liver)) are associated with the disease. The 2LOmb result suggests that there is no interaction between each pair of the identified genes that can be described by purely epistatic two-locus interaction models. Moreover, there are no interactions between these four genes that can be described by purely epistatic multi-locus interaction models with marginal two-locus effects. The findings provide an alternative explanation for the aetiology of T2D in a UK population.

CONCLUSION

An omnibus permutation test on ensembles of two-locus analyses can detect purely epistatic multi-locus interactions with marginal two-locus effects. The study also reveals that SNPs from large-scale or genome-wide case-control data which are discarded after single-locus analysis detects no association can still be useful for genetic epidemiology studies.

Association study between glutathione S-transferase GST-M1, GST-T1, and GST-P1 polymorphisms and tardive dyskinesia

OBJECTIVES

Data from several studies suggest that oxidative stress may play a role in the pathophysiology of tardive dyskinesia (TD). Glutathione S-transferase (GST) enzymes play important roles in protecting cells against oxidative stress. In the present study, we investigated the hypothesis that polymorphisms in genes for these detoxifying enzymes can influence susceptibility to TD in patients with schizophrenia.

METHODS

The GST-M1, GST-T1, and GST-P1 loci were analyzed by polymerase chain reaction (PCR)-based methods in 83 schizophrenic patients with TD and 126 schizophrenic without TD who were matched for antipsychotic drug exposure and other relevant variables. The multifactor dimensionality reduction (MDR) approach was used to analyze gene-gene interactions.

RESULTS

There were no significant differences in the distributions of the GST-M1, GST-T1, and GST-P1 genotypes between the TD and non-TD groups (p > 0.05). However, in comparison of the severity of TD among genotypes using Poisson regression showed that Ile/Ile genotype of GST-P1 had higher AIMS score compared to Ile/Val + Val/Val genotypes (X(2) = 7.13, p = 0.008). MDR analysis did not show a significant interaction between the three GST gene variants and susceptibility to TD (p > 0.05).

CONCLUSIONS

These results suggest that GST gene polymorphisms do not confer increased susceptibility to TD in patients with schizophrenia but TD severity might be related with GST-P1 variants.

Association analyses of the interaction between the ADSS and ATM genes with schizophrenia in a Chinese population

BACKGROUND

The blood-derived RNA levels of the adenylosuccinate synthase (ADSS) and ataxia telangiectasia mutated (ATM) genes were found to be down- and up-regulated, respectively, in schizophrenics compared with controls, and ADSS and ATM were among eight biomarker genes to discriminate schizophrenics from normal controls. ADSS catalyzes the first committed step of AMP synthesis, while ATM kinase serves as a key signal transducer in the DNA double-strand breaks response pathway. It remains unclear whether these changes result from mutations or polymorphisms in the two genes.

METHODS

Six SNPs in the ADSS gene and three SNPs in the ATM gene in a Chinese population of 488 schizophrenics and 516 controls were genotyped to examine their association with schizophrenia (SZ). Genotyping was performed using the Sequenom platform.

RESULTS

There was no significant difference in the genotype, allele, or haplotype distributions of the nine SNPs between cases and controls. Using the Multifactor Dimensionality Reduction (MDR) method, we found that the interactions among rs3102460 in the ADSS gene and rs227061 and rs664143 in the ATM gene revealed a significant association with SZ. This model held a maximum testing accuracy of 60.4% and a maximum cross-validation consistency of 10 out of 10.

CONCLUSION

These findings suggest that the combined effects of the polymorphisms in the ADSS and ATM genes may confer susceptibility to the development of SZ in a Chinese population.

Positional cloning of "Lisch-Like", a candidate modifier of susceptibility to type 2 diabetes in mice

In 404 Lep^ob/ob F2 progeny of a C57BL/6J (B6) x DBA/2J (DBA) intercross, we mapped a DBA-related quantitative trait locus (QTL) to distal Chr1 at 169.6 Mb, centered about D1Mit110, for diabetes-related phenotypes that included blood glucose, HbA1c, and pancreatic islet histology. The interval was refined to 1.8 Mb in a series of B6.DBA congenic/subcongenic lines also segregating for Lep^ob. The phenotypes of B6.DBA congenic mice include reduced β-cell replication rates accompanied by reduced β-cell mass, reduced insulin/glucose ratio in blood, reduced glucose tolerance, and persistent mild hypoinsulinemic hyperglycemia. Nucleotide sequence and expression analysis of 14 genes in this interval identified a predicted gene that we have designated “Lisch-like” (Ll) as the most likely candidate. The gene spans 62.7 kb on Chr1qH2.3, encoding a 10-exon, 646–amino acid polypeptide, homologous to Lsr on Chr7qB1 and to Ildr1 on Chr16qB3. The largest isoform of Ll is predicted to be a transmembrane molecule with an immunoglobulin-like extracellular domain and a serine/threonine-rich intracellular domain that contains a 14-3-3 binding domain. Morpholino knockdown of the zebrafish paralog of Ll resulted in a generalized delay in endodermal development in the gut region and dispersion of insulin-positive cells. Mice segregating for an ENU-induced null allele of Ll have phenotypes comparable to the B.D congenic lines. The human ortholog, C1orf32, is in the middle of a 30-Mb region of Chr1q23-25 that has been repeatedly associated with type 2 diabetes.

Type 2 diabetes (T2D) accounts for over 90% of instances of diabetes and is a leading cause of medical morbidity and mortality. Twin studies indicate a strong polygenic contribution to susceptibility within the context of obesity. Although approximately ten genes making important contributions to individual risk have been identified, it is clear that others remain to be identified. In this study, we intercrossed obese, diabetes-resistant and diabetes-prone mouse strains to implicate a genetic interval on mouse Chr1 associated with reduced β-cell numbers and elevated blood glucose. We narrowed the region using molecular genetics and computational approaches to identify a novel gene we designated “Lisch-like” (Ll). The orthologous human genetic interval has been repeatedly implicated in T2D. Mice with an induced mutation that reduces Ll expression are impaired in both β-cell development and glucose metabolism, and reduced expression of the homologous gene in zebrafish disrupts islet development. Ll is expressed in organs implicated in the pathophysiology of T2D (hypothalamus, islets, liver, and skeletal muscle) and is predicted to encode a transmembrane protein that could mediate cholesterol transport and/or convey signals related to cell division. Either mechanism could mediate effects on β-cell mass that would predispose to T2D.

GPDTI: a Genetic Programming Decision Tree induction method to find epistatic effects in common complex diseases

MOTIVATION

The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities.

RESULTS

We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors.

AVAILABILITY

Information on the generated data sets and executable code is available upon request.

Prediction of complex traits based on the epistasis of multiple haplotypes

Analysis of epistasis, or gene-gene interactions, is of particular importance for revealing the molecular mechanisms of complex human diseases. Multiple genes, each of which has a moderate effect, might interact and produce a complex phenotypic trait. In this paper, we present a novel method of epistasis analysis, utilizing multiple phase-resolved haplotypes residing in different genomic regions. Prediction models can then be derived from the epistasis to indicate the susceptibility of a person to a dichrotomous phenotypic trait. The simulation results showed that the prediction accuracy of this method is dependent on the penetrance rate of the underlying model. The computation cost, on the other hand, is dependent on the number of genomic regions involved for the complex phenotypic trait.

Pattern-recognition techniques with haplotype analysis in pharmacogenomics

Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. However, it would be extremely inefficient to test all the 10 million common SNPs for an association study. Here we review haplotype analysis and pattern-recognition techniques to systematically select candidate SNPs for candidate-gene association studies in pharmacogenomics. First, we survey linkage disequilibrium methods to identify tag SNPs and explore the use of haplotypes as genetic markers that are correlated and associated with drug efficacy. Secondly, we investigate pattern-recognition algorithms and statistical analyses to assess drug efficacy based on SNPs and other factors. Finally, we study pattern-recognition approaches to evaluate the epistasis among genes and SNPs. These techniques may provide tools for clinical association studies and help find genes/SNPs involved in responses to therapeutic drugs or adverse drug reactions.