Disease-related Single Nucleotide Polymorphisms Research Articles

The effects of case/control ratio and sample size on genome-wide association studies: A simulation study.

Genome-wide association studies (GWAS) is a useful tool for the detection of disease or quantitative trait-related genetic variations in the veterinary field. For a binary trait, a case/control experiment is designed in GWAS. However, there is limited information on the optimal case/control and sample size in GWAS. In this study, it was aimed to detect the effects of case/control ratio and sample size for GWAS using computer simulation under certain assumptions. Using the PLINK software, we simulated three different disease scenarios. In scenario 1, we simulated 10 different case/control ratios with increasing ratio of cases to controls. In scenario 2, we did versa of scenario 1 with the increasing ratio of controls to cases. In scenarios 1 and 2, sample size gradually was increased with the change case/control ratios. In scenario 3, the total sample size was fixed to 2000 to see real effects of case/control ratio on the number of disease-related single nucleotide polymorphisms (SNPs). The results showed that the number of disease-related SNPs were the highest when the case/control ratio is close to 1:1 in scenarios 1 and 2 and did not change with an increase in sample size. Similarly, the number of disease-related SNPs was the highest in case/control ratios 1:1 in scenario 3. However, unbalanced case/control ratio caused the detection of lower number of disease-related SNPs in scenario 3. The estimated average power of SNPs was highest when case/control ratio is 1:1 in all scenarios. All findings led to the conclusion that an increase in sample size may enhance the statistical power of GWAS when the number of cases is small. In addition, case/control ratio 1:1 may be the optimal ratio for GWAS. These findings may be valuable not only for veterinary field but also for human clinical experiments.

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns.

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Polygenic risk scores and personalized approaches to cardiometabolic disease prevention and treatment: A short review

Accounting for the role of genetic variants in disease is increasingly gaining ground as a major contributing factor to the maximization of successful precision medicine and personalized nutrition approaches. An aggregated technique to quantifying genetic effect refers to the development and use of disease-specific Polygenic Risk Scores (PRSs) deriving from the sum of the weighted effects of multiple disease-related Single Nucleotide Polymorphisms (SNPs), mainly from Genome-Wide association studies (GWAS). Integration of PRS use in medical and nutritional practice is largely discussed in current literature, with special attention to: i) disease prediction accuracy after PRS consideration and their potential utility; ii) the role of current methodological approaches used to derive reliable results and the effect of limitations such as ancestry or population size; iii) the familiarization of healthcare professionals with the meaning of genetic information; and iv) the context-based interpretations of PRS results in the formation of personalized advice. In this context, the present short review aims to summarize current findings on PRS use and utility in cardiometabolic, weight-related disorders and discuss future directions for their potential integration in the practice of personalized nutrition.

Construction and comprehensive analysis of the biological network related to rheumatoid arthritis-related interstitial lung disease.

Interstitial lung disease (ILD) is a severe manifestation of rheumatoid arthritis (RA), which is characterized by low survival time post-diagnosis. Thus, it is important to explore the role of gene regulation related with ILD. Constructed a RA-ILD-related long chain noncoding RNA - messenger RNA (lncRNA-mRNA) network (ILD-LMN), based on ILD- and RA-related genes. We analyzed the topological properties of the resulting network. The results for network modularization and functional analysis showed that ILD-LMN performed basic and specific functions in ILD pathology. Furthermore, differential expression and correlation analysis of hub nodes revealed highly correlated competitive endogenous RNA regulatory relationships with important roles in pathological regulation. Following this, statistical analysis of disease-related single nucleotide polymorphisms (SNPs) in hub lncRNAs revealed that some of transcription factor-related SNPs were significantly associated with the expression of lncRNA. In fact, these SNPs exhibited significant differential expression in disease and normal samples. These results suggest that ILD-LMN has important implications in the study of disease. Altogether, the study of RA- and ILD-related lncRNA and genes on the basis of biological network would assist in providing better treatment opportunities for ILD patients. Additionally, it would promote further research on treatment of the disease.

Alzheimer's disease related single nucleotide polymorphisms and correlation with intracerebral hemorrhage incidence.

Apolipoprotein E alleles have been associated with both Alzheimer’s disease (AD) and intracerebral hemorrhage (ICH). In addition, ICH is associated with a markedly high risk of subsequent dementia compared to other subtypes of stroke. We sought to evaluate if other genetic markers for AD were also associated with ICH. We examined whether published AD risk single nucleotide polymorphisms (SNPs) and haplotypes were associated with ICH utilizing genome-wide association study data from 2 independent studies (genetic and environmental risk factors for hemorrhagic stroke [GERFHS] study and genetics of cerebral hemorrhage with anticoagulation [GOCHA]). Analyses included evaluation by location of ICH. GERFHS and GOCHA cohorts contained 745 ICH cases and 536 controls for analysis. The strongest association was on 1q32 near Complement receptor type 1 (CR1), where rs6701713 was associated with all ICH (P = .0074, odds ratio [OR] = 2.07) and lobar ICH (P = .0073, OR = 2.80). The 51 most significant 2-SNP haplotypes associated with lobar ICH were identified within the Clusterin (CLU) gene. We identified that variation within CR1 and CLU, previously identified risk factors for AD, and are associated with an increased risk for ICH driven primarily by lobar ICH. Previous work implicated CR1 and CLU in cerebral amyloid clearance, the innate immune system, and cellular stress response.

Impact of Fkbp5 × early life adversity × sex in humanised mice on multidimensional stress responses and circadian rhythmicity

The cumulative load of genetic predisposition, early life adversity (ELA) and lifestyle shapes the prevalence of psychiatric disorders. Single nucleotide polymorphisms (SNPs) in the human FKBP5 gene were shown to modulate disease risk. To enable investigation of disease-related SNPs in behaviourally relevant context, we generated humanised mouse lines carrying either the risk (AT) or the resiliency (CG) allele of the rs1360780 locus and exposed litters of these mice to maternal separation. Behavioural and physiological aspects of their adult stress responsiveness displayed interactions of genotype, early life condition, and sex. In humanised females carrying the CG- but not the AT-allele, ELA led to altered HPA axis functioning, exploratory behaviour, and sociability. These changes correlated with differential expression of genes in the hypothalamus, where synaptic transmission, metabolism, and circadian entrainment pathways were deregulated. Our data suggest an integrative role of FKBP5 in shaping the sex-specific outcome of ELA in adulthood.

Protein arginine N-methyltransferase 1 gene polymorphism is associated with proliferative diabetic retinopathy in a Japanese population.

To investigate the effects of single-nucleotide polymorphisms (SNPs) around the protein arginine N-methyltransferase 1 (PRMT1) gene on the incidence and severity of diabetic retinopathy (DR). A total of 310 Japanese patients with type 2 diabetes mellitus (T2DM) were investigated. Genotyping of ten tagged SNPs were performed by quantitative real-time polymerase chain reaction (qRT-PCR). The association between each SNP genotype and diabetic microangiopathy was assessed using univariate analysis in a dominant model of the minor alleles followed by multivariate logistic regression analysis with the propensity score matching (PSM) method. The effect of disease-related SNP on PRMT1 and hypoxia-inducible factor-1α (HIF-1α) mRNA levels in vivo was evaluated by qRT-PCR. In the univariate analysis, the minor A allele at rs374569 and the minor C allele at rs3745468 were associated with DR severity (P = 0.047 and P = 0.003, respectively), but not diabetic nephropathy and peripheral polyneuropathy severity. Multivariate analysis showed that the rs3745468 variant caused an increased incidence of proliferative DR (PDR) (odds ratio 9.37, 95% confidence interval 1.12-78.0, P = 0.039). In the PSM cohort, the patients carrying the rs3745468 variant had lower PRMT1 mRNA levels compared to those without the variant (P = 0.037), and there was an inverse correlation between PRMT1 and HIF-1α mRNA levels (r = -0.233, P = 0.035). The rs3745468 variant in the PRMT1 gene was associated with an increased incidence of PDR in Japanese patients with T2DM and might be involved in the HIF-1-dependent hypoxic pathway through altered PRMT1 levels.

Effect of dietary fat intake and genetic risk on glucose and insulin-related traits in Brazilian young adults

PurposeThe development of metabolic diseases such as type 2 diabetes (T2D) is closely linked to a complex interplay between genetic and dietary factors. The prevalence of abdominal obesity, hyperinsulinemia, dyslipidaemia, and high blood pressure among Brazilian adolescents is increasing and hence, early lifestyle interventions targeting these factors might be an effective strategy to prevent or slow the progression of T2D.MethodsWe aimed to assess the interaction between dietary and genetic factors on metabolic disease-related traits in 200 healthy Brazilian young adults. Dietary intake was assessed using 3-day food records. Ten metabolic disease-related single nucleotide polymorphisms (SNPs) were used to construct a metabolic-genetic risk score (metabolic-GRS).ResultsWe found significant interactions between the metabolic-GRS and total fat intake on fasting insulin level (Pinteraction = 0.017), insulin-glucose ratio (Pinteraction = 0.010) and HOMA-B (Pinteraction = 0.002), respectively, in addition to a borderline GRS-fat intake interaction on HOMA-IR (Pinteraction = 0.051). Within the high-fat intake category [37.98 ± 3.39% of total energy intake (TEI)], individuals with ≥ 5 risk alleles had increased fasting insulin level (P = 0.021), insulin-glucose ratio (P = 0.010), HOMA-B (P = 0.001) and HOMA-IR (P = 0.053) than those with < 5 risk alleles.ConclusionOur study has demonstrated a novel GRS-fat intake interaction in young Brazilian adults, where individuals with higher genetic risk and fat intake had increased glucose and insulin-related traits than those with lower genetic risk. Large intervention and follow-up studies with an objective assessment of dietary factors are needed to confirm our findings.

Crohn’s disease–related single nucleotide polymorphisms are associated with ileal pouch afferent limb stenosis

BackgroundIleal pouch-anal anastomosis (IPAA) is a common surgical treatment for ulcerative colitis. Afferent limb stenosis is an infrequent complication following IPAA, suggesting underlying Crohn’s disease (CD). We hypothesized that CD-related single nucleotide polymorphisms (SNPs) are associated with afferent limb stenosis. MethodsAfferent limb stenosis and CD control group patients were recruited from a prospective institutional inflammatory bowel disease database and associated biobank. Patient demographics, Montreal classification, and medication use were recorded. Ten SNPs associated with stricturing Crohn’s disease were examined in genomic DNA and compared among afferent limb stenosis, stricturing CD, and non-stricturing CD controls. ResultsTwenty-seven afferent limb stenosis and 162 CD control group patients (108 stricturing, 54 non-stricturing) were identified. Patients were gender and race matched. Afferent limb stenosis and stricturing CD controls were younger at diagnosis (Montreal A1/A2 vs. A3) compared to non-stricturing CD controls (both p < 0.05). The majority of afferent limb stenosis patients were non-smokers compared to CD controls (74% vs. 36%, p < 0.01) and did not use biologic therapies (4% vs. 37%, p < 0.001). The FUT2 G allele was more frequent in afferent limb stenosis and stricturing CD controls compared to non-stricturing CD controls (both p < 0.05). The NOD2 T allele was more frequent in stricturing CD controls compared to afferent limb stenosis and non-stricturing CD controls (both p < 0.05). ConclusionAfferent limb stenosis patients are phenotypically similar to stricturing CD controls, but differ with lower smoking rates and lower NOD2 allele frequency. Such differences could contribute to the presentation delay with a stricturing phenotype. Selective SNP assessment may help categorize patients likely to develop afferent limb stenosis.

Two-Stage Model with Rough Cluster and Salp Optimization Technique for Epistasis Detection

The discovery of gene-gene interactions to identify complex diseases is one of the primary challenges in genome-wide association studies (GWAS). Genetic interactions (Epistasis) are typically seen as interactions between various single nucleotide polymorphisms (SNPs). Genetic interactions discovery can assist the researchers in identifying gene pathways, recognizing gene activity, and discovering potential drug targets. Rough Cluster based Salp Optimization for Epistasis detection (RCSalp-Epi) is a two-stage epistasis model that has been evaluated on a variety of simulated disease models. In the screening stage, the rough clustering algorithm is employed to partition the genotype data into different clusters. The selection stage presents Salp optimization with a single objective function (SalpEpi-SO) and multiple objective functions (SalpEpi-MO) over the clusters to find disease-related SNP combinations. RCSalp-performance Epi's is evaluated in comparison with SalpEpi-SO and SalpEpi-MO. The outcome of the experimental analysis revealed that RCSalp-Epi-MO is superior to SalpEpi-SO and SalpEpi-MO in terms of power and execution time.

Integration of SNP Disease Association, eQTL, and Enrichment Analyses to Identify Risk SNPs and Susceptibility Genes in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a complex disease caused by the disturbance of genetic and environmental factors. Single-nucleotide polymorphisms (SNPs) play a vital role in the genetic dissection of complex diseases. In-depth analysis of SNP-related information could recognize disease-associated biomarkers and further uncover the genetic mechanism of complex diseases. Risk-related variants might act on the disease by affecting gene expression and gene function. Through integrating SNP disease association study and expression quantitative trait loci (eQTL) analysis, as well as functional enrichment of containing known causal genes, four risk SNPs and four corresponding susceptibility genes were identified utilizing next-generation sequencing (NGS) data of COPD. Of the four risk SNPs, one could be found in the SNPedia database that stored disease-related SNPs and has been linked to a disease in the literature. Four genes showed significant differences from the perspective of normal/disease or variant/nonvariant samples, as well as the high performance of sample classification. It is speculated that the four susceptibility genes could be used as biomarkers of COPD. Furthermore, three of our susceptibility genes have been confirmed in the literature to be associated with COPD. Among them, two genes had an impact on the significance of expression correlation of known causal genes they interact with, respectively. Overall, this research may present novel insights into the diagnosis and pathogenesis of COPD and susceptibility gene identification of other complex diseases.

Detecting disease-related SNP loci based on GSP

A large number of studies have shown that susceptibility to some diseases may be related to some SNP (Single Nucleotide Polymorphism) loci. Accurate location of disease-related SNP loci can help people understand the pathogenesis of diseases and prevent them from happening. Based on Graph Signal Processing (GSP) theory, this paper proposes a novel method called GSP to detect disease-related SNP loci. GSP method is divided into five steps. The first step is to establish a graph signal model of all SNP loci. The second step is to extract the high-frequency components of graph signal with a high-pass filter. The third step is to transform the filtered graph signal with graph Fourier transformation. The fourth step is to extract the graph signal corresponding to high frequency with inverse graph Fourier transformation. The fifth step is to transform the extracted signal with Gaussian distribution and use Pauta criterion to screen out disease-related SNP loci. The experimental results show that GSP method can detect all disease-related SNP loci of simulation data and Genetic Disease A (GDA) and three of the four disease-related SNP loci of Age-related Macular Degeneration (AMD) with excellent performance. GSP method provides a new idea for the identification of pathogenicity loci.

Single-nucleotide polymorphisms based genetic risk score in the prediction of pancreatic cancer risk.

BACKGROUNDDisease-related single nucleotide polymorphisms (SNPs) based genetic risk score (GRS) has been proven to provide independent inherited risk other than family history in multiple cancer types.AIMTo evaluate the potential of GRS in the prediction of pancreatic cancer risk.METHODSIn this case-control study (254 cases and 1200 controls), we aimed to evaluate the association between GRS and pancreatic ductal adenocarcinoma (PDAC) risk in the Chinese population. The GRS was calculated based on the genotype information of 18 PDAC-related SNPs for each study subject (personal genotyping information of the SNPs) and was weighted by external odd ratios (ORs).RESULTSGRS was significantly different in cases and controls (1.96 ± 3.84 in PDACs vs 1.09 ± 0.94 in controls, P < 0.0001). Logistic regression revealed GRS to be associated with PDAC risk [OR = 1.23, 95% confidence interval (CI): 1.13-1.34, P < 0.0001]. GRS remained significantly associated with PDAC (OR = 1.36, 95%CI: 1.06-1.74, P = 0.015) after adjusting for age and sex. Further analysis revealed an association of increased risk for PDAC with higher GRS. Compared with low GRS (< 1.0), subjects with high GRS (2.0) were 99% more likely to have PDAC (OR: 1.99, 95%CI: 1.30-3.04, P = 0.002). Participants with intermediate GRS (1.0-1.9) were 39% more likely to have PDAC (OR: 1.39, 95%CI: 1.03-1.84, P = 0.031). A positive trend was observed (P trend = 0.0006).CONCLUSIONGRS based on PDAC-associated SNPs could provide independent information on PDAC risk and may be used to predict a high risk PDAC population.

Pharmosome: an integrative and collective database for exploration and analysis of single nucleotide polymorphisms associated with disease

Current single nucleotide polymorphism (SNP) databases are limited to a narrow set of SNPs, which has led to a lack of interactivity between different databases, limited tools to analyze and manipulate the already existing data, and complexity in the graphical user interface. Here we introduce Pharmosome, a web-based, user-friendly and collective database for more than 30,000 human disease-related SNPs, with dynamic pipelines to explore SNPs associated with disease development, drug response and the pathways shared between different genes related to these SNPs. Pharmosome implements several tools to design primers to detect SNPs in large genomes and facilitates analysis of different SNPs to determine relationships between them by aligning sequences, constructing phylogenetic trees, and providing consensus sequences illustrating the connections between SNPs. Pharmosome was written in the Python programming language using the Django web framework in combination with HTML, CSS, and JavaScript to receive user inputs, and process and export the sorted result to the interface. Pharmosome is available from: https://pharmosome.herokuapp.com/.

Decoding the Atlas of RNA Modifications from Epitranscriptome Sequencing Data.

Over 100 types of chemical modifications have been identified in protein-coding and noncoding RNAs (ncRNAs). However, the prevalence, regulation, and function of diverse RNA modifications remain largely unknown. In this chapter, we describe how to annotate, visualize, and analyze the RNA modification sites from the high-throughput epitranscriptome sequencing data using RMBase platform and software. We developed two stand-alone computational software, modAnnotator and metaProfile, to annotate and visualize RNA modification sites and their prevalence in the gene body. In addition, we constructed interactive web implementations to decode the atlas of various RNA modifications, including the N6-methyladenosine (m6A) modification, pseudouridine (Ψ) modification, 5-methylcytosine (m5C) modification, and 2'-O-methylation (2'-O-Me) modification, as well as other types of modifications. We also developed web-based interfaces to analyze the associations between RNA modification sites with miRNA target sites and disease-related single-nucleotide polymorphisms (SNPs). Moreover, RMBase provides a genome browser and a web-based modTool to query, annotate, and visualize various RNA modifications. RMBase is expected to provide comprehensive interfaces and tools to facilitate the analysis and functional study of the massive RNA modification sites. The software and platform are available at http://rna.sysu.edu.cn/rmbase/modSoftware.php .

RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.

More than 100 distinct chemical modifications to RNA have been characterized so far. However, the prevalence, mechanisms and functions of various RNA modifications remain largely unknown. To provide transcriptome-wide landscapes of RNA modifications, we developed the RMBase v2.0 (http://rna.sysu.edu.cn/rmbase/), which is a comprehensive database that integrates epitranscriptome sequencing data for the exploration of post-transcriptional modifications of RNAs and their relationships with miRNA binding events, disease-related single-nucleotide polymorphisms (SNPs) and RNA-binding proteins (RBPs). RMBase v2.0 was expanded with ∼600 datasets and ∼1 397 000 modification sites from 47 studies among 13 species, which represents an approximately 10-fold expansion when compared with the previous release. It contains ∼1 373 000 N6-methyladenosines (m6A), ∼5400 N1-methyladenosines (m1A), ∼9600 pseudouridine (Ψ) modifications, ∼1000 5-methylcytosine (m5C) modifications, ∼5100 2′-O-methylations (2′-O-Me), and ∼2800 modifications of other modification types. Moreover, we built a new module called ‘Motif’ that provides the visualized logos and position weight matrices (PWMs) of the modification motifs. We also constructed a novel module termed ‘modRBP’ to study the relationships between RNA modifications and RBPs. Additionally, we developed a novel web-based tool named ‘modMetagene’ to plot the metagenes of RNA modification along a transcript model. This database will help researchers investigate the potential functions and mechanisms of RNA modifications.

Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data.

Many disease-related single nucleotide polymorphisms (SNPs) have been inferred from genome-wide association studies (GWAS) in recent years. Numerous studies have shown that some SNPs located in protein-coding regions are associated with numerous diseases by affecting gene expression. However, in noncoding regions, the mechanism of how SNPs contribute to disease susceptibility remains unclear. Enhancer elements are functional segments of DNA located in noncoding regions that play an important role in regulating gene expression. The SNPs located in enhancer elements may affect gene expression and lead to disease. We presented a method for identifying liver cancer-related enhancer SNPs through integrating GWAS and histone modification ChIP-seq data. We identified 22 liver cancer-related enhancer SNPs, 9 of which were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer.

Discovery of Protein–lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets

Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein–lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP–lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP–lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.

Genome-wide association study combined with biological context can reveal more disease-related SNPs altering microRNA target seed sites

BackgroundEmerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3′UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs.ResultsIn 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10−5, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively).ConclusionsMRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-669) contains supplementary material, which is available to authorized users.

Is Low Back Pain a Genetic Problem?

Twin studies indicate that the (1) heritability of low back pain (LBP) is considerable varying between 30 and 46% depending on the setting, and (2) disc degeneration (DD) has a strong mediating effect in LBP with 11 to 13% of shared genetic effects. Until now, genetic research in LBP has focused on candidate genes; disease-related single-nucleotide polymorphisms have been found in structural, immune system, and nociception-related proteins. So far, no genome-wide association studies (GWAS) have been published in LBP. GWAS data will undoubtedly increase our knowledge on genetics of LBP. However, GWAS is able to detect only common variants (&gt; 5% MAF) and it is reasonable to expect that rare variants, obtained by whole-genome sequencing, have a role in LBP as well as in other complex disorders. Environmental factors have a significant role in LBP. Epigenetics describes heritable information not encoded in DNA. Different epigenetic mechanisms include such as methylation of cytosines in DNA, posttranslational modifications (mainly acetylation) of histones and microRNAs. Epigenetic regulation has an important role in complex disorders and preliminary evidence indicates that epigenetic mechanisms are important in DD as well. The future of LBP genetics requires first an unambiguous phenotype (mere subjective experience of pain is not enough). Discovery of objective endophenotypes of LBP may clarify the phenotype issue. Second, we need to establish large consortiums with genetic and epigenetic sample collection and systematic assessment of environmental (includes also psychological factors) determinants. Methodologically, bioinformatics will be most likely the rate-limiting step—at least in the beginning. Genetic analyses will most likely shift from common-to-rare variants—covering more extensively also the nonprotein coding area. However, in time, genetic research in LBP will evolve and become more commonplace. Disclosure of Interest None declared References Battié MC, Videman T, Levalahti E, Gill K, Kaprio J. Heritability of low back pain and the role of disc degeneration. Pain 2007;131(3):272–280 Denk F, McMahon SB, Tracey I. Pain vulnerability: a neurobiological perspective. Nat Neurosci 2014;17(2):192–200 Diatchenko L, Fillingim RB, Smith SB, Maixner W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat Rev Rheumatol 2013;9(6):340–350 J Eskola P, Männikkö M, Samartzis D, Karppinen J. Genome-wide association studies of lumbar disc degeneration-are we there yet? Spine J 2014;14(3):479–482 Foulkes T, Wood JN. Pain genes. PLoS Genet 2008;4(7):e1IN1.02086 Livshits G, Popham M, Malkin I, et al. Lumbar disc degeneration and genetic factors are the main risk factors for low back pain in women: the UK Twin Spine Study. Ann Rheum Dis 2011;70(10):1740–1745 Mogil JS. Pain genetics: past, present and future. Trends Genet 2012;28(6):258–266 Näkki A, Battié MC, Kaprio J. Genetics of disc-related disorders: current findings and lessons from other complex diseases. Eur Spine J 2013; July 10 (Epub ahead of print)