Disease-associated Single Nucleotide Polymorphisms Research Articles

The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.

The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P < 5E-08) as the four P values resulted from the tested methods for any SNP were all below or all above 5E-08. Nevertheless, larger discrepancies were detected between logistic regression and LMMs-based models at suggestive level of significance (i.e., of 5E-08 ≤ P < 5E-06). The SNP effects estimated by the logistic regression models were not statistically different from those estimated by GLMMs that implemented Wald's test. However, several SNP effects were significantly different from their counterparts in LMMs analyses. We suggest that fitting GLMMs with Wald's test on a pre-selected subset of SNPs obtained from logistic regression models can ensure the balance between the speed of analyses and the accuracy of parameters.

Association analysis of interleukin-23 receptor SNPs and SAPHO syndrome in Chinese people.

SAPHO syndrome (synovitis, acne, pustulosis, hyperostosis, and osteitis) is an autoimmune disease of unknown etiology that seriously affects patients' daily lives. Family-based investigations support genetic contributions toward disease susceptibility. The present study evaluated whether the previously reported autoimmune disease-associated single nucleotide polymorphisms (SNPs) have any genetic overlap with SAPHO syndrome. Genomic DNA was obtained from 71 SAPHO patients and 104 healthy controls. The SNP genotypes of each patient were determined with polymerase chain reaction and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Genotype, allele, and haplotype frequencies were analyzed with SPSS software. Three SNP sites (rs10889677 and rs2201841 of interleukin [IL]-23R, and rs2243248 of IL-4) showed significant correlation with the occurrence of SAPHO syndrome in additive and dominant genetic models, while rs7517847 of IL-23R showed substantial correlation with SAPHO in the dominant genetic model. The G allele of rs2243248 (IL-4) was a high risk factor for SAPHO (P=2.41e-5, odds ratio [OR] =7.79, 95% CI: 2.59-23.3). The haplotype (A-G-C-G-T), comprising 5 SNPs of the IL-23R gene, had a significantly higher frequency in the SAPHO cohort than in the controls (P=.011, OR=2.05, 95% CI: 1.12-3.60). Variants rs10889677, rs2201841, and rs7517847 of IL-23R, and variant rs2243248 of IL-4, showed strong associations with SAPHO syndrome. Patients carrying the A-G-C-G-T haplotype of IL-23 are significantly more likely to develop SAPHO syndrome.

Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. Code can be downloaded from https://github.com/Zoesgithub/MtBNN. Supplementary data are available at Bioinformatics online.

Is Dupuytren's disease really a 'disease of the Vikings'?

It has been suggested that Dupuytren's disease has an origin in Norse countries. We harnessed data from a genome-wide association study of Dupuytren's disease and the People of the British Isles study to determine evidence for a Norse origin of Dupuytren's disease. We computed Wright's Fixation Index between Orkney (Norse ancestry), Wales (ancient British) and South East England (Anglo-Saxons); compared mean Wright's Fixation Index in cases versus controls; used permutation to determine any excess of Norse inheritance in disease associated variants; constructed a genetic risk score for Dupuytren's disease and applied this to the People of the British Isles dataset to look for systematic differences between counties with known high and low levels of Norse ancestry. Finally, chromosome painting was used to see whether Dupuytren's disease-associated single nucleotide polymorphisms are geographically structured. In all analyses, no evidence was found for an excess of Norse ancestry in Dupuytren's disease. We conclude that there is no genetic evidence for a 'Viking origin of Dupuytren's disease'.

Genome-wide Analyses of Chromatin State in Human Mast Cells Reveal Molecular Drivers and Mediators of Allergic and Inflammatory Diseases

Mast cells (MCs) are versatile immune cells capable of rapidly responding to a diverse range of extracellular cues. Here, we mapped the genomic and transcriptomic changes in human MCs upon diverse stimuli. Our analyses revealed broad H3K4me3 domains and enhancers associated with activation. Notably, the rise of intracellular calcium concentration upon immunoglobulin E (IgE)-mediated crosslinking of the high-affinity IgE receptor (FcεRI) resulted in genome-wide reorganization of the chromatin landscape and was associated with a specific chromatin signature, which we term Ca2+-dependent open chromatin (COC) domains. Examination of differentially expressed genes revealed potential effectors of MC function, and we provide evidence for fibrinogen-like protein 2 (FGL2) as an MC mediator with potential relevance in chronic spontaneous urticaria. Disease-associated single-nucleotide polymorphisms mapped onto cis-regulatory regions of human MCs suggest that MC function may impact a broad range of pathologies. The datasets presented here constitute a resource for the further study of MC function.

Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies

Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study.

Enhancer variants associated with Alzheimer\u2019s disease affect gene expression via chromatin looping

BackgroundGenome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops.MethodsTo characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments.ResultsWe report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes.ConclusionOur results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

Predicting the effects of SNPs on transcription factor binding affinity.

Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary data are available at Bioinformatics online.

Analysis of genetically driven alternative splicing identifies FBXO38 as a novel COPD susceptibility gene.

While many disease-associated single nucleotide polymorphisms (SNPs) are associated with gene expression (expression quantitative trait loci, eQTLs), a large proportion of complex disease genome-wide association study (GWAS) variants are of unknown function. Some of these SNPs may contribute to disease by regulating gene splicing. Here, we investigate whether SNPs that are associated with alternative splicing (splice QTL or sQTL) can identify novel functions for existing GWAS variants or suggest new associated variants in chronic obstructive pulmonary disease (COPD). RNA sequencing was performed on whole blood from 376 subjects from the COPDGene Study. Using linear models, we identified 561,060 unique sQTL SNPs associated with 30,333 splice sites corresponding to 6,419 unique genes. Similarly, 708,928 unique eQTL SNPs involving 15,913 genes were detected at 10% FDR. While there is overlap between sQTLs and eQTLs, 55.3% of sQTLs are not eQTLs. Co-localization analysis revealed that 7 out of 21 loci associated with COPD (p<1x10−6) in a published GWAS have at least one shared causal variant between the GWAS and sQTL studies. Among the genes identified to have splice sites associated with top GWAS SNPs was FBXO38, in which a novel exon was discovered to be protective against COPD. Importantly, the sQTL in this locus was validated by qPCR in both blood and lung tissue, demonstrating that splice variants relevant to lung tissue can be identified in blood. Other identified genes included CDK11A and SULT1A2. Overall, these data indicate that analysis of alternative splicing can provide novel insights into disease mechanisms. In particular, we demonstrated that SNPs in a known COPD GWAS locus on chromosome 5q32 influence alternative splicing in the gene FBXO38.

HIGH-THROUGHPUT IDENTIFICATION OF NONCODING FUNCTIONAL SNPS

Genome wide association studies (GWAS) have provided significant insight into complex diseases by identifying numerous disease-associated non-coding single nucleotide polymorphisms (SNPs). However, GWAS cannot distinguish functional SNPs (fSNPs) from other disease-associated SNPs that are in linkage disequilibrium (LD). To address this challenge, we developed a novel approach to bridge the gap between GWAS and biological mechanisms with a set of high throughput methodologies. We use SNP-seq (Single Nucleotide Polymorphism-Sequencing) and Reel-seq (Regulatory Element-Sequencing) to identify disease-associated fSNPs and FREP-MS (Flanking Restriction Enhanced DNA Pulldown-Mass Spectrometry) to characterize these fSNPs by identifying regulatory proteins that specifically bind to the fSNPs. To prove the feasibility, we applied this approach to a couple of disease-associated loci. For this study, we applied the same approach to a library that contains SNPs in LD on 27 loci that are associated with late onset Alzheimer's disease (LOAD). We confirmed 27 fSNPs on 12 LOAD risk loci by allele-imbalanced gel shifting including 5 on the HLA-DQA/ HLA DRB1 locus. We identified multiple proteins that specifically bind to each of these fSNPs. The regulation of the risk gene expression on these 12 loci will be explored.

A SNP of lncRNA gives HIV-1 a boost.

Although the molecular basis of most disease-associated single-nucleotide polymorphisms has remained elusive, an HIV-1 viral load–associated polymorphism (rs1015164) has been identified that marks expression of a long non-coding RNA that regulates the co-receptor CCR5 and thereby influences infection of CD4+ T cells.

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach.

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied.Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians.Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations.Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions.Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank.

The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.

The Transcription Factor ERG Regulates Super-Enhancers Associated With an Endothelial-Specific Gene Expression Program.

The ETS (E-26 transformation-specific) transcription factor ERG (ETS-related gene) is essential for endothelial homeostasis, driving expression of lineage genes and repressing proinflammatory genes. Loss of ERG expression is associated with diseases including atherosclerosis. ERG's homeostatic function is lineage-specific, because aberrant ERG expression in cancer is oncogenic. The molecular basis for ERG lineage-specific activity is unknown. Transcriptional regulation of lineage specificity is linked to enhancer clusters (super-enhancers). To investigate whether ERG regulates endothelial-specific gene expression via super-enhancers. Chromatin immunoprecipitation with high-throughput sequencing in human umbilical vein endothelial cells showed that ERG binds 93% of super-enhancers ranked according to H3K27ac, a mark of active chromatin. These were associated with endothelial genes such as DLL4 (Delta-like protein 4), CLDN5 (claudin-5), VWF (von Willebrand factor), and CDH5 (VE-cadherin). Comparison between human umbilical vein endothelial cell and prostate cancer TMPRSS2 (transmembrane protease, serine-2):ERG fusion-positive human prostate epithelial cancer cell line (VCaP) cells revealed distinctive lineage-specific transcriptome and super-enhancer profiles. At a subset of endothelial super-enhancers (including DLL4 and CLDN5), loss of ERG results in significant reduction in gene expression which correlates with decreased enrichment of H3K27ac and MED (Mediator complex subunit)-1, and reduced recruitment of acetyltransferase p300. At these super-enhancers, co-occupancy of GATA2 (GATA-binding protein 2) and AP-1 (activator protein 1) is significantly lower compared with super-enhancers that remained constant following ERG inhibition. These data suggest distinct mechanisms of super-enhancer regulation in endothelial cells and highlight the unique role of ERG in controlling a core subset of super-enhancers. Most disease-associated single nucleotide polymorphisms from genome-wide association studies lie within noncoding regions and perturb transcription factor recognition sequences in relevant cell types. Analysis of genome-wide association studies data shows significant enrichment of risk variants for cardiovascular disease and other diseases, at ERG endothelial enhancers and super-enhancers. The transcription factor ERG promotes endothelial homeostasis via regulation of lineage-specific enhancers and super-enhancers. Enrichment of cardiovascular disease-associated single nucleotide polymorphisms at ERG super-enhancers suggests that ERG-dependent transcription modulates disease risk.

A new class of constitutively active super-enhancers is associated with fast recovery of 3D chromatin loops

BackgroundSuper-enhancers or stretch enhancers are clusters of active enhancers that often coordinate cell-type specific gene regulation during development and differentiation. In addition, the enrichment of disease-associated single nucleotide polymorphism in super-enhancers indicates their critical function in disease-specific gene regulation. However, little is known about the function of super-enhancers beyond gene regulation.ResultsIn this study, through a comprehensive analysis of super-enhancers in 30 human cell/tissue types, we identified a new class of super-enhancers which are constitutively active across most cell/tissue types. These ‘common’ super-enhancers are associated with universally highly expressed genes in contrast to the canonical definition of super-enhancers that assert cell-type specific gene regulation. In addition, the genome sequence of these super-enhancers is highly conserved by evolution and among humans, advocating their universal function in genome regulation. Integrative analysis of 3D chromatin loops demonstrates that, in comparison to the cell-type specific super-enhancers, the cell-type common super-enhancers present a striking association with rapidly recovering loops.ConclusionsIn this study, we propose that a new class of super-enhancers may play an important role in the early establishment of 3D chromatin structure.

A Pipeline to Assess Disease-Associated Haplotypes in Repeat Expansion Disorders: The Example of MJD/SCA3 Locus.

At least 40 human diseases are associated with repeat expansions; yet, the mutational origin and instability mechanisms remain unknown for most of them. Previously, genetic epidemiology and predisposing backgrounds for the instability of some expanding loci have been studied in different populations through the analysis of diversity flanking the respective pathogenic repeats. Here, we aimed at developing a pipeline to assess disease-associated haplotypes at oligonucleotide repeat loci, combining analysis of single nucleotide polymorphisms (SNPs) and short tandem repeats (STRs). Machado-Joseph disease (MJD/SCA3), the most frequent dominant ataxia worldwide, was used as an example of a detailed procedure. Thus, to identify genetic backgrounds that segregate with expanded/mutated alleles in MJD, we selected a set of 26 SNPs and 7 STRs flanking the causative CAG repeat. Key criteria and steps for this selection are described, and included (1) haplotype blocks minimizing the occurrence of recombination (for SNPs); and (2) match scores to increase potential for polymorphic information content of repetitive sequences found in Tandem Repeats Finder (for STRs). To directly assess SNP haplotypes in phase with MJD expansions, we optimized a strategy with preferential amplification of normal over expanded alleles, in addition to SNP allele-specific amplifications; this allowed the identification of disease-associated SNP haplotypes, even when only the proband is available in a given family. To infer STR haplotypes, we optimized a multiplex PCR, including 7 STRs plus the MJD_CAG repeat, followed by analysis of segregation or the use of the PHASE software. This protocol is a ready-to-use tool to assess MJD haplotypes in different populations. The pipeline designed can be used to assess disease-associated haplotypes in other repeat-expansion diseases. This should be of great utility to study (1) genetic epidemiology (population-of-origin, age and spreading routes of mutations) and (2) mechanisms responsible for de novo expansions, in these neurological diseases; (3) to detect predisposing haplotypes and (4) phenotype modifiers; (5) to help solving cases of apparent homoallelism (two same-size normal alleles) in diagnosis; and (6) to identify the best targets for the development of allele-specific therapies in ethnically diverse patient populations.

Addressing the Missing Heritability Problem With the Help of Regulatory Features.

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions.

Multiple sclerosis (MS) is the most common neurological disorder in young adults. Despite extensive studies, only a fraction of MS heritability has been explained, with association studies focusing primarily on protein-coding genes, essentially for the difficulty of interpreting non-coding features. However, non-coding RNAs (ncRNAs) and functional elements, such as super-enhancers (SE), are crucial regulators of many pathways and cellular mechanisms, and they have been implicated in a growing number of diseases. In this work, we searched for possible enrichments in non-coding elements at MS genome-wide associated loci, with the aim to highlight their possible involvement in the susceptibility to the disease. We first reconstructed the linkage disequilibrium (LD) structure of the Italian population using data of 727,478 single-nucleotide polymorphisms (SNPs) from 1,668 healthy individuals. The genomic coordinates of the obtained LD blocks were intersected with those of the top hits identified in previously published MS genome-wide association studies (GWAS). By a bootstrapping approach, we hence demonstrated a striking enrichment of non-coding elements, especially of circular RNAs (circRNAs) mapping in the 73 LD blocks harboring MS-associated SNPs. In particular, we found a total of 482 circRNAs (annotated in publicly available databases) vs. a mean of 194 ± 65 in the random sets of LD blocks, using 1,000 iterations. As a proof of concept of a possible functional relevance of this observation, we experimentally verified that the expression levels of a circRNA derived from an MS-associated locus, i.e., hsa_circ_0043813 from the STAT3 gene, can be modulated by the three genotypes at the disease-associated SNP. Finally, by evaluating RNA-seq data of two cell lines, SH-SY5Y and Jurkat cells, representing tissues relevant for MS, we identified 18 (two novel) circRNAs derived from MS-associated genes. In conclusion, this work showed for the first time that MS-GWAS top hits map in LD blocks enriched in circRNAs, suggesting circRNAs as possible novel contributors to the disease pathogenesis.

Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks.

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.

Landscape of SNPs-mediated lncRNA structural variations and their implication in human complex diseases.

An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains. An accurate alignment algorithm was performed to identify 40 triple pairs between single-nucleotide polymorphisms (SNPs), lncRNAs and diseases. In order to detect the potential contribution of a lncRNA's modular character, we estimated and evaluated structural rearrangements, which were derived from disease-associated SNPs. In addition, we focused on annotating and comparing the global and local heterogeneity of the wild-type and mutant lncRNAs. Assessing lncRNA architecture has yielded how variations in structured regions impact the molecular mechanisms of lncRNAs and how SNPs disturb binding and recruiting ability. These observations are the first glimpse of the 'lncRNA structurome' and make it possible to robustly explore and assemble intricate space conformation and their stress variation. This result also successfully demonstrates that lncRNA transcripts contain a complex structural landscape and highlights the proposed contribution of lncRNA structure in controlling RNA functions and disease mechanisms.