Explaining the disease phenotype of intergenic SNP through predicted long range regulation

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

Molecular landscape of the interleukin-40 encoding gene, C17orf99, in patients with acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant hematological disorder in which aberrant cytokine signaling and inflammation play a role in disease initiation and progression. Interleukin-40 (IL-40) is a novel cytokine encoded by the chromosome 17 open reading frame 99 (C17orf99) gene. This cytokine is involved in mediating inflammation but its biological significance in the pathogenesis of AML has not been investigated. In this case-control and observational study, mRNA expression and DNA methylation of the C17orf99 gene were evaluated in the peripheral blood of AML patients. In addition, the polymorphism of two novel intergenic variants of the C17orf99 gene, rs2004339 A/G and rs2310998 G/A, were explored using a real-time polymerase chain reaction assay. The study was conducted on 131 patients with AML and 106 controls and gene expression and DNA methylation were expressed as fold-change (2-ΔΔCt). Results revealed that mRNA expression of the C17orf99 gene was down-regulated in AML patients, particularly in females, while up-regulated expression was found in patients with hypoalbuminemia. For DNA methylation, it was up-regulated in AML patients, particularly in females, AML M5 subtype, and CD4-negative and CD14-positive peripheral blood cells. The mutant A allele and the corresponding homozygous AA genotype of rs2004339 was significantly associated with an increased risk of AML. The AA genotype was also associated with significantly up-regulated C17orf99 mRNA expression and DNA methylation of compared to the wild-type GG genotype. In conclusions, C17orf99 mRNA expression showed down-regulated levels in the peripheral blood of AML patients, while DNA methylation was up-regulated. The intergenic variant rs2004339 was associated with susceptibility to AML and had an effect on mRNA expression and DNA methylation.

Unraveling the Link between Periodontitis and Inflammatory Bowel Disease: Challenges and Outlook

Periodontitis and Inflammatory Bowel Disease (IBD) are chronic inflammatory conditions, characterized by microbial dysbiosis and hyper-immunoinflammatory responses. Growing evidence suggest an interconnection between periodontitis and IBD, implying a shift from the traditional concept of independent diseases to a complex, reciprocal cycle. This review outlines the evidence supporting an "Oral-Gut" axis, marked by a higher prevalence of periodontitis in IBD patients and vice versa. The specific mechanisms linking periodontitis and IBD remain to be fully elucidated, but emerging evidence points to the ectopic colonization of the gut by oral bacteria, which promote intestinal inflammation by activating host immune responses. This review presents an in-depth examination of the interconnection between periodontitis and IBD, highlighting the shared microbiological and immunological pathways, and proposing a "multi-hit" hypothesis in the pathogenesis of periodontitis-mediated intestinal inflammation. Furthermore, the review underscores the critical need for a collaborative approach between dentists and gastroenterologists to provide holistic oral-systemic healthcare.

Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

Disrupted long-range gene regulations elucidate shared tissue-specific mechanisms of neuropsychiatric disorders

Neurological and psychiatric disorders have overlapped phenotypic profiles, but the underlying tissue-specific functional processes remain largely unknown. In this study, we explore the shared tissue-specificity among 14 neuropsychiatric disorders through the disrupted long-range gene regulations by GWAS-identified regulatory SNPs. Through Hi-C interactions, averagely 38.0% and 17.2% of the intergenic regulatory SNPs can be linked to target protein-coding genes in brain and non-brain tissues, respectively. Interestingly, while the regulatory target genes in the brain tend to enrich in nervous system development related processes, those in the non-brain tissues are inclined to interfere with synapse and neuroinflammation related processes. Compared to psychiatric disorders, neurological disorders present more prominently the neuroinflammatory processes in both brain and non-brain tissues, indicating an intrinsic difference in mechanisms. Through tissue-specific gene regulatory networks, we then constructed disorder similarity networks in two brain and three non-brain tissues, highlighting both known disorder clusters (e.g. the neurodevelopmental disorders) and unexpected disorder clusters (e.g. Parkinson's disease is consistently grouped with psychiatric disorders). We showcase the potential pharmaceutical applications of the small bowel and its disorder clusters, illustrated by the known drug targets NR1I3 and NFACT1, and their small bowel-specific regulatory modules. In conclusion, disrupted long-range gene regulations in both brain and non-brain tissues contribute to the similarity among distinct clusters of neuropsychiatric disorders, and the tissue-specifically shared functions and regulators for disease clusters may provide insights for future therapeutic investigations.

Capturing large genomic contexts for accurately predicting enhancer-promoter interactions

Enhancer-promoter interaction (EPI) is a key mechanism underlying gene regulation. EPI prediction has always been a challenging task because enhancers could regulate promoters of distant target genes. Although many machine learning models have been developed, they leverage only the features in enhancers and promoters, or simply add the average genomic signals in the regions between enhancers and promoters, without utilizing detailed features between or outside enhancers and promoters. Due to a lack of large-scale features, existing methods could achieve only moderate performance, especially for predicting EPIs in different cell types. Here, we present a Transformer-based model, TransEPI, for EPI prediction by capturing large genomic contexts. TransEPI was developed based on EPI datasets derived from Hi-C or ChIA-PET data in six cell lines. To avoid over-fitting, we evaluated the TransEPI model by testing it on independent test datasets where the cell line and chromosome are different from the training data. TransEPI not only achieved consistent performance across the cross-validation and test datasets from different cell types but also outperformed the state-of-the-art machine learning and deep learning models. In addition, we found that the improved performance of TransEPI was attributed to the integration of large genomic contexts. Lastly, TransEPI was extended to study the non-coding mutations associated with brain disorders or neural diseases, and we found that TransEPI was also useful for predicting the target genes of non-coding mutations.

Facilitating Antiviral Drug Discovery Using Genetic and Evolutionary Knowledge

Over the course of human history, billions of people worldwide have been infected by various viruses. Despite rapid progress in the development of biomedical techniques, it is still a significant challenge to find promising new antiviral targets and drugs. In the past, antiviral drugs mainly targeted viral proteins when they were used as part of treatment strategies. Since the virus mutation rate is much faster than that of the host, such drugs feature drug resistance and narrow-spectrum antiviral problems. Therefore, the targeting of host molecules has gradually become an important area of research for the development of antiviral drugs. In recent years, rapid advances in high-throughput sequencing techniques have enabled numerous genetic studies (such as genome-wide association studies (GWAS), clustered regularly interspersed short palindromic repeats (CRISPR) screening, etc.) for human diseases, providing valuable genetic and evolutionary resources. Furthermore, it has been revealed that successful drug targets exhibit similar genetic and evolutionary features, which are of great value in identifying promising drug targets and discovering new drugs. Considering these developments, in this article the authors propose a host-targeted antiviral drug discovery strategy based on knowledge of genetics and evolution. We first comprehensively summarized the genetic, subcellular location, and evolutionary features of the human genes that have been successfully used as antiviral targets. Next, the summarized features were used to screen novel druggable antiviral targets and to find potential antiviral drugs, in an attempt to promote the discovery of new antiviral drugs.

Intergenic SNPs in Obstructive Sleep Apnea Syndrome: Revealing Metabolic, Oxidative Stress and Immune-Related Pathways

There is strong evidence supporting the contribution of genetic factors to obstructive sleep apnea syndrome (OSAHS) susceptibility. In the current study we analyzed both in a clinical cohort and in silico, four single nucleotide polymorphisms SNPs, rs999944, rs75108997, rs35329661 and rs116133558 that have been associated with OSAHS. In 102 patients with OSAHS and 50 healthy volunteers, genetic testing of the above polymorphisms was performed. Polymorphism rs116133558 was invariant in our study population, whereas polymorphism rs35329661 was more than 95% invariant. Polymorphism rs999944 displayed significant (>5%) variance in our study population and was used in the binary logistic regression model. In silico analyses of the mechanism by which these three SNPs may affect the pathophysiology of OSAHS revealed a transcriptomic network of 274 genes. This network was involved in multiple cancer-associated gene signatures, as well as the adipogenesis pathway. This study, uncover a regulatory network in OSAHS using transcriptional targets of intergenic SNPs, and map their contributions in the pathophysiology of the syndrome on the interplay between adipocytokine signaling and cancer-related transcriptional dysregulation.

A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank

BACKGROUND

Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated.

METHODS

We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module.

RESULTS

In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities.

CONCLUSIONS

Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities ( https://multimorbidity.comp-sysbio.org ).

Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants

MOTIVATION

Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction.

RESULTS

By incorporating the Hi-C data, we propose a novel and powerful deep learning model of artificial intelligence to classify interacting and non-interacting fragment pairs and predict the functional effects of sequence alteration of single nucleotide on chromatin interaction and thus on gene expression. The changes in chromatin interaction probability between the reference sequence and the altered sequence reflect the degree of functional impact for the variant. The model was effective and efficient with the classification of interacting and non-interacting fragment pairs. The predicted causal SNPs that had a larger impact on chromatin interaction were more likely to be identified by GWAS and eQTL analyses. We demonstrate that an integrative approach combining artificial intelligence-deep learning with high throughput experimental evidence of chromatin interaction leads to prioritizing the functional variants in disease- and phenotype-related loci and thus will greatly expedite uncover of the biological mechanism underlying the association identified in genomic studies.

AVAILABILITY AND IMPLEMENTATION

Source code used in data preparing and model training is available at the GitHub website (https://github.com/biocai/DeepHiC).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic and expression variations of cell cycle pathway genes in brain tumor patients

The present study was designed to determine the association between the genetic polymorphisms/expression variations of RB1 and CCND1 genes and brain tumor risk. For this purpose, 250 blood samples of brain tumor patients along with 250 controls (cohort I) and 96 brain tumor tissues (cohort II) with adjacent control section were collected. Mutation analysis of RB1 (rs137853294, rs121913300) and CCND1 (rs614367, rs498136) genes was performed using ARMS-PCR followed by sequencing, and expression analysis was performed using real-time PCR and immunohistochemistry. The results showed homozygous mutant genotype of RB1 gene polymorphism, rs121913300 (P=0.003) and CCND1 gene polymorphism rs614367 (P=0.01) were associated significantly with brain tumor risk. Moreover, significant down-regulation of RB1 (P=0.005) and up-regulation of CCND1 (P=0.0001) gene was observed in brain tumor sections vs controls. Spearman correlation showed significant negative correlation between RB1 vs proliferation marker, Ki-67 (r = -0.291*, P<0.05) in brain tumors. Expression levels of selected genes were also assessed at protein level using immunohistochemical analysis (IHC) and signification down-regulation of RB1 (P=0.0001) and up-regulation of CCND1 (P=0.0001) was observed in brain tumor compared with control sections. In conclusion, it is suggested that polymorphisms/expression variations of RB1 and CCND1 genes may be associated with increased risk of brain tumor.

Chitinase 3-like-1, Tolloid-like protein 1, and intergenic gene polymorphisms are predictors for hepatocellular carcinoma development after hepatitis C virus eradication by direct-acting antivirals

Hepatocellular carcinoma (HCC) is a major cause of cancer death in Egypt. There is still a risk for HCC development even after eradicating hepatitis C virus (HCV) by direct-acting antivirals (DAAs). Chitinase-3-like-protein-1 (CHI3L1), a biomarker for predicting many diseases, plays an essential role in inflammation, angiogenesis, and antiapoptosis. Tolloid-like protein 1 (TLL1) may be involved in hepatic fibrogenesis and carcinogenesis. This study aimed to determine the role and combined effect of CHI3L1 (rs880633), TLL1 (rs1503298), and an intergenic (rs597533) polymorphisms on the risk of developing HCC in Egyptian patients after achieving sustained virological response (SVR) by DAAs. Blood samples were collected from 68 HCC patients, 77 non-HCC subjects, and 80 healthy controls. The DNA was extracted and analyzed for rs880633, rs1503298, and rs597533 using Genotyping TaqMan™ assay. The result of the present study showed a significant difference in genotypes and alleles frequencies in both (rs880633) and (rs597533) in HCC group as compared to healthy control and also as compared to the non-HCC group. However, regarding to (rs1503298) genotypes and alleles between the HCC and non-HCC groups, there were no significant differences. Combined polymorphism in more than one gene simultaneously showed a higher risk to HCC after SVR than an individual locus. Both allelic and genotypic variations of the CHI3L1 gene (rs880633) and an intergenic (rs597533) seemed to be significant predictors confirming a great risk for HCC susceptibility in Egyptian patients achieved SVR. Patients with a polymorphism in more than one gene showed an increased risk to HCC after SVR rather than individual locus.

The Use of Whole Genome and Exome Sequencing for Newborn Screening: Challenges and Opportunities for Population Health

Newborn screening (NBS) is a population-based program with a goal of reducing the burden of disease for conditions with significant clinical impact on neonates. Screening tests were originally developed and implemented one at a time, but newer methods have allowed the use of multiplex technologies to expand additions more rapidly to standard panels. Recent improvements in next-generation sequencing are also evolving rapidly from first focusing on individual genes, then panels, and finally all genes as encompassed by whole exome and genome sequencing. The intersection of these two technologies brings the revolutionary possibility of identifying all genetic disorders in newborns, allowing implementation of therapies at the optimum time regardless of symptoms. This article reviews the history of newborn screening and early studies examining the use of whole genome and exome sequencing as a screening tool. Lessons learned from these studies are discussed, along with technical, ethical, and societal challenges to broad implementation.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MOTIVATION

Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.

RESULTS

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Genome-Wide Association Study and Fine Mapping Reveals Candidate Genes for Birth Weight of Yorkshire and Landrace Pigs

Birth weight of pigs is an important economic factor in the livestock industry. The identification of the genes and variants that underlie birth weight is of great importance. In this study, we integrated two genotyping methods, single nucleotide polymorphism (SNP) chip analysis and restriction site associated DNA sequencing (RAD-seq) to genotype genome-wide SNPs. In total, 45,175 and 139,634 SNPs were detected with the SNP chip and RAD-seq, respectively. The genome-wide association study (GWAS) of the combined SNP panels identified two significant loci located at chr1: 97,745,041 and chr4: 112,031,589, that explained 6.36% and 4.25% of the phenotypic variance respectively. To reduce interval containing causal variants, we imputed sequence-level SNPs in the GWAS identified regions and fine-mapped the causative variants into two narrower genomic intervals: a ∼100 kb interval containing 71 SNPs and a broader ∼870 kb interval with 432 SNPs. This fine-mapping highlighted four promising candidate genes, SKOR2, SMAD2, VAV3, and NTNG1. Additionally, the functional genes, SLC25A24, PRMT6 and STXBP3, are also located near the fine-mapping region. These results suggest that these candidate genes may have contribute substantially to the birth weight of pigs.

HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy

Epilepsy is a common neurological disorder that affects mammalian species including human beings and dogs. In order to discover novel drugs for the treatment of canine epilepsy, multiomics data were analyzed to identify epilepsy associated genes. In this research, the original ranking of associated genes was obtained by two high-throughput bioinformatics experiments: Genome Wide Association Study (GWAS) and microarray analysis. The association ranking of genes was enhanced by a re-ranking system, HPO-Shuffle, which integrated information from GWAS, microarray analysis and Human Phenotype Ontology database for further downstream analysis. After applying HPO-Shuffle, the association ranking of epilepsy genes were improved. Afterward, a weighted gene coexpression network analysis (WGCNA) led to a set of gene modules, which hinted a clear relevance between the high ranked genes and the target disease. Finally, WGCNA and connectivity map (CMap) analysis were performed on the integrated dataset to discover a potential drug list, in which a well-known anticonvulsant phensuximide was included.

Genetic determinants of gut microbiota composition and bile acid profiles in mice

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions.

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

Impact of Long Non-Coding RNA HOTAIR Genetic Variants on the Susceptibility and Clinicopathologic Characteristics of Patients with Urothelial Cell Carcinoma

Increasing evidence shows that dysregulated expression of long non-coding (lnc)RNAs can serve as diagnostic or prognostic markers in urothelial cell carcinoma (UCC), the most common pathological type of bladder cancer. lncRNA HOX transcript antisense RNA (HOTAIR) was shown to promote tumor progression and be associated with a poor prognosis in multiple cancers including bladder cancer. Polymorphisms of HOTAIR were recently linked to a predisposition for diverse malignancies. Herein we conducted a case-control study to evaluate whether genetic polymorphisms of HOTAIR were associated with UCC risk and clinicopathologic characteristics. Four loci (rs920778 T>C, rs1899663 G>T, rs4759314 A>G, and rs12427129, C>T) of HOTAIR were genotyped by a TaqMan allelic discrimination method in 431 cases and 862 controls. We found that female patients who carried AG + GG genotype of rs4759314 were associated with an increased UCC risk after controlling for age and tobacco consumption (adjusted odds ratio (AOR) = 1.92, 95% confidence interval (CI): 1.01–3.64, p = 0.047) and a lower overall survival rate (p = 0.008). Moreover, patients with a smoking habit or younger age (≤65 years), who had at least one T allele of HOTAIR rs12427129 were at a higher risk of developing advance tumor T satge (p = 0.046), compared to those patients with CC homozygotes. In contrast, rs920778 C allele carriers were negatively correlated with the development of lymph node metastasis (OR = 0.51, 95% CI: 0.28–0.94, p = 0.031). Further analyses of clinical datasets revealed correlations of the expression of HOTAIR with tumor metastasis and a poor survival rate in patients with UCC. Our results verified the diverse impacts of HOTAIR variants on UCC susceptibility and clinicopathologic characteristics.

Networking in Biology: The Hybrid Rat Diversity Panel

One of the most fruitful resources for systems genetic studies of nonhuman mammals is a panel of inbred strains that exhibits significant genetic diversity between strains but genetic stability (isogenicity) within strains. These characteristics allow for fine mapping of complex phenotypes (QTLs) and provide statistical power to identify loci which contribute nominally to the phenotype. This type of resource also allows the planning and performance of investigations using the same genetic backgrounds over several generations of the test animals. Often, rats are preferred over mice for physiologic and behavioral studies because of their larger size and more distinguishable anatomy (particularly for their central nervous system). The Hybrid Rat Diversity Panel (HRDP) is a panel of inbred rat strains, which combines two recombinant inbred panels (the HXB/BXH, 30 strains; the LEXF/FXLE, 34 strains and 35 more strains of inbred rats which were selected for genetic diversity, based on their fully sequenced genomes and/or thorough genotyping). The genetic diversity and statistical power of this panel for mapping studies rivals or surpasses currently available panels in mouse. The genetic stability of this panel makes it particularly suitable for collection of high-throughput omics data as relevant technology becomes available for engaging in truly integrative systems biology. The PhenoGen website ( http://phenogen.org ) is the repository for the initial transcriptome data, making the raw data, the processed data, and the analysis results, e.g., organ-specific protein coding and noncoding transcripts, isoform analysis, expression quantitative trait loci, and co-expression networks, available to the research public. The data sets and tools being developed will complement current efforts to analyze the human transcriptome and its genetic controls (the Genotype-Tissue Expression Project (GTEx)) and allow for dissection of genetic networks that predispose to particular phenotypes and gene-by-environment interactions that are difficult or even impossible to study in humans. The HRDP is an essential population for exploring truly integrative systems genetics.

The genetic architecture of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells. There is a strong genetic risk to T1D; with genome-wide association studies (GWAS) identifying over 60 susceptibility regions within the human genome which are marked by single nucleotide polymorphisms (SNPs). Here, we review what is currently known about the genetics of T1D. We argue that advancing our understanding of the aetiology and pathogenesis of T1D will require the integration of genome biology (omics-data) with GWAS data, thereby making it possible to elucidate the putative gene regulatory networks modulated by disease-associated SNPs. This approach has a potential to revolutionize clinical management of T1D in an era of precision medicine.

Cancer-specific expression quantitative loci are affected by expression dysregulation

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

The rs13388259 Intergenic Polymorphism in the Genomic Context of the BCYRN1 Gene Is Associated with Parkinson's Disease in the Hungarian Population

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by bradykinesia, resting tremor, and muscle rigidity. To date, approximately 50 genes have been implicated in PD pathogenesis, including both Mendelian genes with rare mutations and low-penetrance genes with common polymorphisms. Previous studies of low-penetrance genes focused on protein-coding genes, and less attention was given to long noncoding RNAs (lncRNAs). In this study, we aimed to investigate the susceptibility roles of lncRNA gene polymorphisms in the development of PD. Therefore, polymorphisms (n=15) of the PINK1-AS, UCHL1-AS, BCYRN1, SOX2-OT, ANRIL and HAR1A lncRNAs genes were genotyped in Hungarian PD patients (n=160) and age- and sex-matched controls (n=167). The rare allele of the rs13388259 intergenic polymorphism, located downstream of the BCYRN1 gene, was significantly more frequent among PD patients than control individuals (OR = 2.31; p=0.0015). In silico prediction suggested that this polymorphism is located in a noncoding region close to the binding site of the transcription factor HNF4A, which is a central regulatory hub gene that has been shown to be upregulated in the peripheral blood of PD patients. The rs13388259 polymorphism may interfere with the binding affinity of transcription factor HNF4A, potentially resulting in abnormal expression of target genes, such as BCYRN1.

Genome organization: connecting the developmental origins of disease and genetic variation

An adverse early life environment can increase the risk of metabolic and other disorders later in life. Genetic variation can modify an individual's susceptibility to these environmental challenges. These gene by environment interactions are important, but difficult, to dissect. The nucleus is the primary organelle where environmental responses impact directly on the genetic variants within the genome, resulting in changes to the biology of the genome and ultimately the phenotype. Understanding genome biology requires the integration of the linear DNA sequence, epigenetic modifications and nuclear proteins that are present within the nucleus. The interactions between these layers of information may be captured in the emergent spatial genome organization. As such genome organization represents a key research area for decoding the role of genetic variation in the Developmental Origins of Health and Disease.

Modulation of transcription factor binding and epigenetic regulation of the MLH1 CpG island and shore by polymorphism rs1800734 in colorectal cancer

The MLH1 promoter polymorphism rs1800734 is associated with MLH1 CpG island hypermethylation and expression loss in colorectal cancer (CRC). Conversely, variant rs1800734 is associated with MLH1 shore, but not island, hypomethylation in peripheral blood mononuclear cell DNA. To explore these distinct patterns, MLH1 CpG island and shore methylation was assessed in CRC cell lines stratified by rs1800734 genotype. Cell lines containing the variant A allele demonstrated MLH1 shore hypomethylation compared to wild type (GG). There was significant enrichment of transcription factor AP4 at the MLH1 promoter in GG and GA cell lines, but not the AA cell line, by chromatin immunoprecipitation studies. Preferential binding to the G allele was confirmed by sequencing in the GA cell line. The enhancer-associated histone modification H3K4me1 was enriched at the MLH1 shore; however, H3K27ac was not, indicating the shore is an inactive enhancer. These results demonstrate the role of variant rs1800734 in altering transcription factor binding as well as epigenetics at regions beyond the MLH1 CpG island in which it is located.

The dynamic DNA methylation landscape of the mutL homolog 1 shore is altered by MLH1-93G>A polymorphism in normal tissues and colorectal cancer

BACKGROUND

Colorectal cancers (CRCs) undergo distinct genetic and epigenetic alterations. Expression of mutL homolog 1 (MLH1), a mismatch repair gene that corrects DNA replication errors, is lost in up to 15% of sporadic tumours due to mutation or, more commonly, due to DNA methylation of its promoter CpG island. A single nucleotide polymorphism (SNP) in the CpG island of MLH1 (MLH1-93G>A or rs1800734) is associated with CpG island hypermethylation and decreased MLH1 expression in CRC tumours. Further, in peripheral blood mononuclear cell (PBMC) DNA of both CRC cases and non-cancer controls, the variant allele of rs1800734 is associated with hypomethylation at the MLH1 shore, a region upstream of its CpG island that is less dense in CpG sites.

RESULTS

To determine whether this genotype-epigenotype association is present in other tissue types, including colorectal tumours, we assessed DNA methylation in matched normal colorectal tissue, tumour, and PBMC DNA from 349 population-based CRC cases recruited from the Ontario Familial Colorectal Cancer Registry. Using the semi-quantitative real-time PCR-based MethyLight assay, MLH1 shore methylation was significantly higher in tumour tissue than normal colon or PBMCs (P < 0.01). When shore methylation levels were stratified by SNP genotype, normal colorectal DNA and PBMC DNA were significantly hypomethylated in association with variant SNP genotype (P < 0.05). However, this association was lost in tumour DNA. Among distinct stages of CRC, metastatic stage IV CRC tumours incurred significant hypomethylation compared to stage I-III cases, irrespective of genotype status. Shore methylation of MLH1 was not associated with MSI status or promoter CpG island hypermethylation, regardless of genotype. To confirm these results, bisulfite sequencing was performed in matched tumour and normal colorectal specimens from six CRC cases, including two cases per genotype (wildtype, heterozygous, and homozygous variant). Bisulfite sequencing results corroborated the methylation patterns found by MethyLight, with significant hypomethylation in normal colorectal tissue of variant SNP allele carriers.

CONCLUSIONS

These results indicate that the normal tissue types tested (colorectum and PBMC) experience dynamic genotype-associated epigenetic alterations at the MLH1 shore, whereas tumour DNA incurs aberrant hypermethylation compared to normal DNA.

Using GWAS to identify novel therapeutic targets for osteoporosis

Osteoporosis is a common, increasingly prevalent, global health burden characterized by low bone mineral density (BMD) and increased risk of fracture. Despite its significant impact on human health, there is currently a lack of highly effective treatments free of side effects for osteoporosis. Therefore, a major goal in the field is to identify new drug targets. Genetic discovery has been shown to be effective in the unbiased identification of novel drug targets and genome-wide association studies (GWASs) have begun to provide insight into genetic basis of osteoporosis. Over the last decade, GWASs have led to the identification of ∼100 loci associated with BMD and other bone traits related to risk of fracture. However, there have been limited efforts to identify the causal genes underlying the GWAS loci or the mechanisms by which GWAS loci alter bone physiology. In this review, we summarize the current state of the field and discuss strategies for causal gene discovery and the evidence that the novel genes underlying GWAS loci are likely to be a new source of drug targets.