Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.

Taking together from both physiological and genetic levels, we suggest that FGF21 is inversely associated with regional BMD. And we haven't observed sex-specific effect in this study.

RANKL is a key regulator involved in bone metabolism, and a drug target for osteoporosis. The clinical diagnosis and assessment of osteoporosis are mainly based on bone mineral density (BMD). Previous powerful genomewide association studies (GWASs) have identified multiple intergenic single-nucleotide polymorphisms (SNPs) located over 100 kb upstream of RANKL and 65 kb downstream of AKAP11 at 13q14.11 for osteoporosis. Whether these SNPs exert their roles on osteoporosis through RANKL is unknown. In this study, we conducted integrative analyses combining expression quantitative trait locus (eQTL), genomic chromatin interaction (high-throughput chromosome conformation capture [Hi-C]), epigenetic annotation, and a series of functional assays. The eQTL analysis identified six potential functional SNPs (rs9533090, rs9594738, r8001611, rs9533094, rs9533095, and rs9594759) exclusively correlated with RANKL gene expression (p < 0.001) at 13q14.11. Co-localization analyses suggested that eQTL signal for RANKL and BMD-GWAS signal shared the same causal variants. Hi-C analysis and functional annotation further validated that the first five osteoporosis SNPs are located in a super-enhancer region to regulate the expression of RANKL via long-range chromosomal interaction. Particularly, dual-luciferase assay showed that the region harboring rs9533090 in the super-enhancer has the strongest enhancer activity, and rs9533090 is an allele-specific regulatory SNP. Furthermore, deletion of the region harboring rs9533090 using CRISPR/Cas9 genome editing significantly reduced RANKL expression in both mRNA level and protein level. Finally, we found that the rs9533090-C robustly recruits transcription factor NFIC, which efficiently elevates the enhancer activity and increases the RANKL expression. In summary, we provided a feasible method to identify regulatory noncoding SNPs to distally regulate their target gene underlying the pathogenesis of osteoporosis by using bioinformatics data analyses and experimental validation. Our findings would be a potential and promising therapeutic target for precision medicine in osteoporosis. © 2018 American Society for Bone and Mineral Research.

Both systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are autoimmune diseases sharing similar genetic backgrounds. Genome-wide association studies have constantly disclosed numerous genetic variants conferring to both disease risks at 7q32.1, but the functional mechanisms underlying them are still largely unknown. Through a series of bioinformatics and functional analyses, we prioritized a potential independent functional single-nucleotide polymorphism (rs13239597) within TNPO3 promoter region, residing in a putative enhancer element and validated that IRF5 is the distal target gene (w118 kb) of rs13239597, which is a key regulator involved in pathogenic autoantibody dysregulation, increasing risk of both SLE and SSc. We experimentally validated the long-range chromatin interactions between rs13239597 and IRF5 using chromosome conformation capture assay. We further demonstrated that rs13239597-A acted as an allele-specific enhancer regulating IRF5 expression, independently of TNPO3 by using dual-luciferase reporter assays and CRISPR-Cas9. Particularly, the transcription factor EVI1 could preferentially bind to rs13239597-A allele and increase the enhancer activity to regulate IRF5 expression. Taken together, our results uncovered a mechanistic insight of a noncoding functional variant acting as an allele-specific distal enhancer to directly modulate IRF5 expression, which might obligate in understanding of complex genetic architectures of SLE and SSc pathogenesis.

More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell–specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes ( NFKB1 and SH2B3 ) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.

Motivation CircRNAs are an abundant class of noncoding RNAs with widespread, cell/tissue specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. Results The predicted accuracy of the expression status models was high with area under the curve of ROC (AUC) values of 0.89∼0.92 and the false positive rates (FPR) of 0.17∼0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression level prediction models was also good with normalized root-mean-square errors (RMSE) of 0.28∼0.30 and Pearson’s correlation coefficient r (PCC) over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33∼0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional 9 cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. Availability The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. Supplementary information Supplementary data are available at Bioinformatics online.

Although genome-wide association studies (GWASs) have identified some risk single-nucleotide polymorphisms in East Asian never-smoking females, the unexplained missing heritability is still required to be investigated. Runs of homozygosity (ROHs) are thought to be a type of genetic variation acting on human complex traits and diseases. We detected ROHs in 8,881 East Asian never-smoking women. The summed ROHs were used to fit a logistic regression model which noteworthily revealed a significant association between ROHs and the decreased risk of lung cancer (P < 0.05). We identified 4 common ROHs regions located at 2p22.1, which were significantly associated with decreased risk of lung cancer (P = 2.00 × 10-4 - 1.35 × 10-4). Functional annotation was conducted to investigate the regulatory function of ROHs. The common ROHs were overlapped with potential regulatory elements, such as active epigenome elements and chromatin states in lung-derived cell lines. SOS1 and ARHGEF33 were significantly up-regulated as the putative target genes of the identified ROHs in lung cancer samples according to the analysis of differently expressed genes. Our results suggest that ROHs could act as recessive contributing factors and regulatory elements to influence the risk of lung cancer in never-smoking East Asian females.

47Circular RNAs (circRNAs) are an abundant class of noncoding RNAs with widespread, 48 cell/tissue specific pattern. Because of their involvement in the pathogenesis of multiple disease, 49 they are receiving increasing attention. Previous work suggested that epigenetic features might 50 be related to circRNA expression. However, current algorithms for circRNAs prediction neglect 51 these features, leading to constant results across different cells. 52 53Here we built a machine learning framework named CIRCScan, to predict expression status 54 and expression levels of circRNAs in various cell lines based on sequence and epigenetic 55 features. Both expression status and expression levels can be accurately predicted by different 56 groups of features. For expression status, the top features were similar in different cells. 57 However, the top features for predicting expression levels were different in different cells. 58 Noteworthy, the importance of H3K79me2 ranked high in predicting both circRNAs expression 59 status and levels across different cells, indicating its important role in regulating circRNAs 60 expression. Further validation experiment in K562 confirmed that knock down of H3K79me2 61 did result in reduction of circRNA production. 62 63Our study offers new insights into the regulation of circRNAs by incorporating epigenetic 64 features in prediction models in different cellular contexts. 65 66 which are believed to be necessary for circularization (Dubin et al. 1995; Zhang et al. 2014). 87 Specifically, Alu repeats were found to be enriched in the flanking intron regions of circRNA-88 forming exons with high conservation and were correlated with the human circRNAs formation. 89The competition between these inverted repeated Alu pairs can promote and regulate alternative 90 circularization, resulting in multiple circular RNA transcripts derived from one gene (Jeck et al. 91 2013; Liang and Wilusz 2014; Zhang et al. 2014). According to the "exon skipping" (Vicens 92 and Westhof 2014; Barrett et al. 2015; Starke et al. 2015) model of exon circularization, a 93 5 method (Ivanov et al. 2015) was developed to predict circRNAs according to sequence features 94 in the intron region. Besides, some tools also tried to distinguish circRNAs from other lncRNAs 95 based on conformational and conservation features, sequence compositions, Alu, SNP densities, 96 and thermodynamic dinucleotide properties (Pan and Xiong 2015; Liu et al. 2016). However, 97 these methods based on genomic sequence features generate indiscriminate predicted results 98 across different tissue/types, which is unable to find the tissue/cell type circRNAs. More 99 importantly, these tools only focus on indicating the circRNAs expression status, and are not 100 capable of predicting circRNAs expression values. 101 102 Several studies have used epigenetic or chromatin features to predict gene expression, for 103 example, Karlić et al. applied a linear regression model using histone modifications to predict 104 gene expression on human T-cell ...