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      CRISPR/cas9 mediated knockout of an intergenic variant rs6927172 identified IL-20RA as a new risk gene for multiple autoimmune diseases

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          Abstract

          Genetic variants near the tumor necrosis factor-α-induced protein 3 gene (TNFAIP3) at the chromosomal region 6q23 demonstrated significant associations with multiple autoimmune diseases. The signals of associations have been explained to the TNFAIP3 gene, the most likely causal gene. In this study, we employed CRISPR/cas9 genome-editing tool to generate cell lines with deletions including a candidate causal variant, rs6927172, at 140 kb upstream of the TNFAIP3 gene. Interestingly, we observed alterations of multiple genes including IL-20RA encoding a subunit of the receptor for interleukin 20. Using Electrophoretic mobility shift assay (EMSA), Western blotting, and chromatin conformation capture we characterized the molecular mechanism that the DNA element carrying the variant rs6927172 influences expression of IL-20RA and TNFAIP3 genes. Additionally, we developed a new use of the transcription activator-like effector (TALE) to study the role of the variant in regulating expressions of its target genes. In summary, we generated deletion knockouts that included the candidate causal variant rs6927172 in HEK293T cells provided new evidence and mechanism for IL-20RA gene as a risk factor for multiple autoimmune diseases.

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          Autoimmune diseases - connecting risk alleles with molecular traits of the immune system.

          Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.
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            Highly efficient generation of heritable zebrafish gene mutations using homo- and heterodimeric TALENs

            Transcription activator-like effector nucleases (TALENs) are powerful new research tools that enable targeted gene disruption in a wide variety of model organisms. Recent work has shown that TALENs can induce mutations in endogenous zebrafish genes, but to date only four genes have been altered, and larger-scale tests of the success rate, mutation efficiencies and germline transmission rates have not been described. Here, we constructed homodimeric TALENs to 10 different targets in various endogenous zebrafish genes and found that 7 nuclease pairs induced targeted indel mutations with high efficiencies ranging from 2 to 76%. We also tested obligate heterodimeric TALENs and found that these nucleases induce mutations with comparable or higher frequencies and have better toxicity profiles than their homodimeric counterparts. Importantly, mutations induced by both homodimeric and heterodimeric TALENs are passed efficiently through the germline, in some cases reaching 100% transmission. For one target gene sequence, we observed substantially reduced mutagenesis efficiency for a variant site bearing two mismatched nucleotides, raising the possibility that TALENs might be used to perform allele-specific gene disruption. Our results suggest that construction of one to two heterodimeric TALEN pairs for any given gene will, in most cases, enable researchers to rapidly generate knockout zebrafish.
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              A Comprehensive Analysis of Shared Loci between Systemic Lupus Erythematosus (SLE) and Sixteen Autoimmune Diseases Reveals Limited Genetic Overlap

              Introduction Systemic lupus erythematosus (SLE [MIM 152700]) is a chronic and severe systemic autoimmune disease characterized by the production of high titers of autoantibodies directed against native DNA and other cellular constituents. It is a prototypic autoimmune disease with heterogeneous clinical manifestations that may involve many different organs and tissues, including skin, kidney, lungs, heart, and brain. The prevalence of SLE in the U.S. is estimated to be between 0.05% and 0.1% of the population, disproportionately affecting women and African Americans (0.009% of white men, 0.066% of white women, 0.038% of African-American men, and 0.282% of African-American women) [1]. A genetic etiology for SLE is unequivocal, as recent genome-wide association studies (GWAS) have identified nearly 40 validated susceptibility loci and implicated a broad array of biological pathways [2]. Nevertheless, recent estimates suggest that these risk loci collectively explain between 8%–15% of the genetic risk for SLE [3], [4], highlighting the fact that much of the heritable basis for SLE remains to be identified. The clustering of multiple autoimmune diseases (ADs) within families, including families with SLE [5], [6], suggests some degree of common genetic susceptibility [7]–[9]. This genetic overlap is exemplified by the well-known associations of certain Human Leukocyte Antigen (HLA) loci with multiple human ADs, as well as non-HLA risk loci in diverse pathways such as IL2RA, STAT4, PTPN22 and IFIH1 [10]. This phenomenon where a single mutation or gene can affect multiple traits is known as pleiotropy. Murine studies have similarly identified many susceptibility loci that are shared across different autoimmune mouse models [11]. However, evidence for specific shared risk variants is modest, and consequently the genetic mechanisms that may explain the patterns of disease aggregation remain unclear. To date, there is no large-scale, comprehensive assessment of the genetic overlap between SLE and other ADs. Multiple genes have been reported to be associated with both SLE and other ADs, but analyses of such shared autoimmune loci have been limited to specific loci and few diseases (reviewed in [12]). Criswell et al. [13] analyzed a collection of 265 multiplex families with at least two ADs. Based on findings concerning PTPN22, they suggest that multiple sclerosis (MS) may have a pathogenesis that is distinct from SLE, rheumatoid arthritis (RA) and type 1 diabetes (T1D). Several genome-wide association studies (GWAS) have been conducted in multiple ADs, providing an opportunity to assess genetic similarity at the genome-wide scale. These include studies of SLE, RA, T1D, MS, ankylosing spondylitis (AS), inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), celiac disease (CelD), psoriasis (PS), psoriatic arthritis (PsA), juvenile idiopathic arthritis (JIA), Kawasaki disease (KA), systemic sclerosis (SScl), sarcoidosis (SA), vitiligo (VI), alopecia areata (AA) and Behçet's disease (BeD). Several studies have evaluated pleiotropic effects between two or three diseases, but have been limited to a few dozen variants in a few loci [14]–[18]. Exceptions include that of Sirota et al. [19], which used over 500 SNPs to analyze allele-specific similarities and differences across six ADs, and Thompson et al. [20], which evaluated the association of over 500 reported autoimmune loci with JIA. Wang et al. [21] similarly performed a genome-wide comparative analysis of CD, UC and T1D, and Festen et al. [22] of CD and CelD. Only Cotsapas et al. [23] have recently analyzed shared variation of 107 immune SNPs between seven Ads including SLE. In order to assess the genetic overlap between SLE and other ADs, potentially unveiling novel contributors to SLE pathogenesis, we comprehensively tested all non-HLA variants implicated in other ADs through large GWA approaches with P 0.70), the most significant being rs6074022 (P = 1.41×10−03, PFDR = 1.24×10−02) (Table 2), upstream of CD40. The risk allele for this variant is the same in SLE as the one reported in MS. All the variants lie within a known CNV region. The IL12A region, identified in GWAS of MS and CelD [28], showed association with SLE at two variants not in LD with each other (r2 = 0.04). The most significant association was with rs17810546 (Pmeta = 9.39×10−03) (Table 2), upstream of IL12A, but the risk allele is different from that reported in CeID. Shared SLE loci One of our goals was to specifically evaluate pleiotropy between SLE and other ADs. In Table 3 we report our most significant findings in regions previously reported to be associated with SLE. The ADs that reported a GWA P 1.45 for variants with minor allele frequency (MAF) = 0.05, OR>1.30 for variants with MAF = 0.10, or OR>1.20 for variants with MAF>0.30. The power for each of the SNPs herein reported is shown in Table S1. For many of the shared GWAS autoimmune loci we found no evidence for association with SLE in these cohorts. We scrutinized all variants that met QC criteria and whose smallest P-value was P>0.05 in any (joint-, replication- , or meta-) analysis. Amongst the loci shared between the most diseases, we found no evidence of association for IL23R, FASLG, REL, IL18RAP, MST1, RBPJ, IL7R, PTGER4, BACH2, PVT1, PTPN2, and C1QTNF6 regions. As in all studies a definitive answer is not possible for all loci. Specifically, we note that in several of the AD loci shared between the largest number of diseases, we did not find SNPs that met the FDR-adjusted threshold in the joint-analysis, but met all the QC criteria and had an unadjusted P-value 10 reported loci in their GWA studies, to associations reported from populations of European ancestry, and excluded those reported from the aggregate phenotype of IBD, as well as those with MS severity or age of onset. Dendrogram and heatmap are shown. We observed that SLE shares the largest number of loci with RA (FAM167A/BLK, IRF5/TNP03, and STAT4). It is noteworthy that SLE appears isolated from the other ADs (i.e., shares the least with the other ADs despite being among the ADs with the most identified risk loci). In contrast, despite only having 12 reported loci, VI clusters more closely with other ADs, suggesting more genetic overlap. However, we strongly caution against over-interpretation of this clustering result, as bootstrapping only revealed strong statistical support (Bootstrap Probability Value≥0.95) for differentiating height from the ADs and the other control diseases (see Materials and Methods). Discussion The clustering of multiple autoimmune disorders in families and evidence for autoimmune pleiotropic loci are well known. Nevertheless, no comprehensive assessment of the specific shared variants between SLE and other autoimmune diseases (ADs) has yet been performed in a single large-scale study based on GWAS data. Analyses of shared SLE loci have been limited to specific loci and few diseases (reviewed in [12]). In this study we used findings from published GWAS to assess the extent of genetic overlap between SLE and seventeen autoimmune diseases, testing if variants implicated in other ADs show association in our large SLE cohort. Given that the MHC is unquestionably a universal risk region for autoimmunity, and some GWAS did not report their results in this region, we excluded HLA loci from our analyses. The loci that were associated with the largest number of ADs include IL23R, TNFAIP3, and IL2RA, supporting an important role for T cell and innate immune response pathways in autoimmunity. Nevertheless, these loci are not implicated in all ADs, suggesting that, with the exception of the HLA region, there seem to be no universal genetic risk factors for autoimmunity. It is commonly accepted that there is a common genetic background predisposing to autoimmunity and inflammation, and that further combinations of more disease-specific variation at HLA and non-HLA genes, in interaction with epigenetic and environmental factors, contribute to disease and its clinical manifestations [33]. Our data additionally suggests that, instead of resulting from common risk factors, autoimmunity may result from specific and multiple different pleiotropic effects. This is consistent with a recent report showing that genomic pleiotropy is relatively low, as most genes affect only a small number of traits [34]. The authors suggest that genes displaying a high degree of pleiotropy also exhibit an individually larger effect on each trait [34]. It is likely that different population genetic factors (e.g., natural selection, migration/isolation, random mutation) in similar or distinct environments led to the establishment of different autoimmune loci and subsequent migrations and interbreeding have led to the current plethora of loci that predispose to autoimmunity. Based on our analyses of shared non-HLA loci across ADs, the most genetically similar diseases appear to be CD with UC, and T1D with RA, sharing 15 and 11 loci, respectively. While the former pair is clearly supported by overlapping clinical manifestations, since both CD and UC are subsets of IBD, the overlap between the latter pair is not entirely clear based on their organ involvement. The clustering patterns do not seem biased by the number of reported loci for each disease. As such, while the genetic overlap between CD and UC may reflect the prevalence of more specific IBD genes, the genetic overlap between T1D and RA may reflect the existence of general, nonspecific autoimmunity genes. Despite being a prototypic AD, the non-HLA genetic overlap between SLE and the ADs herein investigated is more modest than we anticipated. The disease with which it shares the most loci is RA, which is potentially interesting due to the common clinical presentation of arthritis. The number of reported SLE loci is similar to other ADs and does not explain its relative distance from other ADs. The clinical heterogeneity of SLE may, at least in part, account for the relatively modest number of shared loci. Different SLE loci are likely differentially associated with specific clinical criteria, as was recently shown in GWAS of anti-RNA binding proteins [35], and anti–dsDNA autoantibody production [36] in SLE. It should also be noted that SLE may share more loci with systemic diseases not included or not well represented in our analyses. Our data included 49 loci reported for RA, two for BeD, three for SScl, but Sjögren's syndrome and antiphospholipid syndrome lack GWAS. Interestingly, two of the three loci reported in the GWAS of SScl, IRF5 and STAT4, also show association in GWAS of SLE. Similarly, Anaya et al. [37] recently analyzed the association of the SLE predisposing risk variant (rs1143679) for ITGAM-ITGAX across 7 other ADs, only showing a suggestive association for SScl. For many of the shared GWAS autoimmune loci we found no evidence for association with SLE, including for IL23R, in spite of having enough power to detect the effects reported in other diseases. Although we cannot exclude the possibility that 1) other variants in these loci predispose to SLE, or 2) that these loci have weaker effects in SLE implying a potential lack of statistical power, or 3) that their effects are conditional on other unknown loci, it is plausible that the lack of these common genetic factors contributes to SLE being a distinct disease. Also, several established SLE loci are apparently not associated with other ADs, including the ITGAM-ITGAX region, TNFSF4, PTTG1, PHRF1, WDFY4 and BANK1 regions. Obviously, these risk variants may simply have weaker effects in other ADs and the studies lacked power to detect them. This situation was recently illustrated in a meta-analysis of CD and CelD, where the increased power of the combined datasets allowed the detection of shared loci with a relatively small effect, hence undetectable in the individual diseases [22]. Our analyses identify novel shared SLE loci. The results that we report were adjusted for the number of comparisons, which decreases the likelihood of a false positive result. The V-set domain containing T cell activation inhibitor 1 (VTCN1) region, which has been reported in a GWAS of JIA, showed the strongest novel association with SLE. Evidence suggests that this gene plays a role in the negative regulation of T cell responses. The zinc finger ZGPAT region also shows a significant association with SLE. Despite being clearly strong candidates because of their association with other ADs, the new SLE loci require validation. It is worth noting that we discovered associations consistent with and in contrast to the same risk allele in other ADs. This observation was recently confirmed by Wang et al. [34], who suggests that susceptibility loci involved in the pathogenesis of ADs may have antagonistic pleiotropic effects, where risk alleles for one disease may confer selective advantage for another disease or infection resistance. Given that the functional variant is not known, we cannot rule out that the inverse association arises from different LD patterns. A limitation of our study is the fact that we restricted our analyses to variants reported from GWAS in populations of European Ancestry. Although we have certainly missed shared variants identified in large candidate gene studies or targeted meta-analyses, many ADs lack such studies. Thus, given the increasing coverage of the genome with modern SNP chips, we preferred to restrict our analyses to a directly comparable set of results based on GWAS. These agnostic scans help to minimize the extent of potential methodological and publication biases. We should note that our analyses do not provide an unbiased estimate of the total degree of genetic overlap amongst ADs, given that the application of stringent significance thresholds in GWAS certainly overlooks true risk loci. Future studies using all variants in these GWAS will be required to directly estimate the degree of shared susceptibility. Finally, it is important to note that some of the genetic overlap with SLE may have been missed in our analyses because a large proportion of candidate SNPs failed our quality control thresholds, and thus could not be effectively tested for association in our samples. Much remains to be done before the genetic etiology of the autoimmunity spectrum is resolved. Continued studies of populations beyond those of European ancestry are certainly needed. A catalog of all shared and distinct risk loci requires that these regions be thoroughly resequenced in suitably large population samples, with additional genotyping of the resulting comprehensive set of variants in order to confirm and fully characterize the extent of genetic risk. The examination of the patterns observed here generates an appreciation for potential interplay between population genetic factors (e.g., natural selection, migration) and environmental factors and calls for the interrogation of these loci in significant numbers of samples from different ethnic populations. This study represents the most comprehensive evaluation of shared autoimmune loci to date. In addition, we provide further evidence for previously and newly identified pleiotropic genes in SLE. These findings support a relatively distinct genetic susceptibility for SLE, a genetic basis for the shared pathogenesis of ADs, and the value of studies of potentially pleiotropic genes in autoimmune diseases. Materials and Methods Ethics statement Written informed consent was obtained from all study participants and the institutional review board at each collaborating center approved the study. Autoimmune disease loci We constructed a list of reported risk variants for ADs using data from the National Human Genome Research Institute's Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies) accessed on June 4th, 2011 [28]. Briefly, this database contains all identified SNPs with P 0.05); 2) overall 0.01, HWE in cases P>0.0001; and 4) minor allele frequencies (MAFs) of controls within a 95% or 99.99% confidence interval for ethnicity matched HapMap MAFs, for genotyped and imputed SNPs, respectively. Retained SNPs had an estimated MAF>0.01 in the control samples, an information score >0.50 and a confidence score >0.90. Imputed SNPs were analyzed using SNPTEST with probabilistic genotypes [40]. We combined the genotypic and imputed data from the three cohorts described above and performed a joint- and a meta-analysis. In the tables with the results we report which SNPs and cohorts were imputed vs. directly genotyped. We used SNPs that met the same quality criteria as described above. To account for potential population stratification, we computed Principal Components (PCs) and adjusted these analyses for four PCs, as described [25]. The genome-wide inflation factor in the joint analysis was λ = 1.15. We include the joint analysis of these loci after applying quality control to each individual cohort as the joint analysis can provide increased power for some genetic models for more modest allele frequencies (e.g., recessive model). From our list with 446 autoimmune SNPs, 424 total unique SNPs were genotyped or imputed in our SLE cohorts. Of these, 237 (55.9%) met our QC thresholds, while 187 (44.1%) failed as follows: 6 (1.4%) have 10–20% missing genotype data, 1 (0.2%) have MAF 20% missing genotype data, and /or have significant differences in missingness between cases and controls, and 86 (20.3%) did not meet imputation QC thresholds. We report uncorrected P-values, though we also corrected for multiple comparisons using a False Discovery Rate (FDR) procedure [29] for the 237 SNPs that passed QC. As such, our multiple comparison strategy consisted of only selecting those variants that met FDR significance, that is, with a FDR-adjusted P-value<0.05. Although we computed the FDR-adjusted P-value for the smallest P-value (under the additive, dominant or recessive model), this smallest P-value is virtually always within one order of magnitude different from the additive P-value, which is hence comparable to computing the FDR for P-values under the same model. We performed a weighted Z-score meta-analysis as implemented in METAL (www.sph.umich.edu/csg/abecasis/metal), with weights being the square root of the sample size for each dataset; thus, the meta-analysis incorporates direction, magnitude of association and sample size. We report the minimum P-value based on hypothesis tests considering additive, dominant and recessive modes of inheritance; however, because these tests can be affected by low genotype counts, we required at least 30 homozygotes for the minor allele to consider the recessive, and 15 to consider the additive model, otherwise the results under the dominant model are reported. All genetic models were defined relative to the minor allele. Associations with SLE susceptibility were considered statistically significant if they met a FDR-adjusted threshold of P<0.05. We used Quanto (http://hydra.usc.edu/gxe/) to calculate the power of our sample size. We assumed an additive genetic model, population risk of 0.1%, and α = 0.001. In order to examine the global similarity between ADs based on their reported risk loci (defined based on LD, as described above), we performed a hierarchical clustering analysis of ADs with at least 10 reported loci (binary yes/no). ADs with less than 10 reported loci were excluded as their lower count of reported loci may reflect a less intensive assessment of genetic risk factors (i.e. fewer genome-wide investigations often with smaller sample sizes). We restricted the analysis to associations reported from populations of European ancestry, and excluded those reported for MS severity or age of onset. So as not to inform the clustering of ADs based on the presence of joint analyses, we excluded associations from studies of pooled phenotypes including IBD, RA with CelD, CD with CelD, and CD with SA. This produced a final dataset of 330 loci reported across nine ADs. We also included loci reported from the GWAS catalogue for 4 control diseases (height, breast cancer, coronary heart disease, and bipolar disorder), similarly using LD to define specific genomic loci. We computed the dissimilarity between ADs and the control diseases using distance metric appropriate for binary data, performing hierarchical clustering using the hclust function for the R Statistical Programming Language [41]. We evaluated the uncertainty in the clustering analyses using a multiscale bootstrap resampling approach implemented within the pvclust package for R [42]. Supporting Information Table S1 First tier shows the SNPs presented in Table 2 of the manuscript, followed by Table 3 in the middle and Table 4 at the bottom. The power was computed in the joint-analysis of 1,500 cases and 5,706 controls, under the genetic model presented, assuming a population risk of 0.1% and α = 0.001. OR – odds ratio; CI – confidence interval; MAF – Minor allele frequency. The smallest P-value is presented and, unless noted otherwise, it is under the additive model. OR and CI calculated under the model presented. * The superscript after the P-value denotes its genetic model, when other than the additive: ddominant, rrecessive. (DOC) Click here for additional data file.
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                Journal
                Genes & Immunity
                Genes Immun
                Springer Nature
                1466-4879
                1476-5470
                February 23 2018
                :
                :
                Article
                10.1038/s41435-018-0011-6
                29483615
                c5f5f19a-d02b-4ea1-add2-d5de3978348c
                © 2018

                http://www.springer.com/tdm

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