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    <title>STAT3-mediated allelic imbalance of novel genetic variant Rs1047643 and B-cell-specific
      super-enhancer in association with systemic lupus erythematosus</title>
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        <h1 itemprop="headline"
          content="STAT3-mediated allelic imbalance of novel genetic variant Rs1047643 and B-cell-specific super-enhancer in ass…">
          STAT3-mediated allelic imbalance of novel genetic variant Rs1047643 and B-cell-specific
          super-enhancer in association with systemic lupus erythematosus</h1>
        <meta itemprop="image"
          content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=STAT3-mediated%20allelic%20imbalance%20of%20novel%20genetic%20variant%20Rs1047643%20and%20B-cell-specific%20super-enhancer%20in%20ass%E2%80%A6">
        <ol data-itemprop="authors">
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Yanfeng Zhang"><span data-itemprop="givenNames"><span
                itemprop="givenName">Yanfeng</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Zhang</span></span><span data-itemprop="emails"><a
                itemprop="email"
                href="mailto:yanfengzhang1984@outlook.com">yanfengzhang1984@outlook.com</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Kenneth Day"><span data-itemprop="givenNames"><span
                itemprop="givenName">Kenneth</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Day</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Devin M Absher"><span data-itemprop="givenNames"><span
                itemprop="givenName">Devin</span><span itemprop="givenName">M</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Absher</span></span><span
              data-itemprop="emails"><a itemprop="email"
                href="mailto:dabsher@hudsonalpha.org">dabsher@hudsonalpha.org</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
        </ol>
        <ol data-itemprop="affiliations">
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1"
            id="author-organization-1"><span itemprop="name">Huntsville</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressCountry">United States</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2"
            id="author-organization-2"><span itemprop="name">Irvine</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressCountry">United States</span></address></li>
        </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher">
          <meta itemprop="name" content="Unknown"><span itemscope=""
            itemtype="http://schema.org/ImageObject" itemprop="logo">
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              content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown">
          </span>
        </span><time itemprop="datePublished" datetime="2022-02-21">2022-02-21</time>
        <ul data-itemprop="genre">
          <li itemprop="genre">Research Article</li>
        </ul>
        <ul data-itemprop="about">
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Computational and Systems Biology</span></li>
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Genetics and Genomics</span></li>
        </ul>
        <ul data-itemprop="keywords">
          <li itemprop="keywords">allelic imbalance</li>
          <li itemprop="keywords">super-enhancer</li>
          <li itemprop="keywords">STAT3</li>
          <li itemprop="keywords">systemic lupus erythematosus</li>
          <li itemprop="keywords">b-lymphocyte</li>
          <li itemprop="keywords">chromatin accessibility</li>
          <li itemprop="keywords">Human</li>
        </ul>
        <ul data-itemprop="identifiers">
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/publisher-id"><span
              itemprop="name">publisher-id</span><span itemprop="value"
              data-itemtype="http://schema.org/Number">72837</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi">
            <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.72837</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/elocation-id"><span
              itemprop="name">elocation-id</span><span itemprop="value">e72837</span>
          </li>
        </ul>
        <section data-itemprop="description">
          <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2>
          <meta itemprop="description"
            content="Mapping of allelic imbalance (AI) at heterozygous loci has the potential to establish links between genetic risk for disease and biological function. Leveraging multi-omics data for AI analysis and functional annotation, we discovered a novel functional risk variant rs1047643 at 8p23 in association with systemic lupus erythematosus (SLE). This variant displays dynamic AI of chromatin accessibility and allelic expression on  FDFT1  gene in B cells with SLE. We further found a B-cell restricted super-enhancer (SE) that physically contacts with this SNP-residing locus, an interaction that also appears specifically in B cells. Quantitative analysis of chromatin accessibility and DNA methylation profiles further demonstrated that the SE exhibits aberrant activity in B cell development with SLE. Functional studies identified that STAT3, a master factor associated with autoimmune diseases, directly regulates both the AI of risk variant and the activity of SE in cultured B cells. Our study reveals that STAT3-mediated SE activity and cis-regulatory effects of SNP rs1047643 at 8p23 locus are associated with B cell deregulation in SLE.">
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Mapping of allelic imbalance
            (AI) at heterozygous loci has the potential to establish links between genetic risk for
            disease and biological function. Leveraging multi-omics data for AI analysis and
            functional annotation, we discovered a novel functional risk variant rs1047643 at 8p23
            in association with systemic lupus erythematosus (SLE). This variant displays dynamic AI
            of chromatin accessibility and allelic expression on <em itemscope=""
              itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> gene in B cells with SLE. We
            further found a B-cell restricted super-enhancer (SE) that physically contacts with this
            SNP-residing locus, an interaction that also appears specifically in B cells.
            Quantitative analysis of chromatin accessibility and DNA methylation profiles further
            demonstrated that the SE exhibits aberrant activity in B cell development with SLE.
            Functional studies identified that STAT3, a master factor associated with autoimmune
            diseases, directly regulates both the AI of risk variant and the activity of SE in
            cultured B cells. Our study reveals that STAT3-mediated SE activity and cis-regulatory
            effects of SNP rs1047643 at 8p23 locus are associated with B cell deregulation in SLE.
          </p>
        </section>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction
        </h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Super-enhancers (SEs) are
          recently discovered large domains of clustered enhancers <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib28"><span>28</span><span>Parker
                  et al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib44"><span>44</span><span>Whyte et
                  al.</span><span>2013</span></a></cite></span>. The extraordinary feature of SEs is
          the extremely high and broad enrichment of enhancer-related transcription factors (TFs),
          H3K4me1 and H3K27ac modifications, resulting in high capability to enhance gene expression
          programs <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib44"><span>44</span><span>Whyte et al.</span><span>2013</span></a></cite>. A
          large quantity of SEs show cell/tissue specificity <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>Vahedi et
                al.</span><span>2015</span></a></cite>, thereby they have become principal
          determinants of cell identity <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Hnisz et
                al.</span><span>2013</span></a></cite>. Nonetheless, disease-associated SEs, in
          particular those exhibiting aberrant activity in autoimmune diseases, are less
          characterized.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Signal transducer and activator
          of transcription 3 (STAT3), as one of seven STAT family members, is activated by
          phosphorylation at tyrosine 705 (Y705) and/or at serine 727 (S727) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib8"><span>8</span><span>Decker and
                Kovarik</span><span>2000</span></a></cite>. After import to the nucleus, the
          phospho-STAT3 (pSTAT3) modulates gene transcription by binding its target sequence <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib22"><span>22</span><span>Levy and
                Darnell</span><span>2002</span></a></cite>. STAT3 has gained broad attention because
          it plays a key role in a variety of pathophysiological immune responses related to
          lymphocyte development and differentiation, and in other cellular processes of normal and
          tumor cells <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib48"><span>48</span><span>Yu et al.</span><span>2009</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Systemic lupus erythematosus
          (SLE) is an autoimmune disease that is known to be associated with an array of abnormal
          immune cell signaling. B-cell hyperactivity in auto-antigen recognition and interaction
          with T-cells, which ultimately results in multi-organ damage, is central to the
          pathogenesis of SLE <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib31"><span>31</span><span>Rahman and
                Isenberg</span><span>2008</span></a></cite>. Genetic factors conferring a
          predisposition to the development of SLE have been widely characterized. Over 100 loci
          have been implicated in SLE by genome-wide association studies (GWAS) <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib6"><span>6</span><span>Catalina
                  et al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib47"><span>47</span><span>Yin et
                  al.</span><span>2021</span></a></cite></span>. Among them, several genes and/or
          loci are potent as putative drivers of the disease. For example, genetic risk variants at
          the promoter of <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">BLK</em> at
          8p23 locus alter <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">BLK</em>
          transcription activity and thus contribute to autoreactive B-cell responses <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib13"><span>13</span><span>Guthridge et
                al.</span><span>2014</span></a></cite>. Nonetheless, the GWAS-identified genetic
          variants together explained approximately 30% of the heritability of SLE <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib37"><span>37</span><span>Sun et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib27"><span>27</span><span>Morris
                  et al.</span><span>2016</span></a></cite></span>, suggesting a requirement of
          further efforts to explain the missing heritability of SLE. Meanwhile, there is growing
          evidence that genetic risk factors behave in a context-dependent or cell-specific manner
          <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib13"><span>13</span><span>Guthridge et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib12"><span>12</span><span>Gallagher and
                  Chen-Plotkin</span><span>2018</span></a></cite></span>. Thus, for SLE and other
          autoimmune diseases, there is a need to identify the regulatory programs in which these
          genetic factors impact the immune cell developmental processes.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">One approach for tying genetic
          risk to function in the post-GWAS era <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Gallagher
                and Chen-Plotkin</span><span>2018</span></a></cite>, is a measurement of allelic
          imbalance (AI) on two alleles at a given heterozygous locus, typically at
          single-nucleotide polymorphism (SNP). The genes and/or loci with SNPs exhibiting AI could
          provide a strong foundation for implicating the genetic or epigenetic mechanisms linked to
          complex traits or diseases <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib29"><span>29</span><span>Pastinen
                  and Hudson</span><span>2004</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib46"><span>46</span><span>Yan et
                  al.</span><span>2002</span></a></cite></span>. As a readout of AI, analyses of
          allele-specific chromatin accessibility and allele-specific RNA expression have
          accumulated a wealth of interesting findings, including functional cis-regulation <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>Li et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib52"><span>52</span><span>Zhang et
                  al.</span><span>2020</span></a></cite></span>, genomic imprinting <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib30"><span>30</span><span>Pollard et al.</span><span>2008</span></a></cite>,
          X-chromosome inactivation or escape <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib51"><span>51</span><span>Zhang et
                al.</span><span>2020</span></a></cite>. Therefore, tracking AI difference in a
          comparison between diseases and controls may enable to uncover novel functional variants
          associated with complex diseases. In this study, we describe one such strategy through
          integrative multi-omics analysis to discover known or novel functional variants associated
          with SLE, and report on the identification of a novel risk variant rs1047643 and
          B-cell-specific SE in B cells with SLE. We further demonstrate that the resultant allelic
          imbalanced variant and SE activity are directly controlled by STAT3, a master TF that
          plays a critical role in B cell development and highly associates with autoimmune
          diseases.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="multi-omics-data-summary">
          Multi-omics data summary</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Functional genomics sequencing
          data sets comprising 279 samples from eleven studies were collected (<a href="#supp1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>). Of
          eleven studies, seven are SLE case-control studies with data across three immune cell
          types including B cells, T cells, and Neutrophils (<a href="#supp2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 2</a>). Also included in the
          present study were SNP microarray data from a SLE GWAS study (n = 2279).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="identification-of-sle-associated-variant-showing-ai-at-both-chromatin-and-rna-levels">
          Identification of SLE-associated variant showing AI at both chromatin and RNA levels</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We next designed a two-stage
          study (<a href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1</a>)
          to identify putative SLE-associated functional variants. In stage I, also named as the
          discovery stage, two chromatin accessibility (ATAC-seq) data sets (Accession ID: GSE118253
          and GSE71338, <a href="#supp1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>) comprised 49 samples
          were analyzed. We focused on those variants displaying difference in AI of chromatin
          accessibility at heterozygous SNP sites in a comparison between SLE and controls (see
          Materials and methods in detail). In total, 18,456 (the GSE118253 dataset) and 4319 (the
          GSE71338 dataset) SNPs were tested. We collected the resulting SNPs with nominal
          (unadjusted) p &lt; 0.05. From the reciprocal validation between two data sets, three SNPs
          (rs1047643, rs246367, and rs72642993) were identified to show the significant AI (combined
          p &lt; 0.01) in B cells from patients with SLE, relative to controls (<a href="#fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2A</a> and <a href="#fig2s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure supplement 1</a>).
          Based on annotations from the GWAS central <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib2"><span>2</span><span>Beck et
                al.</span><span>2020</span></a></cite> and HaploReg <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib42"><span>42</span><span>Ward and
                Kellis</span><span>2016</span></a></cite> databases, we then focused on the
          rs1047643 because it is the most promising variant. Meanwhile, in B cells at different
          stages, the allelic preference of chromatin accessibility for SNP rs1047643 is alterable.
          For example, the T allele exhibits more preferential chromatin accessibility in activated
          B cells from patients, relative to the C allele. However, the direction is reversed in SLE
          naive B cells.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1" title="Figure 1.">
          <label data-itemprop="label">Figure 1.</label><img src="index.html.media/fig1.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="schematic-of-the-study-design">Schematic of the study design.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">On the basis of the
              functional genomic data feature, a two-stage study was designed. Summary of data sets
              is available in <a href="#supp1" itemscope=""
                itemtype="http://schema.stenci.la/Link">Supplementary files 1-2</a>.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2b"
          title="Figure 2B."><label data-itemprop="label">Figure 2B.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>library(&quot;ggplot2&quot;)
library(&quot;gridExtra&quot;)
dataSNPNonRn &lt;- read.table(&quot;Figure 2-Source Data 2.txt&quot;, sep = &quot;\t&quot;, header=TRUE, col.names=c(&quot;id&quot;,&quot;chr&quot;,&quot;pos&quot;,&quot;ratio&quot;,&quot;group&quot;))

# Leftplot
leftplot &lt;- ggplot(dataSNPNonRn, aes(x = as.factor(group), y = ratio, color = group, fill=group)) + geom_boxplot(size = 0.2, alpha = 0.75)  + ylab(&quot;Allelic ratio\nrs1047643 (T vs C)&quot;) + xlab(&quot;&quot;) + theme_bw() + theme(panel.grid = element_blank(), legend.position = &quot;none&quot;, axis.line = element_line(colour=&quot;black&quot;), text = element_text(size=11)) + ggtitle(&quot;Non-rN B cells&quot;)

# P-value
#wilcox.test(dataSNP[dataSNP$group %in% &quot;CNTL&quot;, 6], dataSNP[dataSNP$group %in% &quot;SLE&quot;, 6])

dataSNPRn &lt;- read.table(&quot;Figure 2-Source Data 3.txt&quot;, sep = &quot;\t&quot;, header=TRUE, col.names=c(&quot;id&quot;,&quot;chr&quot;,&quot;pos&quot;,&quot;ratio&quot;,&quot;group&quot;))

# Rightplot
rightplot &lt;- ggplot(dataSNPRn, aes(x = as.factor(group), y = ratio, color = group, fill=group)) + geom_boxplot(size = 0.2, alpha = 0.75) + ylab(&quot;Allelic ratio\nrs1047643 (C vs T)&quot;) + xlab(&quot;&quot;) + theme_bw() + theme(panel.grid.minor = element_blank(), legend.position = &quot;none&quot;, axis.line = element_line(colour=&quot;black&quot;), text = element_text(size=11)) + ggtitle(&quot;rN B cells&quot;)

grid.arrange(leftplot,rightplot, nrow=1)

dataSNP &lt;- read.table(&quot;Figure 2-Source Data 4.txt&quot;, sep = &quot;\t&quot;, header=TRUE, col.names=c(&quot;id&quot;,&quot;chr&quot;,&quot;pos&quot;,&quot;ratio&quot;,&quot;group&quot;))

ggplot(dataSNP, aes(x = as.factor(group), y = ratio, color = group, fill=group)) + stat_boxplot(alpha = 0.4, outlier.size = -1, coef=1.95) + geom_point(position = position_jitter(width=0.3), alpha = 0.6, size=I(3)) + ylab(&quot;Allelic expression ratio\nRef vs Alt alleles&quot;) + xlab(&quot;&quot;) + theme_bw() + theme(panel.grid = element_blank(), legend.position = &quot;none&quot;, axis.line = element_line(colour=&quot;black&quot;), text = element_text(size=11))</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="boxplot-showing-allelic-expression-of-snp-rs1047643-in-both-rn-and-activated-b-cells-in-patients-with-sle-as-compared-with-healthy-individuals">
              Boxplot showing allelic expression of SNP rs1047643 in both rN and activated B cells
              in patients with SLE as compared with healthy individuals</h4>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2"
          title="Figure 2A and C."><label data-itemprop="label">Figure 2A and C.</label><img
            src="index.html.media/fig2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="change-of-allelic-chromatin-accessibility-and-expression-in-b-cell-subtypes-from-sle-patients-and-controls">
              Change of allelic chromatin accessibility and expression in B cell subtypes from SLE
              patients and controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Forest plot showing AI of
              allelic chromatin state of SNP rs1047643 in both resting naive (rN) and activated
              (Non-rN) B cells in patients of SLE compared with healthy controls. The p-value per
              study and combined p-value (summary) are calculated based on the linear regression
              model and Fisher’s method, respectively. The plot in the right panel displays the 95%
              of confidence interval of beta-value. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Boxplot showing allelic
              expression of SNP rs1047643 in both rN and activated B cells in patients with SLE as
              compared with healthy individuals. All raw data are available in <a href="#fig2sdata1"
                itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—source data 1</a>.</p>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Source files for presenting
              results in Figure 2.This zip archive contains all source data used for the
              quantitative analyses shown in Figure 2.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s1"
          title="Figure 2—figure supplement 1."><label data-itemprop="label">Figure 2—figure
            supplement 1.</label><img src="index.html.media/fig2-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="change-of-allelic-chromatin-accessibility-in-b-cell-subtypes-from-sle-patients-and-controls">
              Change of allelic chromatin accessibility in B cell subtypes from SLE patients and
              controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Forest plots showing AI of
              allelic chromatin state of SNP rs246367 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) and rs72642993 (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) in both rN and
              activated (Non-rN) B cells in patients of SLE compared with healthy controls. The
              plots in the right panel display the 95% of confidence interval of beta-value.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s2"
          title="Figure 2—figure supplement 2."><label data-itemprop="label">Figure 2—figure
            supplement 2.</label><img src="index.html.media/fig2-figsupp2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="expression-pattern-of-fdft1-and-blk-across-b-cell-subtypes-in-patients-with-sle-and-healthy-controls">
              Expression pattern of FDFT1 and BLK across B cell subtypes in patients with SLE and
              healthy controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The data showing expression
              profiles for FDFT1 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) and BLK (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) in B cell subtypes were from a
              case-control study (Accession ID: GSE118254).</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2s3"
          title="Figure 2—figure supplement 3."><label data-itemprop="label">Figure 2—figure
            supplement 3.</label><img src="index.html.media/fig2-figsupp3.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="expression-pattern-of-fdft1-and-blk-across-b-cell-subtypes-in-patients-with-sle-and-healthy-controls-1">
              Expression pattern of FDFT1 and BLK across B cell subtypes in patients with SLE and
              healthy controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The data showing expression
              profiles for FDFT1 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) and BLK (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) in B cell subtypes were from a
              case-control study (Accession ID: GSE92387).</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Because the rs1047643 is
          located in the first exon of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> gene (<a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3E</a>), it enables us to
          test the functionality of this variant at the transcriptional level. Analyzing RNA-seq
          data (Accession ID: GSE118254), we determined the AI of RNA transcripts for the rs1047643
          by comparing the AR values (see Materials and methods in detail) between SLE and controls.
          In line with results shown above, we observed the dynamic AI pattern on the
          transcriptional level for the rs1047643 (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2B</a>). Meanwhile, this dynamic allelic
          expression pattern is specific during B cell development with SLE (<a href="#fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2C</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3c"
          title="Figure 3C?"><label data-itemprop="label">Figure 3C?</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#&#39; @width 28
#&#39; @height 20
dataSorted &lt;- read.table(&quot;Figure 3-Source Data 2.txt&quot;, sep = &quot;\t&quot;, header = TRUE, col.names = c(&quot;group&quot;,&quot;length&quot;,&quot;EID&quot;,&quot;Group&quot;,&quot;Epigenome.Mnemonic&quot;,&quot;Color&quot;))

ggplot(dataSorted, aes(x= group, y= length/1000, fill=Group)) + labs(x = &quot;Epigenome ID&quot;, y = &quot;Enhancer length (kb)&quot;) + geom_bar(stat = &quot;identity&quot;) + theme_bw() + theme(
   axis.text.x = element_text(size = 7, angle = 90, hjust = 0), legend.position = &quot;top&quot;, legend.direction = &quot;horizontal&quot;
)</code></pre>
          </stencila-code-chunk>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3" title="Figure 3.">
          <label data-itemprop="label">Figure 3.</label><img src="index.html.media/fig3.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="association-analysis-and-functional-prediction-of-snp-rs1047643">Association
              analysis and functional prediction of SNP rs1047643.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Association results for the
              SNP rs1047643 with SLE risk in single marker analyses. MAF, minor allele frequency;
              OR, odds ratio; CI, confidence interval. Adjusted p-trend: after adjustment for 12
              GWAS index SNPs (shown in <strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">E</strong>) in a logistic regression
              model. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>)
              Haplotype analyses of the two SNPs (SNP1: GWAS indexed SNP rs17807624; SNP2:
              rs1047643) in relation to SLE risk. Baseline (the reference haplotype) represents the
              alleles associated with a reduced risk in two SNPs. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Barplot showing the genomic
              length of chromHMM-annotated enhancer state on the super-enhancer region (blue
              highlighted in 3 C) in 43 epigenomes. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Plot shows the eQTL result of
              SNP rs1047643 in whole blood or B cells from three databases (shown in y-axis).
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) Genomic
              annotations of the SNP rs1047643. The three tracks show locations of 13 GWAS index
              SNP, gene annotation and 15-state chromatin segments in CD19+ B cells at 8p23 locus,
              respectively. Vertical blue and purple lines, represents the location of
              super-enhancer and SNP rs1047643, respectively. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">F</strong>) Long-range interaction between
              a super-enhancer and SNP rs1047643. The two tracks show chromatin interactions from
              two independent studies using whole-genome Hi-C and capture Hi-C technologies,
              respectively. Orange curves show the interactions between the super-enhancer and the
              SNP rs1047643. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">G</strong>) Heatmaps showing the 3D DNA
              interactions at 8p23.1 locus in eight cell lines. The rectangle represents
              interactions between the super-enhancer and the SNP rs1047643. All raw data are
              available in <a href="#fig3sdata1" itemscope=""
                itemtype="http://schema.stenci.la/Link">Figure 3—source data 1</a>.</p>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Source files for presenting
              results in Figure 3.This zip archive contains all source data used for the
              quantitative analyses shown in Figure 3.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3s1"
          title="Figure 3—figure supplement 1."><label data-itemprop="label">Figure 3—figure
            supplement 1.</label><img src="index.html.media/fig3-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="chromatin-interactions-with-fdft1-promoter-region-marked-in-green-arrow-on-8p23-locus-from-chi-c-data-with-duplicates-in-two-types-of-normal-t-cells">
              Chromatin interactions with <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter region (marked in
              green arrow) on 8p23 locus from CHi-C data with duplicates in two types of normal T
              cells.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Orange arrow represents the
              location of super-enhancer identified in this study.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3s2"
          title="Figure 3—figure supplement 2."><label data-itemprop="label">Figure 3—figure
            supplement 2.</label><img src="index.html.media/fig3-figsupp2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="heatmaps-of-long-range-chromatin-interactions-from-hi-c-data-in-8p23-locus-at-10-kb-or-20-kb-resolution-in-a-panel-of-human-tissues-n--9-from-the-3d-genome-browser">
              Heatmaps of Long-range chromatin interactions from Hi-C data in 8p23 locus at 10 kb
              (or 20 kb) resolution in a panel of human tissues (n = 9) from the 3D Genome Browser.
            </h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The circles shown on
              heatmaps are the interaction density between SNP rs1047643 and SE region.</p>
          </figcaption>
        </figure>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="association-with-sle-risk-in-american-hispanic-and-european-populations">Association
          with SLE risk in American Hispanic and European populations</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Because SNP rs1047643 has not
          been reported to be associated with the susceptibility of SLE and other autoimmune
          diseases, we next tested the association using a dataset from an SLE GWAS case-control
          study. Employing the univariate analysis for SNP rs1047643 in samples from Hispanic
          populations, we identified an association of the rs1047643 with SLE risk (OR per C effect
          allele = 0.77, 95% CI 0.54–0.99, p = 0.023 after adjusting for covariates, <a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3A</a>), albeit not reaching
          the significance after adjustment for 12 GWAS index SNPs (the top track in <a href="#fig3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3E</a>, where one SNP
          rs2736336 is excluded due to its multivariate alleles). We also examined an association in
          European population (n = 19,468) from <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib3"><span>3</span><span>Bentham et
                al.</span><span>2015</span></a></cite> study. A similar result was observed for the
          rs1047643 (OR per C effect allele = 0.91, 95% CI 0.84–0.98, p = 0.02, <a href="#supp3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 3</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">An analysis of linkage
          disequilibrium (LD) with each of 12 GWAS tag SNPs showed that there was no strong LD
          (r<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span
              data-itemtype="http://schema.org/Number">2</span></sup> &lt;0.1) between SNP rs1047643
          and GWAS SNPs in European population (<a href="#supp4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 4</a>). This result indicates
          that SNP rs1047643 is a potentially SLE GWAS independent functional variant. Of the 12
          index SNPs, indeed, one index SNP rs17807624 with the statistical significance with p &lt;
          1.5 × 10<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">–3</sup> using
          the univariate analysis, is the top signal to which the SNP rs1047643 is conditional.
          Thus, we performed haplotype analyses on these two SNPs (index SNP rs17807624 and
          rs1047643, <a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            3B</a>). Compared with the reference haplotype, which carries the alleles associated
          with a reduced risk in two SNPs, haplotype 2, which carries the risk-associated alleles,
          showed a significant association (p = 0.03).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="functional-annotation">
          Functional annotation</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">An analysis of eQTL data
          derived from three independent cohorts indicated both proximal ( &lt; 200 kb) and distal (
          &gt; 200 kb) regulatory potential for the SNP rs1047643 in normal B or blood cells (<a
            href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3D</a>).
          Interestingly, besides correlated with three adjacent genes (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em>, <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">CTSB</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">NEIL2</em>), the rs1047643 is also an eQTL
          linked with an upstream <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BLK</em> gene in a distance of ~300 kb, a
          result that is detected in two independent data sets. An analysis of RNA-seq data from two
          independent studies (Accession ID: GSE118254 and GSE92387, <a href="#supp1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>) consistently showed
          that expression patterns for two representative genes (<em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BLK</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em>) are gradually increased in a
          developmental process from naive to memory B cells, in particular, the double negative
          memory B cell subset in patients with SLE, the pattern that is not observed in controls
          (<a href="#fig2s2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 2—figure
            supplements 2</a> and <a href="#fig2s3" itemscope=""
            itemtype="http://schema.stenci.la/Link"><span
              data-itemtype="http://schema.org/Number">3</span></a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">By searching for enhancers and
          other regulatory elements across 8p23 locus from a dataset of the 127 epigenomes from
          Roadmap, we identified a SE with a length of 7 kb in the upstream of <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BLK</em> gene in CD19+ B cells (Epigenome
          ID: E032, <a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            3E</a>). An analysis of enhancer elements across the 127 epigenomes showed 43 (33.9%)
          epigenomes had enhancers at this SE region. Comparative analysis of the enhancer length at
          this SE region on the 43 epigenomes further showed that this SE is specific in CD19+ B
          cells (Epigenome ID: E032, <a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3C</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Analyzing Hi-C data sets from
          two independent studies in GM12878 cells, we observed a DNA looping between the SNP
          rs1047643 within FDFT1 and the SE region (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3F</a>). More importantly, in GM12878
          B-lymphoblastic cells, this SE region has a wealth of long-range interactions with
          adjacent genes (e.g., BLK) and functional elements. In contrast, in another seven cells
          (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3G</a>), as
          well as in normal T cells (<a href="#fig3s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3—figure supplement 1</a>) and nine
          selected tissues (<a href="#fig3s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3—figure supplement 2</a>), these
          interactions are either much weaker or completely absent. These results indicate that the
          physical interaction between SNP rs1047643 and SE region, and many interactions with this
          SE, are specific to B-lymphocytes.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="specificity-in-b-cells">
          Specificity in B cells</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We then hypothesized that the
          SE region may show aberrant activity in B cells from SLE patients. To test this
          hypothesis, we conducted quantitative analysis on the same ATAT-seq data (Accession ID:
          GSE118253 and GSE71338) used in stage I (see Materials and methods in detail). Comparison
          of SE activity in a quantitative manner between SLE patients and controls indicated that
          the SE activity is gradually increased through B cell development in SLE patients (<a
            href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4A–B</a>), with
          a hyper-activity being observed in double negative (DN) B cells in patients, relative to
          controls (<a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            4B–C</a>). It should be noted that such a pattern is not observed in the background
          sampling (<a href="#fig4s1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            4—figure supplement 1</a>). Similarly, the rs1047643-containing promoter activity also
          shows up-regulation toward B cell development in SLE patients (<a href="#fig4"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4D–E</a>). In a comparison
          of B cell development on activities of SE and FDFT1 promoter regions in two individuals,
          the chromatin accessibility on both regions in an individual with SLE is increased during
          B cell development, but remains relatively unchanged in the healthy individual (<a
            href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4F–H</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4a"
          title="Figure 4A and C. "><label data-itemprop="label">Figure 4A and C. </label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#&#39; @width 28
#&#39; @height 20
data_final &lt;- read.table(&quot;Figure 4-Source Data 1.txt&quot;, sep = &quot;\t&quot;, header = TRUE, col.names = c(&quot;pos&quot;,&quot;tpm&quot;,&quot;group&quot;,&quot;gsmid&quot;,&quot;cell&quot;))

# ECDF plot 1 within each of two groups
plota &lt;- ggplot(data_final, aes(x=tpm, colour=cell)) + geom_step(aes(y=..y..), stat=&quot;ecdf&quot;, size = 1.1, alpha = 0.75) + xlab(&quot;TPM on a super-enhancer&quot;) + ylab(&quot;Cumulative frequency&quot;) + scale_colour_manual(name = &quot;B-cell&quot;, values = c(&quot;T3&quot; = &quot;black&quot;, &quot;aN&quot; = &quot;#CD950C&quot;, &quot;SM&quot; = &quot;#912CEE&quot;, &quot;DN&quot; = &quot;red&quot;)) + xlim(0, 15) + facet_grid(~ group) + theme_bw() + ggtitle(&quot;A&quot;)

# ECDF plot 1 between two groups
plotc &lt;- ggplot(data_final, aes(x=tpm, colour=group)) + geom_step(aes(y=..y..), stat=&quot;ecdf&quot;, size = 1.2, alpha = 0.65) + xlab(&quot;TPM on a super-enhancer&quot;) + ylab(&quot;Cumulative frequency&quot;) + xlim(0, 15) + scale_colour_manual(name = &quot;Group&quot;, values = c(&quot;SLE&quot; = &quot;red&quot;, &quot;CNTL&quot; = &quot;black&quot;)) + facet_grid(~factor(cell, levels =c(&quot;T3&quot;,&quot;aN&quot;,&quot;SM&quot;,&quot;DN&quot;))) + theme_bw() + ggtitle(&quot;C&quot;)

grid.arrange(plota,plotc, nrow=2)</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="aberration-of-super-enhancer-and-fdft1-promoter-region-in-b-cell-subtypes-from-sle-patients">
              Aberration of super-enhancer and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter region in B cell
              subtypes from SLE patients.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Empirical cumulative
              distribution of TPM values per 50 bp window across the 7 kb SE region in B cell
              subsets for disease and control groups. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Empirical cumulative
              distribution of TPM values on the SE region (same as shown in A) in a comparison
              between two groups across four B cell subtypes.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4" title="Figure 4.">
          <label data-itemprop="label">Figure 4.</label><img src="index.html.media/fig4.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="aberration-of-super-enhancer-and-fdft1-promoter-region-in-b-cell-subtypes-from-sle-patients-1">
              Aberration of super-enhancer and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter region in B cell
              subtypes from SLE patients.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Empirical cumulative
              distribution of TPM values per 50 bp window across the 7 kb SE region in B cell
              subsets for disease and control groups. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Plots showing the TPM values
              at the third quartile (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">Q3</strong>) across B cell subtypes as a
              comparison between SLE and controls. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">C</strong>) Empirical cumulative
              distribution of TPM values on the SE region (same as shown in A) in a comparison
              between two groups across four B cell subtypes. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Boxplots showing the TPM
              values per 50 bp window at the <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter region in B cell
              subtypes for SLE and controls. The black lines and grey areas represent the linear
              regression results towards the B cell development from T3 to DN stages, and 95% of CI.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) Plots
              showing the correlation between super-enhancer and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter regions based on
              mean TPM values with respect to B cell subtypes in SLE and controls. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>) Wiggle plot
              showing the enrichment of open chromatin states at 8p23.1 locus in B cell subtypes for
              two individuals (a healthy individual at upper panel, and a patient with SLE at lower
              panel). Purple and green vertical lines represent the locations for super-enhancer and
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter,
              respectively. Quantitative comparison of chromatin accessibility states in SE (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">G</strong>) and <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter regions
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">H</strong>) with
              respect to B cell subtypes. All raw data are available in <a href="#fig4sdata1"
                itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4—source data 1</a>.</p>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This txt file contains
              source data used for the quantitative analyses shown in Figure 4.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4s1"
          title="Figure 4—figure supplement 1."><label data-itemprop="label">Figure 4—figure
            supplement 1.</label><img src="index.html.media/fig4-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="genome-wide-background-analysis-of-atac-seq-data">Genome-wide background analysis
              of ATAC-seq data.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Left panel: empirical
              cumulative distribution of TPM values per 50 bp window across randomly selected
              regions (n = 2,000) in B cell subsets for disease and control groups. Right panel:
              plots showing the TPM values at the third quartile (Q3) across B cell subtypes as a
              comparison between SLE and controls.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4s2"
          title="Figure 4—figure supplement 2."><label data-itemprop="label">Figure 4—figure
            supplement 2.</label><img src="index.html.media/fig4-figsupp2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="aberration-of-super-enhancer-in-resting-naive-b-cell-subtypes-from-sle-patients-in-relation-to-healthy-controls">
              Aberration of super-enhancer in resting naive B cell subtypes from SLE patients in
              relation to healthy controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Wiggle plot showing the
              enrichment of open chromatin states at 8p23.1 locus in resting native B cells from
              eight individuals. Blue and purple vertical lines represent the locations of SE and
              <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> promoter,
              respectively. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B–C</strong>) Quantitative comparison of
              chromatin accessibility states in the SE and FDFT1 promoter regions in naive B cells
              in a comparison between SLE and controls.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig4s3"
          title="Figure 4—figure supplement 3."><label data-itemprop="label">Figure 4—figure
            supplement 3.</label><img src="index.html.media/fig4-figsupp3.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="super-enhancer-activity-in-t-and-neutrophils-from-sle-patients-and-controls">
              Super-enhancer activity in T and neutrophils from SLE patients and controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A–B</strong>) Empirical cumulative
              distribution of TPM values per 50 bp window and enrichment of ATAC-seq reads (TPM
              value) across the SE region in neutrophil cell subsets from SLE patients and controls.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C–D</strong>)
              Empirical cumulative distribution of TPM values per 50 bp window and enrichment of
              ATAC-seq reads (TPM value) across the SE region in two T cell subsets from SLE
              patients. (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>)
              Wiggle plot showing the enrichment of open chromatin states at 8p23.1 locus in
              neutrophils and T cells. Blue and purple vertical lines represent the locations of SE
              and FDFT1 promoter, respectively.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We also quantitatively compared
          open chromatin states of SE and FDFT1 promoter regions in resting naive B cells (Accession
          ID: GSE71338). Concordant with the results from active B cell subsets, the open chromatin
          states on both regions are low in non-active B cells from SLE patients, relative to
          healthy controls (<a href="#fig4s2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 4—figure supplement 2</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We further conducted
          quantitative analyses on ATAC-seq data from another two independent studies in two immune
          cell types, T cells, and neutrophils (Accession ID: GSE139359 and GSE110017, <a
            href="#supp1" itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file
            1</a>). The results showed that there was no marked enrichment of ATAC-seq reads on both
          the SE and FDFT1 promoter regions in these two immune cell types for both SLE and controls
          (<a href="#fig4s3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4—figure
            supplement 3</a>). Collectively, these results suggest a B cell specific,
          rs1047643-interacting SE whose activity is aberrant in SLE B cell development.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="hypomethylation-in-sle-b-cells">Hypomethylation in SLE B cells</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We further analyzed DNA
          methylation in the SE region using RRBS data in B cell development in a comparison between
          SLE and controls (Accession ID: GSE118255, <a href="#supp1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>). Our results show that
          DNA methylation levels on the SE region are gradually decreased in the developmental
          process from resting native (rN) to memory B cells in patients with SLE (<a href="#fig5"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5A</a>). In contrast, there
          is no such obvious change of DNA methylation pattern in the control group. In addition, an
          analysis of DNA methylation levels on the background sampling doesn’t show such a
          difference between SLE and controls (<a href="#fig5s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5—figure supplement 1</a>), further
          suggesting that epigenetic change on the SE region is biologically meaningful in B cell
          development in SLE.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5"
          title="Figure 5A. "><label data-itemprop="label">Figure 5A. </label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#&#39; @width 28
#&#39; @height 20
dataTrans &lt;- read.table(&quot;Figure 5-Source Data 1.txt&quot;, sep = &quot;\t&quot;, header = TRUE, col.names = c(&quot;values&quot;,&quot;ind&quot;,&quot;pos&quot;,&quot;group&quot;,&quot;cells&quot;))

ggplot(dataTrans, aes(x =factor(cells, levels=c(&quot;rN&quot;,&quot;T3&quot;,&quot;aN&quot;,&quot;SM&quot;,&quot;DN&quot;)), y=values * 100, colour = group)) + geom_boxplot(outlier.size = -1, alpha=0.25, size = 0.1) + ylab(&quot;CpG methylation level (%)\non SE region&quot;) + xlab(&quot;B cell types&quot;) + geom_smooth(method = &quot;lm&quot;, aes(group=group), size = 1.1, alpha=0.45, level = 0.9) + scale_colour_manual(name = &quot;Group&quot;, values = c(&quot;CNTL&quot; = &quot;black&quot;,&quot;SLE&quot; = &quot;red&quot;)) + theme_bw() + theme(panel.grid.minor = element_blank(), axis.line = element_line(colour=&quot;black&quot;), text = element_text(size=12))</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="hypomethylation-in-super-enhancer-region-in-b-cell-subtypes-from-sle-patients">
              Hypomethylation in super-enhancer region in B cell subtypes from SLE patients.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Boxplots showing the CpG
              methylation levels per 50 bp window in 7 kb SE region in B cell subtypes for SLE and
              control groups. The black and red lines represent the linear regression results
              towards the B cell development from rN to DN stages for SLE and controls,
              respectively.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5b"
          title="Figure 5B."><label data-itemprop="label">Figure 5B.</label><img
            src="index.html.media/fig5.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="hypomethylation-in-super-enhancer-region-in-b-cell-subtypes-from-sle-patients-1">
              Hypomethylation in super-enhancer region in B cell subtypes from SLE patients.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Plots showing the correlation
              between TPM values (y-axis) and DNA methylation levels (x-axis) averaged over each B
              cell type in SLE and controls. All raw data are available in <a href="#fig5sdata1"
                itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5—source data 1</a>.</p>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This txt file contains
              source data used for the quantitative analyses shown in Figure 5.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5s1"
          title="Figure 5—figure supplement 1."><label data-itemprop="label">Figure 5—figure
            supplement 1.</label><img src="index.html.media/fig5-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="dna-methylation-comparison-across-randomly-selected-regions-in-b-cell-subtypes-between-patients-with-sle-and-controls">
              DNA methylation comparison across randomly selected regions in B cell subtypes between
              patients with SLE and controls.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Boxplots showing the CpG
              methylation levels per 50 bp window in 2000 randomly selected regions in B cell
              subtypes for SLE and control groups. The black and red lines represent the linear
              regression results towards the B cell development from rN to DN stages for SLE and
              controls, respectively.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A correlation analysis also
          showed a marked negative correlation between open chromatin states (TPM values, also
          presented in <a href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            4E</a>) and DNA methylation levels at the SE region in the SLE group, relative to the
          healthy controls (<a href="#fig5" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 5B</a>). Together, these results
          reinforce the aberrant activity of SE in developmental process of B-lymphocytes in
          patients with SLE.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="stat3-binding-on-both-super-enhancer-and-rs1047643-residing-regions">STAT3 binding on
          both super-enhancer and rs1047643-residing regions</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">TF-motif enrichment and binding
          analysis using the ENCODE TF ChIP-seq dataset (v3) predicted that STAT3 may bind to both
          the SNP rs1047643-containing promoter and SE regions (data not shown). To validate the
          finding, we designed two pairs of primers (SE5 and SE3, <a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6A</a>) to determine the STAT3 binding on
          SE region and its contribution to the SE activity using STAT3, H3K4me1 and H3K27ac
          ChIP-qPCR assays in GM11997 cells. Under normal culture conditions, we validated that
          pSTAT3, H3K4me1 and H3K27ac modifications are remarkably enriched on the SE region (<a
            href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6A, B and
            D</a>), but not on the negative control region (<a href="#fig6s1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6—figure supplement 1</a>) in
          B-lymphoblastic cells, relative to IgG mock controls. We then conducted both the
          inhibition and activation of STAT3 DNA-binding activity using two small molecules. In
          B-lymphoblastic cells challenged with S3I-201, a STAT3 DNA binding inhibitor, both the DNA
          binding of STAT3 on SE region and the SE activity are significantly reduced (<a
            href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6A and B</a>),
          relative to control. In GM11997 cells treated with ML115, a selective activator of STAT3
          (41), both the STAT3 DNA-binding capability on SE region and the SE activity are
          significantly increased (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6D and E</a>), relative to controls.
          These results together demonstrate that STAT3 directly modulates the SE activity.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6" title="Figure 6.">
          <label data-itemprop="label">Figure 6.</label><img src="index.html.media/fig6.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="contribution-of-stat3-modulates-the-enhancer-activity-and-snp-residing-locus-in-cultured-gm11997-cells">
              Contribution of STAT3 modulates the enhancer activity and SNP-residing locus in
              cultured GM11997 cells.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) ChIP-qPCR for H3K27ac (left
              lower panel), H3K4me1 (right lower panel) and pSTAT3 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) at 8p23 super-enhancer region
              following 40 μM S3I-201 treatment for 24 hr. Upper panel: UCSC genome browser showing
              the location of two pairs of qPCR primers (SE5 and SE3) on the SE region (yellow). Two
              tracks shown below are the enrichment of H3K27ac and H3K4me1 across the SE region.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) Allelic
              ChIP-qPCR for pSTAT3 binding on rs1047643 (T vs C alleles) following S3I-201 treatment
              for 24 hr. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D–E</strong>) ChIP-qPCR for H3K27ac
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>), and
              pSTAT3 (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">E</strong>) at
              8p23 super-enhancer region following 100 nM ML115 treatment for 6 hr. (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">F</strong>) Allelic ChIP-qPCR
              for pSTAT3 binding on rs1047643 in cells that have been challenged with ML115 for 6 hr
              as indicated. Note: the fold changes for the rs1047643-associated <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">BLK</em> and <em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> genes in response to small
              molecules compared to vehicle (0.1% DMSO) as control, which was set as one in all
              cases, are presented. NS, not significance; *, p &lt; 0.05; **, p &lt; 0.01; ***, p
              &lt; 0.005.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6s1"
          title="Figure 6—figure supplement 1."><label data-itemprop="label">Figure 6—figure
            supplement 1.</label><img src="index.html.media/fig6-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="quality-control-of-chip-experiments-in-gm11997-cells">Quality control of ChIP
              experiments in GM11997 cells.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Plots showing ChIP-qPCR
              results for H3K27ac (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">A
                and D</strong>), H3K4me1 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B and E</strong>) and pSTAT3 (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">C and F</strong>) at a
              negative control (NC) region with the treatment of S3I-201 and ML115, respectively.
            </p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6s2"
          title="Figure 6—figure supplement 2."><label data-itemprop="label">Figure 6—figure
            supplement 2.</label><img src="index.html.media/fig6-figsupp2.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="genotyping-of-snp-rs1047643-in-gm11997-genomic-dna-using-allelic-qpcr-analysis">
              Genotyping of SNP rs1047643 in GM11997 genomic DNA using allelic qPCR analysis.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Amplification plots are
              presented for two alleles.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6s3"
          title="Figure 6—figure supplement 3."><label data-itemprop="label">Figure 6—figure
            supplement 3.</label><img src="index.html.media/fig6-figsupp3.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="validation-of-stat3-mediated-allelic-binding-in-gm11997-cells">Validation of
              STAT3-mediated allelic binding in GM11997 cells.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Plots showing ChIP-qPCR
              results for pSTAT3 at rs1047643 following 1 μM Cucurbitacin I for 24 hr (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">A</strong>) and 50 ng/ml IL-6
              treatment for 1 h (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>), respectively. NS, not
              significance; *, p &lt; 0.05.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We next tested whether the
          STAT3 might also regulate the rs1047643-residing regions. Using allelic qPCR assay, we
          confirmed that genomic DNA in the GM11997 cells carries a heterozygous variant for the SNP
          rs1047643 (<a href="#fig6s2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            6—figure supplement 2</a>), enabling the AI analysis in this cell model. In GM11997
          cells treated with the STAT3 inhibitor S3I-201, STAT3 binding on the risk allele T is
          significantly reduced, relative to the rs1049643-C allele (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6C</a>). Conversely, we observed an
          increase of STAT3 DNA binding at the rs1049643-T allele in cells stimulated with the STAT3
          activator ML115 (<a href="#fig6" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 6F</a>). We further confirmed the
          findings in cells treated with Cucurbitacin I or IL-6 that acts an inhibitor and
          stimulator (<a href="#fig6s3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            6—figure supplement 3</a>) of the Janus Kinase (JAK)/STAT3 signaling pathway <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Blaskovich
                  et al.</span><span>2003</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib17"><span>17</span><span>Hunter
                  and Jones</span><span>2015</span></a></cite></span>, respectively.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Consistent with findings in the
          STAT3 DNA-binding study, we observed a significant change of the rs1049643-T allele at the
          transcriptional level after treatment with S3I-201 and ML115 at different concentrations
          and duration times, relative to the C allele (<a href="#fig7" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7A and C</a> and <a href="#fig7s1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7—figure supplement 1</a>).
          These results suggest that the risk rs1049643-T allele is preferentially bound and
          regulated by STAT3 in B cells.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7" title="Figure 7.">
          <label data-itemprop="label">Figure 7.</label><img src="index.html.media/fig7.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="expression-of-two-alleles-on-snp-rs1047643-and-its-linked-genes-in-cultured-cells">
              Expression of two alleles on SNP rs1047643 and its linked genes in cultured cells.
            </h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Left panel: allelic RT-qPCR
              on SNP rs1047643 (T vs C alleles) following S3I-201 (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) and ML115 (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) treatment for 24
              hr, respectively. Right panel: RT-qPCR analysis showing the fold changes for the
              rs1047643-associated BLK and FDFT1 genes in response to different concentrations of
              S3I-201 (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>)
              and ML115 (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">D</strong>)
              compared to vehicle (0.1% DMSO) as control, which was set as one in all cases, are
              presented. *, p &lt; 0.05; **, p &lt; 0.01; ***, p &lt; 0.005.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig7s1"
          title="Figure 7—figure supplement 1."><label data-itemprop="label">Figure 7—figure
            supplement 1.</label><img src="index.html.media/fig7-figsupp1.jpg" alt="" itemscope=""
            itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="expression-of-two-alleles-on-snp-rs1047643-in-b-lymphoblastic-cells">Expression of
              two alleles on SNP rs1047643 in B-lymphoblastic cells.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Allelic RT-qPCR in GM11997
              cells that have been challenged with S3I-201 for 24 hr (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) and ML115 for 6 hr (<strong
                itemscope="" itemtype="http://schema.stenci.la/Strong">B</strong>) as indicated,
              respectively.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Finally, we determined RNA
          expression of BLK and FDFT1, two representative genes that correlate with the risk
          rs1047643. The expression levels of both genes are decreased with the treatment of S3I-201
          (<a href="#fig7" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 7B</a>), and
          upregulated with the STAT3 activator ML115 (<a href="#fig7" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 7D</a>). Together, these results suggest
          the STAT3-binding risk allele T is associated with increased expression of <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">BLK</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em>.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the present study, by
          integrating a variety of functional genomic data, we performed AI analysis to uncover
          novel functional promising variants and their regulatory targets in association with SLE.
          Of note, the diversity of genomic data types from this comprehensive data collection for
          autoimmune diseases allowed us to develop an approach not used before for accessing the
          role of variants in SLE disease activity.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">One of the most significant
          findings is the identification of a novel risk variant rs1047643. The association study
          shows that the rs1049643-T is a risk allele for SLE. Our AI analyses indicate that the
          rs1049643-T allele resides in more open chromatin state and has higher expression in SLE
          memory B cell subsets, relative to the C allele. Functional study further provides
          evidence that the rs1049643-T allele is preferentially bound by STAT3. The SNP rs1047643
          is also an eQTL linked with both proximal and distal genes, including <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">BLK</em>, the gene that plays a critical
          role in B lymphocyte development <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib33"><span>33</span><span>Saijo et
                al.</span><span>2003</span></a></cite>. These results demonstrate that this novel
          SLE-associated risk rs1047643 whose functionality is mediated by STAT3, may play a role in
          allele-specific control of adjacent genes at 8p23 locus in B cells. Despite no report for
          association with other autoimmune diseases, this SNP has been associated with multiple
          myeloma <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib41"><span>41</span><span>Van Ness et al.</span><span>2008</span></a></cite>
          and follicular lymphoma <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib36"><span>36</span><span>Skibola et al.</span><span>2008</span></a></cite>,
          two malignant diseases whose pathogenesis is partially associated with the dysfunction of
          B cells. Specifically, hyperactive STAT3 has been reported to be associated with poor
          survival in both diseases <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib16"><span>16</span><span>Huang et
                  al.</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>Jung et
                  al.</span><span>2017</span></a></cite></span>. Therefore, our findings may provide
          a clue for genetic and mechanical studies on those B cell associated diseases.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Another intriguing finding in
          this study is the identification of an aberrant activity of a SE in lupus B cell subsets,
          particularly the hyperactivity in memory B cells. In contrast, there is no enhancer
          activity in other immune cells (T cells and neutrophils analyzed in this study) in
          patients with SLE. We also demonstrate that the aberrant activity of the SE can be
          mediated by STAT3. Some studies have consistently reported a critical role of STAT3 in the
          B cell maturation, differentiation, as well as the autoimmunity <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Avery et
                  al.</span><span>2010</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Ding et
                  al.</span><span>2016</span></a></cite></span>. These reports further support the
          significance of STAT3-mediated SE aberration in B cells with SLE.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Several studies have
          highlighted the 8p23 locus as a major SLE susceptibility region <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Demirci et
                al.</span><span>2017</span></a></cite>. Our study further expands the significance
          at this locus. For example, our study and others together suggest that there are a few
          cis-eQTLs linked with transcriptional levels of BLK <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib13"><span>13</span><span>Guthridge et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Demirci et
                  al.</span><span>2017</span></a></cite></span>. Thus, we speculate that the 8p23
          locus may play functional roles in B cell development in both genetic and epigenetic
          fashions. Besides the SNP rs1047643 discovered in the present study, there are 13
          SLE-associated GWAS leading SNPs reported in this locus. Of 13 SNPs, six SNPs (<a
            href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3E</a>)
          directly sit in the SE region, suggesting these risk variants may play roles in a genetic
          interaction way, in spite of the unavailability of AI analysis, due to either low coverage
          (read depth &lt;8) or homozygosity in most or all samples for the 13 SNPs. Epigenetically,
          the SLE-associated SE has physical interactions with adjacent genes, including <em
            itemscope="" itemtype="http://schema.stenci.la/Emphasis">BLK</em> and <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em>, and the risk rs1047643-residing
          region. This indicates a potentially complex role of the variant rs1047643 for broad
          regulation by physically contacting the SE. Thus, our data provide new insights into the
          molecular mechanisms by merging genetic susceptibility with epigenetic impacts on gene
          expression for autoimmune diseases.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">FDFT1</em> is a gene encoding for squalene
          synthase, the enzyme that catalyzes the early step in the cholesterol biosynthetic pathway
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib39"><span>39</span><span>Tozawa et al.</span><span>1999</span></a></cite>.
          Previous studies have shown dyslipidemia, with elevations in total cholesterol,
          low-density lipoprotein, triglyceride levels in patients with lupus <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a
              href="#bib38"><span>38</span><span>Tisseverasinghe et
                al.</span><span>2006</span></a></cite>, especially in the active disease. Our
          multi-omics data indicate that the SNP rs1047643-linked FDFT1 may be aberrantly activated
          in B cell development in SLE patients, thereby providing an insight into the genetic
          implication of lipid metabolism for autoimmune diseases.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The limitations of this study
          include, due to the presence of six SLE GWAS tagging SNPs in SE region, we are unclear how
          they genetically influence the SE activity during B cell development. Second, it remains
          unclear how the AI pattern occurs in naive B cells with lupus. The C allele shows more
          open chromatin state in SLE naive B cells, this can’t be explained by STAT3 allelic DNA
          binding at the T allele. This implies that some other factors may also contribute to this
          dynamic AI pattern. Third, no functional studies on genetic manipulation at the rs1047643
          prevent us draw the further conclusion about whether and how the rs1047643 impact the
          STAT3 binding in the present study.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In addition, it should be noted
          is the implementation of linear regression model in the initial step to identify the
          allelic difference signals between SLE and controls. Due to small sample size, there is no
          statistical power to analyze with more optimal statistical models, such as the logistic
          regression model. Meanwhile, the unavailability of other variables in regression analysis
          further restricts this study to remove the potential confounding factors. Together, our
          analysis may miss some potential AI signals and disable to evaluate the causation.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In conclusion, we identified a
          novel functional variant and B-cell-specific SE in association with the SLE pathogenesis,
          both mediated by STAT3, and influencing their gene targets. This insight into the
          mechanism by which manipulation of STAT3 affects the SE activity and its associated gene
          expression in B cells may have implications for future drug development in autoimmunity.
        </p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">
          Materials and methods</h2>
        <table id="keyresource" itemscope="" itemtype="http://schema.org/Table">
          <caption><label data-itemprop="label">Key resources table</label></caption>
          <thead>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Reagent type (species)
                or resource</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Designation</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Source or reference</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Identifiers</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Additional information
              </th>
            </tr>
          </thead>
          <tbody>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Chemical compound, drug
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">ML115</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cayman Chemical</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cayman Chemical: 15,178
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><cite itemscope=""
                  itemtype="http://schema.stenci.la/Cite"><a
                    href="#bib26"><span>26</span><span>Madoux et
                      al.</span><span>2010</span></a></cite></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Chemical compound, drug
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">S3I-201</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich: SML0330
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Chemical compound, drug
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cucurbitacin I</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich: C4493
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Chemical compound, drug
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Recombinant human IL-6
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cell Guidance Systems
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cell Guidance Systems:
                GFH10AF</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Phospho-STAT3 (Ser727)
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher Scientific
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher Scientific
                Cat# PA5-17876; RRID:<a href="https://identifiers.org/RRID/RRID:AB_10980044"
                  itemscope="" itemtype="http://schema.stenci.la/Link">AB_10980044</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Anti-Histone H3 (acetyl
                K27)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Abcam</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Abcam Cat# ab4729;
                RRID:<a href="https://identifiers.org/RRID/RRID:AB_2118291" itemscope=""
                  itemtype="http://schema.stenci.la/Link">AB_2118291</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">H3K4me1 Recombinant
                Polyclonal Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher Scientific
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher Scientific
                Cat# 710795; RRID:<a href="https://identifiers.org/RRID/RRID:AB_2532764"
                  itemscope="" itemtype="http://schema.stenci.la/Link">AB_2532764</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">normal mouse IgG</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
                Cat# sc-2025; RRID:<a href="https://identifiers.org/RRID/RRID:AB_737182"
                  itemscope="" itemtype="http://schema.stenci.la/Link">AB_737182</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Antibody</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">normal rabbit IgG</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Santa Cruz Biotechnology
                Cat# sc-2027; RRID:<a href="https://identifiers.org/RRID/RRID:AB_737197"
                  itemscope="" itemtype="http://schema.stenci.la/Link">AB_737197</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Cell line (<em
                  itemscope="" itemtype="http://schema.stenci.la/Emphasis">H. sapiens</em>)</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">GM11997</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Coriell</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Coriell Cat# GM11997;
                RRID:<a href="https://identifiers.org/RRID/RRID:CVCL_5C55" itemscope=""
                  itemtype="http://schema.stenci.la/Link">CVCL_5C55</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sequence-based reagent
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">ChIP-qPCR primers</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">This paper</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">See <a href="#supp5"
                  itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 5</a></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sequence-based reagent
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">RT-qPCR primers</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">This paper</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">See <a href="#supp5"
                  itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 5</a></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sequence-based reagent
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Allelic qPCR primers
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">This paper</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">See <a href="#supp5"
                  itemscope="" itemtype="http://schema.stenci.la/Link">Supplementary file 5</a></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">R</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">R Foundation</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://www.r-project.org" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://www.r-project.org</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Version 4.0.2</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Hisat2</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><cite itemscope=""
                  itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Kim
                      et al.</span><span>2019</span></a></cite></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Version 2</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Allelic imbalance
                analysis and plots</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">This paper (<cite
                  itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                    href="#bib53"><span>53</span><span>Zhang</span><span>2021</span></a></cite>)
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">The R code used for the
                AI analysis can be accessed via github at <a
                  href="https://github.com/youngorchuang/Allelic-imbalance-analysis" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://github.com/youngorchuang/Allelic-imbalance-analysis</a>,
                (copy archived at <a
                  href="https://archive.softwareheritage.org/swh:1:dir:9dec9e4c202c8a7374019f40ad49270021248752;origin=https://github.com/youngorchuang/Allelic-imbalance-analysis;visit=swh:1:snp:47cdfdd9b150ea9a0f0f34eaf59be0e3ab838694;anchor=swh:1:rev:f0db42af8fed130ebbfe0b46abf992300dadddd6"
                  itemscope=""
                  itemtype="http://schema.stenci.la/Link">swh:1:rev:f0db42af8fed130ebbfe0b46abf992300dadddd6</a>)
              </td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">HiCUP</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><cite itemscope=""
                  itemtype="http://schema.stenci.la/Cite"><a
                    href="#bib45"><span>45</span><span>Wingett et
                      al.</span><span>2015</span></a></cite></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Commercial assay or kit
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Mycoplasma detection kit
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Sigma-Aldrich:MP0025
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Commercial assay or kit
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">SuperScript III reverse
                transcriptase</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher Scientific
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Thermo Fisher
                Scientific:18080044</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Commercial assay or kit
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Luna Universal qPCR
                Master Mix</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">New England Biolabs</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">New England
                Biolabs:M3003X</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
          </tbody>
        </table>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="data-collection">Data
          collection</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We collected a variety of
          functional genomics data, including ATAC-seq, RNA-seq, reduced-representation bisulfite
          sequencing (RRBS), Hi-C data (<a href="#supp1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 1</a>), from the Gene
          Expression Omnibus (GEO) and ArrayExpress databases. Meanwhile, we downloaded genotype and
          Epidemiological data from a SLE case-control study (accession: phs001025.v1) in Hispanic
          population (1393 cases and 886 controls) from the dbGaP database with approval (accessed
          29 Sep 2020).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="analysis-of-rna-seq-and-atac-seq-data">Analysis of RNA-Seq and ATAC-Seq data</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">RNA-seq data were analyzed as
          described previously with few modifications <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib50"><span>50</span><span>Zhang et
                al.</span><span>2016</span></a></cite>. In brief, raw sequencing data were mapped to
          the human reference genome (hg19) using Hisat2 program <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Kim et
                al.</span><span>2019</span></a></cite> with the default setting. Aligned data were
          processed and converted into BAM files using SAMtools program <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Li et
                al.</span><span>2009</span></a></cite>. To quantify gene expression levels, read
          counts were calculated using the featureCounts (version 2.0.2) program, then implemented
          in the edgeR package to calculate the count per million (CPM) values.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We used a similar method
          described previously with several modifications <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib49"><span>49</span><span>Zhang et
                al.</span><span>2015</span></a></cite> to process the ATAC-seq data. In brief, raw
          sequencing data were mapped to the human reference genome (hg19) using Bowtie2 program
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib21"><span>21</span><span>Langmead and
                Salzberg</span><span>2012</span></a></cite> with the default setting. Tag per
          million (TPM) metric, a method commonly used for read counting normalization, was used to
          quantitatively present the enrichment of open chromatin states across regions of interest.
        </p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="identification-of-allelic-imbalance-sites">Identification of allelic imbalance sites
        </h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We used a similar approach
          described in our previous study to call variants and allelic analysis for both RNA-seq and
          ATAC-seq data <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib51"><span>51</span><span>Zhang et al.</span><span>2020</span></a></cite>.
          Briefly, the deduplicated reads in BAM format were realigned and recalibrated, and genetic
          variants were called in a multiple-sample joint manner implemented in the GATK toolkit
          (version 3.3). We next filtered out variants as follows: <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib28"><span>28</span><span>Parker et
                al.</span><span>2013</span></a></cite> mapping quality score &lt;20, <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib44"><span>44</span><span>Whyte et al.</span><span>2013</span></a></cite> ≥ 3
          SNPs detected within 10 bp distance, <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>Vahedi et
                al.</span><span>2015</span></a></cite> variant confidence/quality by depth &lt;2,
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib15"><span>15</span><span>Hnisz et al.</span><span>2013</span></a></cite>
          strand bias score &gt;50, <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib8"><span>8</span><span>Decker and Kovarik</span><span>2000</span></a></cite>
          genotype score &lt;15 and <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib22"><span>22</span><span>Levy and Darnell</span><span>2002</span></a></cite>
          read depth &lt;8. Then, we extracted SNPs annotated from dbSNP (Build 150) that were
          called as heterozygotes for each sample. For a reasonable comparison, those heterozygous
          SNPs identified at least triple in both case and control samples were retained for further
          analysis.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For a given heterozygous SNP,
          we calculated allelic ratio (AR) based on read coverage onto two alleles. For RNA-seq
          data, the resulting AR values were used to compare the AI difference of RNA transcripts
          between cases and controls. For ATAC-seq data, by testing for associations between AR of
          each heterozygous SNP (as response variable) and SLE disease status as categorical
          variable (case/control comparison, control and case are coded as 0 and 1, respectively)
          implemented in the regression analysis (see below), we analyzed the AI difference of
          chromatin accessibility between cases and controls. Then, the p-value and beta coefficient
          were calculated to estimate the significance of the association, and the differences
          between cases and controls, respectively.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="genetic-association-analysis">Genetic association analysis</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For genotype data from a SLE
          case-control study in Hispanic population, all typed SNPs in chromosome eight were
          extracted for imputation using TOPMed Imputation Server <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Das et
                al.</span><span>2016</span></a></cite>. To test SNP rs1047643 in association with
          SLE, we used a method described previously for univariate and haplotype analyses <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib35"><span>35</span><span>Shi et al.</span><span>2016</span></a></cite>. In
          brief, the per-allele odds ratio (OR) and 95% confidence interval (CI) for the rs1047643
          was estimated for SLE risk using a log-additive logistic model with covariates of five
          countries of the Hispanic population, sex and five principal components (PCs). We used the
          haplo.stats package in R for haplotype analyses with five countries of the Hispanic
          population, sex and five PCs as covariates. For SLE GWAS data in European population from
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib3"><span>3</span><span>Bentham et al.</span><span>2015</span></a></cite>
          study, we downloaded summary statistical data (Accession ID: GCST003156) from GWAS catalog
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib5"><span>5</span><span>Buniello et al.</span><span>2019</span></a></cite>
          and extracted statistical results for the SNP rs1047643.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A dataset of GWAS leading SNPs
          was downloaded from the GWAS Catalog <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib5"><span>5</span><span>Buniello et
                al.</span><span>2019</span></a></cite>. Then we extracted SLE-associated SNPs at
          8p23. For each indexed SLE-associated SNP at 8p23, we tested the linkage disequilibrium
          (LD) score (r<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span
              data-itemtype="http://schema.org/Number">2</span></sup>) with query SNP rs1047643 from
          the data set of the Phase 3 of the 1,000 Genomes Project in European population using
          LDlink web tool <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib25"><span>25</span><span>Machiela and
                Chanock</span><span>2015</span></a></cite>.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="super-enhancer-annotation">
          Super-enhancer annotation</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We downloaded whole-genome
          chromatin state segmentation data (core 15-state model) for 127 cell types from the
          Roadmap project. As <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib28"><span>28</span><span>Parker et al.</span><span>2013</span></a></cite>
          defined, we consider contiguous genomic region marked by states 6–7 (enhancer states,
          annotated by chromHMM) with ≥3 kb as SE in a cell type. Then, we extracted and annotated
          super-enhancers on 8p23 locus.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="analysis-of-eqtl-data">
          Analysis of eQTL data</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We collected eQTL data sets
          from three large-scale studies, the Genotype-Tissue Expression (GTEx, v8) <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib20"><span>20</span><span>Laboratory</span><span>2017</span></a></cite>, the
          Haploreg v4.1 dataset <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib42"><span>42</span><span>Ward and Kellis</span><span>2016</span></a></cite>
          and the study by <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib43"><span>43</span><span>Westra</span><span>2013</span></a></cite>. By
          searching for the SNP rsID or the coordinate, we extracted the linked genes with query
          SNPs and plotted the results based on the significance and studies.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="hi-c-data-analysis">Hi-C
          data analysis</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For in situ Hi-C dataset
          (Accession ID: GSE63525), we downloaded the Hi-C binary file from Rao et al. study <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib32"><span>32</span><span>Rao et al.</span><span>2014</span></a></cite> and
          extracted the observed long-range interactions normalized with Knight-Ruiz matrix
          balancing (KR) method at 10 kb resolution across the 8p23.1 region (the coordinate:
          chr8:11260000–11740000, hg19).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For other genome-wide Hi-C
          (Accession ID: GSE113405) and capture Hi-C (CHi-C) datasets (Accession ID: GSE81503 and
          E-MTAB-6621), we used the Hi-C Pipeline (HiCUP) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib45"><span>45</span><span>Wingett et
                al.</span><span>2015</span></a></cite> to truncate and align reads to the human
          reference genome. The deduplicated data were then processed using the Homer pipeline <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib14"><span>14</span><span>Heinz et al.</span><span>2010</span></a></cite> to
          call the significant chromatin interaction at 10 kb resolution with the support of ≥5
          reads and p ≤ 0.001. The resulting interactions were visualized using UCSC Genome Browser
          or Sushi package in R environment.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="dna-methylation-analysis">
          DNA methylation analysis</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We downloaded the processed
          RRBS dataset of DNA methylation profiles on each CpG site from <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib34"><span>34</span><span>Scharer et
                al.</span><span>2019</span></a></cite> report, then extracted and compared CpG
          methylation levels on a region of interest between SLE and healthy controls.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="cell-culture">Cell culture
        </h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">GM11997 B lymphoblastic
          (purchased from Coriell Institute) cells were cultured in RPMI-1640 medium, supplemented
          with 10% FBS (Thermo Fisher Scientific), 2 mM L-glutamine and 1% penicillin-streptomycin
          at 37 °C with 5% CO<sub itemscope="" itemtype="http://schema.stenci.la/Subscript"><span
              data-itemtype="http://schema.org/Number">2</span></sub>. These cells are
          mycoplasma-negative when tested with PCR-based mycoplasma detection kit. For perturbation
          of STAT3, B cells were plated in 12-well plates or 10 cm dishes one day prior to the
          experiment. Cells were then treated with S3I-201, ML115, Cucurbitacin I or IL-6. Cells
          were harvested, washed with PBS and analyzed for proper assays.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="reverse-transcription-qpcr">
          Reverse transcription qPCR</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Total RNA was isolated from
          cells using TRIzol Reagent (Invitrogen) according to the manufacturer’s protocol. Oneμg of
          total RNA was reverse transcribed using SuperScript III reverse transcriptase and random
          hexamer. One-tenth of the RT reaction was used as a template for real-time PCR using Luna
          Universal qPCR Master Mix (New England Biolabs) on a QuantStudi six system. Relative
          expression was calculated with 2<sup itemscope=""
            itemtype="http://schema.stenci.la/Superscript">−ΔΔCt</sup> using the average value of
          housekeeping gene <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">GAPDH</em>.
        </p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="chromatin-immunoprecipitation">Chromatin immunoprecipitation</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">ChIP was performed as described
          previously <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib44"><span>44</span><span>Whyte et al.</span><span>2013</span></a></cite>.
          Approximately 10 × 10<sup itemscope=""
            itemtype="http://schema.stenci.la/Superscript"><span
              data-itemtype="http://schema.org/Number">6</span></sup> suspension cells were
          harvested and in 10 ml PBS with 1% formaldehyde for 10 min at room temperature, followed
          by adding 0.125 M glycine for 5 min. Cells were washed and pelleted by centrifugation and
          lysed with buffer (50 mM Tris-HCl, pH 7.5, 1% IGEPAL CA-630, 1 mM EDTA, 0.1% SDS, plus 1
          mM PMSF) in the presence of protease inhibitors and incubated on ice for 30 min. Cell
          lysate was sonicated to shear DNA to a length of 200–600 bp. The lysates were centrifuged,
          and supernatant transferred to new tubes. For immunoprecipitation, approximately 2 ×
          10<sup itemscope="" itemtype="http://schema.stenci.la/Superscript"><span
              data-itemtype="http://schema.org/Number">6</span></sup> cells and 2–3 μg of antibodies
          or isotype matched IgG as control were used per ChIP and incubated with supernatant at 4
          °C on a rotating wheel overnight. Chromatin-antibody complexes were sequentially washed
          with low-salt buffer, high-salt buffer, LiCl buffer, and TE buffer. Cross-links were
          reversed by addition of 100 μl of 1% SDS plus 100 mM NaHCO<sub itemscope=""
            itemtype="http://schema.stenci.la/Subscript"><span
              data-itemtype="http://schema.org/Number">3</span></sub> and by heating at 65 °C
          overnight. Following phenol/chloroform/isoamyl alcohol extraction, immunoprecipitated DNA
          was precipitated with isopropyl alcohol and resuspended in nuclease-free water. For the
          identification of the specific regions of interest, ~ 10 ng of purified DNA was quantified
          to determine the percentage of each analyzed region, as well as a negative control region
          from <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib11"><span>11</span><span>Fullwood et al.</span><span>2009</span></a></cite>,
          against input DNA. The PCR primers are shown in <a href="#supp5" itemscope=""
            itemtype="http://schema.stenci.la/Link">Supplementary file 5</a>.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="additional-statistical-analysis">Additional statistical analysis</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Data were presented as mean ±
          standard deviation (SD) of three replicates unless stated otherwise. Correlation analysis
          was performed using Pearson’s correlation coefficient. Statistical significance was
          considered at two-sided P-values less than 0.05.</p>
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              </ol><time itemprop="datePublished" datetime="2016">2016</time><span
                itemprop="headline">Fine-scale mapping of 8q24 locus identifies multiple independent
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                itemprop="headline">Allele-specific open chromatin in human iPSC neurons elucidates
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                  itemprop="volumeNumber" data-itemtype="http://schema.org/Number">369</span><span
                  itemscope="" itemtype="http://schema.org/Periodical" itemprop="isPartOf"><span
                    itemprop="name">Science (New York, N.Y.)</span></span></span><span
                itemprop="pageStart" data-itemtype="http://schema.org/Number">561</span><span
                itemprop="pageEnd" data-itemtype="http://schema.org/Number">565</span><span
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                </span>
              </span>
              <meta itemprop="image"
                content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Allele-specific%20open%20chromatin%20in%20human%20iPSC%20neurons%20elucidates%20functional%20disease%20variants">
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            <li itemscope="" itemtype="http://schema.org/Article" itemprop="citation" id="bib53">
              <ol data-itemprop="authors">
                <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
                  <meta itemprop="name" content="Y Zhang"><span data-itemprop="givenNames"><span
                      itemprop="givenName">Y</span></span><span data-itemprop="familyNames"><span
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                  itemprop="headline">Allelic analysis</span></a><span itemscope=""
                itemtype="http://schema.org/Organization" itemprop="publisher"><span
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              <meta itemprop="image"
                content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Allelic%20analysis">
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        </section>
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