Genetic variants within silencer elements contribute to human blood cell traits

Genetic variants within silencer elements contribute to human blood cell traits | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Genetic variants within silencer elements contribute to human blood cell traits Minkang Tan , Jan van Weerd , Alper Yavas , View ORCID Profile Baoxu Pang doi: https://doi.org/10.1101/2025.01.13.632723 Minkang Tan 1 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jan van Weerd 1 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alper Yavas 1 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Baoxu Pang 1 Department of Cell and Chemical Biology, Leiden University Medical Center , Leiden, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Baoxu Pang For correspondence: b.pang{at}lumc.nl Abstract Full Text Info/History Metrics Preview PDF Abstract Genome-wide association studies (GWAS) have identified numerous non-coding loci associated with human complex traits and diseases. However, assigning the functional impacts to the underlying variants remains challenging. In this study, we performed high-throughput ReSE screening to characterize the effects of 14,720 fine-mapped causal non-coding SNPs associated with 15 diverse blood traits on the potential silencer activity. By prioritizing non-coding variants that confer allelic imbalances in silencer activity, we identified epigenomic signatures of silencer-related variants and assessed their heritability contributions to specific blood traits. We conducted mechanistic studies on individual silencer variants and characterized the transcriptional factors (TF) that may recognize the silencer elements harboring the blood traits-related GWAS variants. We showed the silencer activity of GWAS variants rs4808806 which is related to red blood cell distribution width (RDW) and rs10758656 which is associate with mean corpuscular hemoglobin (MCH) and mean corpuscular volume (MCV) traits. Our study underscores the importance of investigating the silencer-activity-related variants in post-GWAS functional studies. Introduction The human genome harbors approximately 20,000 protein-coding genes and the related coding sequences represent only 2% of the genome. Despite the fact that the majority of the human genome does not encode proteins, this non-coding genome plays crucial roles in gene regulation and harbors non-coding regulatory elements (NCRE), including enhancers, promoters, insulators, silencers, and others. These NCREs coordinate the time-, dosage- and tissue-specificity of target gene expression 1 . Whereas the roles and functional mechanisms of promoters, insulators, and enhancers have been relatively well characterized 2 – 8 , silencers remain understudied and poorly characterized 9 . As a negative functional counterpart of enhancers, some silencers are also located in the accessible chromatin 10 , 11 and harbor binding sites for repressive transcription factors (TF) and co-repressive factors to mediate silencer-promoter interactions 12 – 14 , leading to the repression of target gene transcription. In recent years, an increasing number of studies were made toward the large-scale identification and characterization of silencers in different cell types 10 , 12 , 13 , 15 ; and these various approaches in multiple cell types started the endeavor for a future comprehensive understanding of the biology of silencers and their contribution to human diseases. Genome-wide associations studies (GWAS) linking genomic variation to an increasing number of phenotypic traits and diseases revealed that over 90% of disease- associated sequence variations were found within the non-coding genome 16 , 17 . Such variations are thought to affect the function of NCREs controlling the activity of their target gene promoters 18 , 19 . Systematic annotation and characterization of the functions of the variants within the non-coding genome is therefore crucial for understanding the mechanisms underlying the related homeostasis and disease. The extensive characterization of enhancers has allowed for the identification of functional links between a large number of GWAS variants and affected enhancers for numerous phenotypes and diseases 20 – 23 . Given the similarities in the function of gene regulation between enhancers and silencers, it is expected that genomic variations could have similar effects on the function of silencers. Here, we aim to elucidate the role of genomic variation on the activity of silencers. We focused on the GWAS data characterizing hematopoietic cell lineages for our testing. Blood cell counts and indices are quantitative clinical measures for a variety of biological processes, including hematopoietic progenitor cell production, hemoglobin production, maturation and maintenance of crucial cell numbers in the circulation. The Blood Cell Consortium (BCX) has performed and analyzed GWAS for 15 different blood cell phenotypes across 5 different ancestries in over 700,000 individuals and found over 5,500 associations 24 . These phenotypes include count and volume of red blood cells (RBC), white blood cells (WBC, also comprising eosinophils, neutrophils, monocytes, lymphocytes, and basophils), and platelets (PLT). We systematically assessed the effect of blood cell phenotype-associated genomic variations on silencer activity and further characterized the mechanisms underlying silencer-mediated gene regulation. We performed the high-throughput screening to characterize the potential silencer elements residing in the noncoding GWAS loci that are associated with 15 diverse blood traits using the ReSE silencer screening system 12 , 25 . The statistical analysis on the allelic imbalance of silencer activity from the high-throughput screening dataset was used to pinpoint the impact of noncoding GWAS variants on the silencer functions, i.e., loss-of-silencer-function (LoSF) and gain-of-silencer-function (GoSF) variants. We also characterized the transcriptional factors (TF) that may recognize the silencer elements harboring the blood traits-related GWAS variants. To link the LoSF or GoSF GWAS variants to their target genes, we integrated the cis-eQTLs resources from large-scale whole-blood samples 26 and promoter capture Hi-C across different primary blood cell types 27 . We measured the effects of the non-coding GWAS variants on silencer activities using luciferase assay. Our study underscores the importance of investigating the silencer-activity-related variants in post-GWAS functional studies. Results Prioritizing the potential hematopoietic GWAS SNPs affecting silencer activity Non-coding variants have been well-annotated in the blood cell phenotypes, and some of the knowledge gained has led to successful clinical trials to treat human diseases 28 . Pinpointing the direct functional effects of these variants would help to understand the related phenotypic effects and etiology of diseases. Non-coding regulatory silencers have been less characterized than enhancers where histone modifications H3K4me1 and H3K27ac or P300 transcriptional factor binding P300 are strong signatures. Therefore, we plan to directly screen for the potential effect of noncoding GWAS variants on silencer activities, as the variants may cause disruption of silencer- associated repressor TF binding motif or formation of de novo silencer ( Fig. 1a ). To assess the effect of noncoding SNPs on cell type-specific silencer function, we focused on the extensive epigenomic characterization of hematopoietic cell types. We collected 14,720 SNPs associated with 15 blood cell phenotypes ( Fig. 1b ) in 5 global populations (European ancestries, South Asian ancestries, Hispanic ancestries, East Asian ancestries, and African ancestries), which were prioritized after statistical fine- mapping 24 . The majority of these variants are located in intronic or intergenic genomic regions ( Fig. 1c ) after filtering based on VEP annotation. K562 is an extensively characterized and widely used chronic myelogenous leukemia (CML) cell line with properties analogous to hematopoietic stem cells. To ensure the best coverage on the effects of SNPs on the silencer activity and the screening input fragments harboring the candidate SNPs capture most functional sequences, for each SNP, six oligonucleotides were included in a sliding window in the final assembled screening library ( Methods ). Therefore, six unique sequences containing either effect or non- effect allele nucleotides located either in the middle, on the left (one-fourth of the total length) or on the right (three-fourths of the total length) were extracted from the reference genome ( Fig. 1d ). Combined with 3,446 positive control silencer sequences (47 from low-throughput validation and 3,339 from high-throughput screening) from the SilencerDB database 29 , the final library comprises 91,766 sequences for the ReSE silencer screening 12 . Download figure Open in new tab Figure 1. Prioritization of candidate non-coding GWAS variant affecting silencer element function. (a) Schematic representation of how genetic variants as single-nucleotide polymorphisms (SNPs) within a silencer element can disrupt the silencer function. Silencer elements can recruit repressor transcription factor (TF) binding to suppress the transcription of the target gene (upper panel). An SNP may disrupt the binding motif. As a result, the repressor TF can no longer bind, leading to gene upregulation (middle panel). An SNP creates a new motif for repressor TFs binding and forms a de novo silencer element that leads to transcription repression of the target gene (lower panel). (b) The numbers of fine-mapped SNPs within 95% confidence sets for 15 blood traits (RBC count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, MCH concentration, RBC distribution width, WBC count, neutrophils, monocytes, lymphocytes, basophils, eosinophils, platelet count, and mean platelet). The fine-mapped SNPs were categorized into posterior probability bins based on the highest posterior probability of a variant being causal from either trans- or sub-ancestry association study. (c) Genomic distribution of included GWAS variants associated with blood traits. (d) Oligonucleotide design strategy for tiling the GWAS variants. The same SNP is located in the position 1/4 (left), 1/2 (middle), and 3/4 (right) of the total length (130 nt) of the oligo and oligos with both allele nucleotides included. High-throughput identification of GWAS loci transcriptional silencing function We cloned the library containing all SNPs together with the positive control oligonucleotides into the modified ReSE silencer screening vector with the addition of two adaptor sequences (ReSE_SNP library), and performed the ReSE screening as we previously described ( Fig. 2a ). Next-generation sequencing of the plasmid library showed that over 97% designed sequences can be retrieved, suggesting a good coverage of the designed library complexity ( Fig. 2b ). We transduced the ReSE_SNP library into K562 cells in three biological replicates and isolated genomic DNA from surviving cells both before (pre) and after (post) apoptosis induction. Amplification and next-generation sequencing of input sequences from the isolated genomic DNA showed majority of library fragments were recovered ( Fig. 2b ), and good correlation between replicate experiments ( Fig. 2c ). We then analyzed the screening data based on all candidate SNP-containing fragments, irrespective of reference or alternative allele, by testing whether fragment abundance differed significantly between pre- and post- replicates using the negative binomial model implemented by MAGeCK 30 and identified 5,886 candidate SNP-containing silencers based on the cutoff of the false discovery rate (FDR) below 0.01. NCREs, such as enhancers and silencers, regulate gene expression in a tissue-specific manner 31 – 33 . To test whether the effects of SNPs on silencers function in a similar tissue-specific manner, we repeated the silencer screen with the same ReSE_SNP library in HepG2 cells, a widely used and extensively characterized hepatoblastoma cell line, in three biological replicates. The screening replicates were in good correlations and with sufficient library representation ( Fig. 2b ). We found 12,479 significantly enriched SNP-containing silencers in HepG2 cells (FDR < 0.01). Similar to what was observed before 12 , only around 7.4% of the silencers identified in HepG2 cells (929 silencers) were also active in K562 cells, suggesting most silencers are tissue-specific ( Fig. 2d ). Download figure Open in new tab Figure 2. Identification of silencer elements with GWAS variants. (a) Outline of ReSE_SNP screening experiment. The oligonucleotide array is designed to contain both allelic nucleotide bases of the SNP regions with a sliding window. PCR amplified oligo pool was cloned into ReSE screening plasmid. ReSE screening was carried out, and then statistical analysis was performed to identify allelic silencer differential activities exerted by the SNPs. (b) Coverage of ReSE_SNP library complexity in ReSE screening samples. (c) Pearson correlation between biological replicates of the ReSE_SNP libraries (pre: initial screening cell population; post: survived cells after dimerizer addition, input: constructed ReSE plasmid library) isolated from K562 cells. (d) Venn diagram of SNP-containing silencers identified in K562 and HepG2 cells. MAGecK-RRA algorithm was used to identify the significant hits with a cutoff FDR < 0.01. (e) Luciferase assays to validate the repressive activity of SNP-containing silencers identified from K562 cells with the cutoff FDR < 0.01. Silencer regions were cloned by PCR from the input plasmid library DNA, and then inserted upstream of the promoter of the luciferase reporter plasmid pGL4.53. The empty pGL4.53 plasmid was used as the control for baseline luciferase activity. The luciferase and renilla plasmid were co- transfected into K562 cells using electroporation. The y axis represents the percentage of luciferase activity compared to pGL4.53 empty plasmids in the respective cells (n = 3 biological independent samples; bars show mean value ± s.e.m; *P < 0.05, **P < 0.01, calculated using two-tailed unpaired t-test). (f) Luciferase assays to validate the repressive activity of SNP-containing silencers identified from HepG2 cells with the cutoff FDR < 0.01. Different colors indicate SNP- containing silencers from the top (in red), bottom (in blue), or random positions (in yellow) of the list containing significant potential silencers and ranked by FDR. Silencer regions were cloned by PCR from the input plasmid library DNA, and then inserted upstream of the promoter of the luciferase reporter plasmid pGL4.53. The empty pGL4.53 plasmid was used as the control for baseline luciferase activity. The luciferase and renilla plasmid were co-transfected into HepG2 cells using PEI transfection. The y axis represents the percentage of luciferase activity compared to pGL4.53 empty plasmids in the respective cells (n = 3 biological independent samples; bars show mean value ± s.e.m; **P < 0.01, ***P < 0.001, ****P < 0.0001, calculated using two-tailed unpaired t-test). To assess whether the FDR threshold of 0.01 we applied to filter and define silencers is stringent, the silencer activities of 8 top-ranked candidate silencers and 14 randomly selected candidate silencers above the FDR 0.01 threshold in K562 cells were assessed by luciferase reporter assay. Four of eight top-ranked candidate silencers showed reduced luciferase activity significantly ( Fig. 2e ). To validate the repressive activity of the identified silencers in HepG2 cells, we tested the 4 top-ranked, 4 bottom- ranked and 5 random-selected silencer candidates from the significant hits ranked by FDR with the threshold of 0.01 by luciferase reporter assay. They all significantly reduced the luciferase expression ( Fig. 2f ). These data indicate that the ReSE screening system can reliably identify silencers in different tissue contexts. Signatures of GWAS variants impacting silencer functions Non-coding GWAS variants contribute to phenotypic effects, possibly by altering the function of regulatory elements. Accumulating evidence showed that GWAS variants within enhancers and insulators may contribute to pathogenesis 34 , 35 . We then further analyzed how the GWAS variants cause the alteration of silencer function based on the ReSE_SNP screening results. We developed a statistical framework ( Methods ) to identify variants exerting differential allelic silencer activity, i.e. loss-of-silencer- function (LOSF) and gain-of-silencer-function (GOSF) SNPs relative to the reference allele ( Fig. 3a ). Briefly, the difference between ranking orders of two alleles (effect or non-effect allele) assigned by the first round MAGeCK testing were calculated and then sorted. The ranking differences of paired allele fragments were normalized by quantile normalization, which is assumed to follow the uniform distribution except for the variants that cause a significant difference between the same testing region harboring different alleles. This ranking-difference test was performed using the modified robust rank aggregation ɑ-RRA approach to filter out regions without silencer activity. We performed this allelic-silencer differential activity analysis on the ReSE screen dataset and identified 84 LOSF SNPs and 90 GOSF SNPs in K562, and 34 LOSF SNPs and 33 GOSF SNPs in HepG2. Similar to the mechanisms underlying the disruption of enhancer function by trait-associated SNPs 34 , 35 , we hypothesize that blood cell trait-associated SNPs affect silencer function via the disruption of repressor TF motifs by the alternative allele, resulting in loss of repressor TF binding and consequently upregulation of the target gene ( Fig. 1a , Fig. 3b-d ). For the 84 LOSF SNPs exerting allelic variation of silencer functions, the alternative allele fragments show significantly decreased silencer activity compared with the reference alleles based on the ReSE_SNP screening results. We performed the TF motif matching around the LOSF SNPs and identified the most significant TFs, which might show differences between alternative and reference alleles based on the PWM score calculated by the SNP2FBS tool 36 . Most LOSF variants significantly disrupt one or two specific TF binding sites within the same candidate silencer region ( Fig. 3b ), and most variants are located in intron or intergenic regions ( Fig. 3d ). The significantly enriched motifs include ESR2, Erg and RFX5 ( Fig. 3c ). The Erg TF can inhibit the activity of the IL-8 promoter in endothelial cells 37 and RFX5 TF has been shown to repress collagen gene expression 38 . Download figure Open in new tab Figure 3. Distinct patterns of silencer-affecting GWAS variants. (a) Schematic statistical framework of the rank difference test to identify GWAS SNPs conferring differential activity of allelic silencers. (b) Number of LOSF variants affecting a particular number of transcription factor binding sites (TFBS) matched by SNP2TFBS tool. The x axis indicates the number of TFBS affected by the LOSF variants within the same tested region, and the y axis indicates the number of variants within the indicated category. (c) The enriched TFBS overlapping LOSF SNPs. The x axis indicates the TFBS, and the y axis indicates the ratio of the observed frequency of TFBS falling into LOSF SNPs compared with the expected frequency. The color indicates the significant level of TFBS enrichment in LOSF SNPs. (d) Genomic distribution of LOSF SNPs into different categories regions (UTR5, UTR3, downstream, intergenic, intronic, ncRNA_intronic, upstream). (e) Number of GOSF variants affecting a particular number of transcriptional factor binding sites (TFBS) matched by SNP2TFBS tool. The x-axis indicates the number of TFBS affected by the GOSF variants within the same tested region, and the y axis indicates the number of variants within the indicated category. (f) The enriched TFBS overlapping GOSF SNPs. The x axis indicates the TFBS, and the y axis indicates the ratio of the observed frequency of TFBS falling into GOSF SNPs compared with the expected frequency. The color indicates the significant level of TFBS enrichment in GOSF SNPs. (g) Genomic loci distribution of GOSF SNPs into different categories regions (UTR5, UTR3, intergenic, intronic, ncRNA_exonic, ncRNA_intronic, upstream). For the case of GOSF, an alternative SNP allele could form a repressor TF binding motif that does not exist in the reference allele, thereby functionally creating a de novo silencer, leading to decreased target gene expression ( Fig. 1a , Fig. 3e-g ). For the 90 GOSF SNPs in K562, we analyzed the PWM scores of TF motifs overlapping the SNPs and identified TFs like Erg, FLI1, Ets1, REST, and Spi1 (P-value < 0.005). The Erg, FLI1 and Ets1 belong to the same Ets family factors, and play the transcriptional repressor functions in angiogenesis 39 . REST is a known repressor in neurogenesis and neuronal differentiation 40 , and Spi1 was reported to play the role of transcriptional repressors in diverse biological processes 41 . In addition, GATA4 and GATA1 (P-value < 0.01) were also identified, which function as transcriptional repressors in the hematopoietic development process 42 ( Fig. 3f ). Similarly, most GOSF SNPs only disrupt one TF binding site and enriched in intron and intergenic regions ( Fig. 3e - 3g ). Combined, these results confirm the differential allelic activity of the tested SNP fragments and suggest the potential mechanisms through which GWAS-identified SNPs affect gene expression and contribute to disease. Link functional silencer variants to target genes and functions The major challenge to understanding how the GWAS variants contribute to phenotype is to link a functional variant to its endogenous targeted genes and to the cell type associated with the phenotype. To study the functional impact of the putative silencer-affecting GWAS variants, we selected GWAS variant rs4808806, which was prioritized from the allelic-silencer activity differential analysis based on ReSE_SNP screening data in K562. The fine-mapping analysis identified rs4808806 as a putative causal variant associated with two blood traits: RBC distribution width (RDW) and hematocrit (HCT), with the posterior probability (PP) of 0.02 and 0.018 respectively ( Fig. 4a ). The RDW test indicates the difference in size between the smallest and largest red blood cells within the same sample and HCT refers to the number of red blood cells per 100 ml of blood. When analyzed with the lineage-specific ATAC-seq data of 13 primary blood cell types in the hematopoiesis hierarchy, rs4808806 resides within the GMP- and Mono-specific accessible chromatin region, possibly a putative endogenous silencer element located in the intron of ELL gene ( Fig. 4b ). To link rs4808806 to its target gene, we checked the cis -eQTL data associated with rs4808806 in whole blood tissue. It indicates that rs4808806 is associated with ELL gene upregulation (P-Value = 3.6 e-23). In addition, we also identified all possible related genes whose promoters are in close proximity to rs4808806 locus based on the promoter-capture Hi-C data (genes UPF1, COPE, GDF1, UBA52, HOMER3-AS and HOMER3 ) ( Fig. 4c ). We first validated the allelic silencer activities using luciferase assays. The fragments containing both alleles were PCR amplified from input ReSE plasmid library DNA and cloned into the luciferase reporter vectors. Site-directed mutagenesis was performed if one of the alleles couldn’t be retrieved using the custom primers incorporating expected mutations ( Methods ). Only the reference allele fragment related to the rs4808806 (rs4808806_C ) located on the right side of the SNP (as illustrated in Fig. 1d ) showed silencer activity ( Fig. 4d ), indicating the core motif of the reference allele silencer contains the far-left region of the fragment. In contrast, the alternative allele rs4808806 (rs4808806_G) abolished the silencer activity of the reference allele ( Fig. 4d ). These tested regions related to rs4808806, however, did not show any enhancer activity based on the luciferase assays ( Fig. 4e ). Download figure Open in new tab Figure 4. The SNP rs4808806 affect silencer activity that may link to blood trait RDW. (a) Schematic of the human hematopoietic hierarchy. Mono, monocyte; HSC, hematopoietic stem cell; Ery, erythroid; Mega, megakaryocyte; CD4, CD4+ T lymphocyte; CD8, CD8+ T lymphocyte; B, B lymphocyte; NK, natural killer cell; mDC, Myeloid dendritic cell; pDC, Plasmacytoid dendritic cell; MPP, multipotent progenitor; LMPP, lymphoid-primed multipotent progenitor; CMP, common myeloid progenitor; CLP, common lymphoid progenitor; GMP, granulocyte-macrophage progenitor; MEP, megakaryocyte–erythroid progenitor. Fine-mapping identified rs4808806 as a putative causal variant for RBC distribution width (PP: 0.02) and hematocrit (PP: 0.018). (b) Normalized ATAC-seq profiles of 13 primary blood cell types surrounding the rs4808806 locus. The SNP rs4808806 is associated with RBC distribution width (PP: 0.02) and hematocrit (PP: 0.018). It resides within the GMP- and Mono-specific accessible chromatin region located in the intron of the ELL gene. (c) Normalized chromatin interaction strength between the SNP rs4808806 and promoter regions of nearby genes in 8 primary red blood cell types. Mon, monocytes; Neu, neutrophils; Mac0–2, Macrophages M0, M1, M2; EndP, endothelial precursors; MK, megakaryocytes; Ery, erythroblasts. The significant promoter-capture Hi-C loops linked to genes ( UPF1, COPE, GDF1, UBA52, HOMER3-AS and HOMER3 ) are shown. The height of loops indicates relative interaction strength between rs4808806 and promoters of target genes. (d) Luciferase assays to determine the effects of the SNP rs4808806 on the silencer activity. The six SNP-containing fragments from the ReSE screening library related to rs4808806 were cloned by PCR into the silencer reporter plasmid pGL4.53 containing a PGK promoter. The empty pGL4.53 plasmid was used as the control for the baseline luciferase activities. The y axis represents the relative unit of luciferase activity compared to that of pGL4.53 empty plasmid (n = 3 independent biological samples; bars show mean ± s.d.; ****P < 0.0001, **P < 0.01, calculated using two-tailed unpaired t-test). (e) Luciferase assays to determine the effects of the SNP rs4808806 on the enhancer activity. The six SNP-containing fragments from the ReSE screening library related to rs4808806 were cloned by PCR into the enhancer reporter plasmid pGL4.23 containing a minimal promoter. The empty pGL4.23 plasmid was used as the control for the baseline luciferase activities. The y axis represents the relative unit of luciferase activity compared to that of pGL4.23 empty plasmid (n = 3 independent biological samples; bars show mean ± s.d.; **P < 0.01, *P < 0.05, calculated using two-tailed unpaired t-test). We also studied another loss-of-function SNP affecting the silencer activity related to rs10758656 , which is identified as a putative causal variant for mean corpuscular hemoglobin (PP: 0.999), mean corpuscular volume (PP: 0.999), RBC distribution width (PP: 0.734) and red blood cell count (PP: 0.079) ( Fig. 5a ). The rs10758656 resides within the CMP-, MEP- and Ery-specific accessible chromatin region within the intron of RCL1 gene and overlaps with the GATA family TF motif predicted by JASPAR database ( Fig. 5b ). The GATA family TFs were reported to be transcriptional repressors in hematopoietic cells. The cis-eQTL data indicate that rs10758656 correlates to the gene RCL1 down-regulation, whilst on the other hand associates with genes PLPP6 and PLGRKT up-regulation. Based on the promoter-capture Hi-C loops, the rs10758656 locus has a strong physical interaction with the promoter of gene JAK2 in megakaryocytes and erythroblasts ( Fig. 5c ). Gene JAK2 encodes cytoplasmic tyrosine kinases that mediate signaling from cytokine receptors to the cell nucleus and are vital in the regulation of erythropoiesis. Mutations on JAK2 were strongly associated with elevated hemoglobin, named polycythemia vera 43 . To test the effect of rs10758656 on the silencer function, we performed luciferase reporter assays on all the related regions. Interestingly, all tested regions showed silencer activity ( Fig. 5d ), except for the fragment where the SNP located on the left side of the testing region (as illustrated Fig. 1d ). These data suggest that the silencer motif probably located in the shared regions of the six tested fragments, and the alternative allele rs10758656 (rs10758656 _ G) only disrupted the silencer activity in the presence of the far-right sequences. Again, all these 6 regions did not show any enhancer activity ( Fig. 5e ). Download figure Open in new tab Figure 5. The SNP rs10758656 affect the silencer activity that may affect blood trait RBC. (a) Schematic of the human hematopoietic hierarchy. Mono, monocyte; HSC, hematopoietic stem cell; Ery, erythroid; Mega, megakaryocyte; CD4, CD4+ T lymphocyte; CD8, CD8+ T lymphocyte; B, B lymphocyte; NK, natural killer cell; mDC, Myeloid dendritic cell; pDC, Plasmacytoid dendritic cell; MPP, multipotent progenitor; LMPP, lymphoid-primed multipotent progenitor; CMP, common myeloid progenitor; CLP, common lymphoid progenitor; GMP, granulocyte-macrophage progenitor; MEP, megakaryocyte-erythroid progenitor. (b) Normalized ATAC-seq profiles of 13 primary blood cell types surrounding the SNP rs10758656 locus. Fine-mapping identified rs10758656 as a putative causal variant for mean corpuscular hemoglobin (PP: 0.999), mean corpuscular volume (PP: 0.999), RBC distribution width (PP: 0.734), and red blood cell count (PP: 0.079). It resides within the CMP-, MEP- and Ery-specific accessible chromatin region located in the intron of the RCL1 gene. The SNP rs10758656 overlaps with motifs of GATA1/2/3/4/5/6, TRPS1, KLF13, KLF17, and GLI3, which are predicted by the JASPAR database. (c) Normalized chromatin interaction strength between the SNP rs10758656 and promoter regions of nearby genes in 8 primary red blood cell types. Mon, monocytes; Neu, neutrophils; Mac0–2, Macrophages M0, M1, M2; EndP, endothelial precursors; MK, megakaryocytes; Ery, erythroblasts. The significant promoter-capture Hi-C loops linked to genes ( JAK2, CD274 and KIAA1432 ) are shown. The height of loops indicates relative interaction strength between rs10758656 and promoters of target genes. (d) Luciferase assays to determine the effect of the SNP rs10758656 on the silencer activity. The six SNP-containing fragments from the ReSE screening library related to rs10758656 were cloned by PCR into the silencer reporter plasmid pGL4.53 containing a PGK promoter. The empty pGL4.53 plasmid was used to control the baseline luciferase activities. The y axis represents the relative unit of luciferase activity compared to that of pGL4.53 empty plasmid (n = 3 independent biological samples; bars show mean ± s.d.; ***P < 0.001, ****P < 0.0001, calculated using two- tailed unpaired t-test). (e) Luciferase assays to determine the effects of the SNP rs10758656 on the enhancer activity. The six SNP-containing fragments from the ReSE screening library related to rs10758656 were cloned by PCR into the enhancer reporter plasmid pGL4.23 containing a minimal promoter. The empty pGL4.23 plasmid was used as the control for the baseline luciferase activities. The y axis represents the relative unit of luciferase activity compared to that of pGL4.23 empty plasmid (n = 3 independent biological samples; bars show mean ± s.d.; *P < 0.05, calculated using two-tailed unpaired t- test). Collectively, all the identified SNPs related to silencer activities overlap with 842 eQTLs associated with genes related to whole blood and have physical interactions with promoters of 164 genes, which may explain their association with hematopoietic phenotypes. Discussion Functional characterization of non-coding GWAS variants is key to deciphering the molecular mechanism of population trait diversity and disease pathogenesis. In recent years, most systematic studies on non-coding variants focus on the well-characterized cis-regulatory elements like promoters and enhancers, overlooking the effects of non- coding variants residing in the silencer elements. We systematically studied the GWAS variants affecting silencer activities via a high-throughput ReSE silencer screening. In total 14,720 fine-mapped causal non-coding SNPs associated with 15 diverse blood traits were tested. The ReSE screening system reliably identified the candidate SNP- containing silencers. We developed a new statistical framework to identify the GWAS variants conferring silencer allelic differential activities. Characterization of the signatures enriched around the loss-of-function or gain-of-function silencer variants elucidated that the tissue-specific repressor TFs are associated with the SNP-related silencer function. We validated the silencer-affecting GWAS variants and deciphered the molecular mechanism of inheritable variants that can contribute to the regulation of erythropoiesis. There are some considerations when interpreting the high-throughput screening results from the ReSE system for future silencer studies. First, this high-throughput screen approach is intrinsically noisy; multiple biological replicates are recommended to mitigate the false positive discovery of silencers. Second, the SNP-containing silencers usually function in a tissue-specific manner. The ReSE_SNP library was designed based on the fine-mapped causal variants related to blood cell traits overlapped with blood cell accessible chromatin regions, and the experiments were performed in K562 cells, which only resemble certain blood cell linages. Therefore, many tissue-dependent silencer activities probably were not captured. The GWAS studies have identified numerous non-coding variants contributing to phenotypes and traits. Comprehensively characterizing the regulatory functions, including the putative silencer functions of these non-coding variants, will guide researchers in discovering drug targets or developing gene therapy to overcome genetic diseases. This screening approach can be combined with CRISPR/Cas9- mediated genome editing screening like CRISPR-activation, CRISPR interference, CRISPR-mediated deletion, base editing, or prime editing to interrogate cellular phenotype impacts of silencer variants 44 , 45 . Methods Cell culture K562 cells were cultured in RPMI 1640 + L-Glutamine (Gibco), 10% fetal bovine serum (Biowest), and 1% Penicillin-Streptomycin (Gibco). HepG2 cells were cultured in DMEM, 10% FBS and Antibiotic-Antimycotic. Cell density and culture conditions were maintained according to the ENCODE Cell Culture Guidelines. Library Design and Construction The initial SNPs list were retrieved from a trans-ethnic meta-analysis of 15 blood cell traits (RBC count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, MCH concentration, RBC distribution width, WBC count, neutrophils, monocytes, lymphocytes, basophils, eosinophils, platelet count, and mean platelet) in 746,667 individuals from five different ancestries (European ancestries, South Asian ancestries, Hispanic ancestries, East Asian ancestries, and African ancestries) generated by the Blood Cell Consortium (BCX) consortium ( https://www.mhi-humangenetics.org/en/resources/ ). All the SNPs from fine-mapped 95% credible sets for 15 blood traits in trans- and sub-ancestry were first merged and deduplicated. The unique GWAS variants list was annotated by VEP and only retained the SNPs falling into the noncoding regions by excluding the coding or splice site variants. For each variant, six 130nt constructs were extracted based on hg19 genome assembly which tile the SNP sites in the position 1/4 (left), 1/2 (middle) and 3/4 (right) of total length and incorporate both allele nucleotide bases in the SNP sites. For ReSE SNP oligo library, the insert fragment containing both alleles of the SNP were designed to be compatible with the screening lentiviral vector pLenti-FKBP-delCasp9-Puro as follows: ACACGACGCTCTTCCGATCT-[130 nt insert]- AGATCGGAAGAGCACACGTC. The oligo pools were then ordered from GenScript. The same ReSE screen lentivirus vector pLenti-FKBP-delCasp9-Puro from our previous study 12 was digested with BsmBI enzyme and gel-purified. The PCR amplified oligo library were then inserted into the digested plasmids, 15 bp upstream of the EF-1α using Gibson Assembly. The assembly mix was made using 50 ng of insert DNA, 50 ng of digested plasmids and 10 μl of 2× Gibson Assembly Master Mix to produce a final volume of 20 μl. The assembly mix was incubated at 50 °C for 60 min, then 2 μl of the mix was electroporated into 25 µl of Endura electrocompetent cells to test the transformation efficiency. The electroporation was scaled to reach approximately 160,000 colonies, which were plated on four 245-mm Petri dishes with 100 μg ml − 1 carbenicillin. Colonies were then scraped and plasmid DNA extracted using the Qiagen Maxiprep Kit. Lentivirus production and infection and ReSE screening The 293T cells were grown in five T175 flasks at 50% confluency before transfection. For each flask of 293T cells grown in 25 ml of fresh medium, 15 μg of library plasmids, 10 μg of psPAX2, 5 μg of pCMV-VSV-G and 90 μl of X-tremeGENE 9 DNA Transfection Reagent were mixed in 1 ml of serum-free medium and used for transfection. Fresh medium was added the day following transfection. Media supernatant containing virus particles was collected on the second and third days after transfection, pooled and further concentrated by ultracentrifugation. Virus titer was then determined by making serial (10 - 3 to 10 - 10 ) dilutions of 4 μl of frozen virus supernatant in media containing 8 μg ml - 1 of polybrene to infect 293T cells. Two days after infection, cells were selected with 2 μg ml - 1 puromycin for an additional 7 d. Virus titer was then calculated based on the survival colonies and the related dilution. K562 cells and HepG2 cells were then infected with the same virus library at multiplicity of infection (MOI) of 0.3 by spin-infection. For spin-infection, 3 × 10 6 cells in each well of a 12-well plate were infected in 1 ml of medium containing 8 μg ml - 1 of polybrene. In total, four plates were used for each infection to analyze a total of 1.5 × 10 8 cells. Two days after infection, cells were selected by 2 mg ml - 1 of puromycin for a further 5 d. For each biological replicate experiment of both K562 and HepG2 cells, the infection was repeated and cells were infected with lentivirus from the same pool of virus containing the same library content. ReSE silencer screening was performed as described previously 12 . Library sequencing and analyses Genomic DNA containing the ReSE lentivirus inserts was amplified by PCR using Illumina PCR primers 1.0 and 2.0. For each 100 μl PCR reaction, 10 μg of genomic DNA, 20 μl of 5× Phusion HF buffer, 2 μl of 10 mM deoxynucleotide triphosphate, 2.5 μl of Phusion polymerase, 5 μl of 25 μM 1.0 primer and 5 μl of 25 μM 2.0 primer were used. For each treatment sample, 16 reactions were prepared and pooled. PCR procedures were: 98 °C for 30 s, 20 cycles of 98 °C for 10 s, 65 °C for 30 s and 72 °C for 30 s, and 72 °C for 5 min. PCR products were then size-selected and purified. Final products were sequenced on either an Illumina MiSeq or Hiseq4000 platform. Cutadapt was used to extract the unique 130 nt SNP-containing sequences (5’ - ACACGACGCTCTTCCGATCT; 3’ - AGATCGGAAGAGCACACGT). Trimmed reads were then mapped to the indexed references with the SNP sites masked by ‘N’ generated by Bowtie2 based on the oligo library designs. Custom script was used to extract the SNP nucleotide information based on the CIGAR string generated in BAM format and assign reads to respective alleles followed by quantification. The derived read counts tables were first median-normalized to adjust for the effect of library sizes and read count distributions. The variance of read counts was then estimated and a negative binomial model was used to test whether fragment abundance differed significantly between post-apoptosis-induction and control replicates by MAGeCK. P values were calculated from the negative binomial model using a modified robust ranking aggregation algorithm. FDR was then computed from the empirical permutation P values using the Benjamini–Hochberg procedure. Because fold enrichments are only semi-quantitative, fragments with FDR < 0.01 were considered as significant hits for downstream analyses and the list of silencers was sorted based on FDR values from low to high. For differential allelic silencer activity assessment, we devised a novel statistical framework, named “Rank difference RRA testing” to analyze the ReSE screen data. Briefly, the ranking difference between the effect and non-effect SNP constructs was extracted from the RRA analysis by MAGecK. The ranking difference was further sorted and converted to the quantile ranks (percentiles). Given the null distribution assumption of uniformity for the quantile ranks, the modified RRA method α-RRA was performed to assess the significance of ranking difference between two allele constructs. The permutation test was performed to compute the P-value assigned to the significant scores. The FDR was further computed from the empirical permutation P-values using the Benjamini-Hochberg procedure for multiple testing correction. Luciferase assay Candidate silencer sequences containing SNPs were amplified with primers containing a homologous arm from the input plasmid library or genomic DNA of K562 cells. These fragments were then inserted in front of the promoters of the luciferase plasmids pGL4.23 (Promega, with some modification on the cloning sites, detecting the enhancer activity) and pGL4.53 (Promega, detecting the silencer activity) by using NEBuilder HiFi. Site-directed mutagenesis assays were performed if another allele- containing fragment couldn’t be retrieved from the PCR amplification. Cells were then co-transfected with the pRL-CMV Renilla reporter plasmid and the pGL4.53 or pGL4.23 plasmid with the silencer sequences inserted. The luciferase assay was performed using the Dual-Luciferase Reporter Assay System from Promega according to manufacturer protocol. 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Share Genetic variants within silencer elements contribute to human blood cell traits Minkang Tan , Jan van Weerd , Alper Yavas , Baoxu Pang bioRxiv 2025.01.13.632723; doi: https://doi.org/10.1101/2025.01.13.632723 Share This Article: Copy Citation Tools Genetic variants within silencer elements contribute to human blood cell traits Minkang Tan , Jan van Weerd , Alper Yavas , Baoxu Pang bioRxiv 2025.01.13.632723; doi: https://doi.org/10.1101/2025.01.13.632723 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetics Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41913) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13372) Ecology (19889) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40365) Molecular Biology (17163) Neuroscience (88540) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15130) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9818) Zoology (2269)