AbstractHere we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants.

摘要在这里，我们提供了来自30个供体的幼稚CD4 T细胞（nCD4）的全面HiChIP数据集，并鉴定了与HiChIP接触的基因型依赖性和/或等位基因特异性变异相关的QTL，这些变异定义了活性调节区（IQTL）之间的环。我们观察到IQTL与先前定义的eQTL和组蛋白QTL之间存在大量重叠，并且富集了精细定位的QTL和GWAS变体。

Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the RNASET2 locus).

此外，我们描述了nCD4 IQTL的一个独特子集，其中nCD4中染色质接触的显着变化被转化为CD4 T细胞记忆亚群中显着的eQTL趋势。最后，我们将连通性QTL定义为IQTL，其与广泛基因组区域（例如RNASET2基因座中的GWAS SNP）上染色质接触的一致基因型依赖性变化显着相关。

Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying previously uncharacterized QTLs linked to cell-specific gene expression and connectivity..

我们的结果证明了染色质接触作为QTL定位的补充方式的重要性，以及它们在识别与细胞特异性基因表达和连接性相关的先前未表征的QTL方面的能力。。

IntroductionUsing genotype to predict phenotypic responses to perturbations, whether at the molecular, cellular or organismal level, is the ultimate challenge of personalized medicine. Genome-wide association studies (GWAS) link common genetic variants to measurable phenotypes and disease susceptibility1.

。全基因组关联研究（GWAS）将常见的遗传变异与可测量的表型和疾病易感性联系起来1。

The majority of these GWAS variants are, however, present in the noncoding DNA sequences and are inherited as dense haploblocks2, hence identifying functional or causal GWAS SNPs becomes challenging3. Expression quantitative trait loci (eQTL) studies either in bulk cells or in single-cell populations have quantified the effect of noncoding cis variants on gene expression for different tissues and cell types4,5,6,7,8,9,10,11,12,13,14,15.

然而，这些GWAS变体中的大多数存在于非编码DNA序列中，并且作为致密的单倍体2遗传，因此鉴定功能性或因果性GWAS SNP变得具有挑战性3。在体细胞或单细胞群体中的表达数量性状基因座（eQTL）研究已经量化了非编码顺式变体对不同组织和细胞类型的基因表达的影响4,5,6,7,8,9,10,11,12,13,14,15。

However, high degree of linkage disequilibrium (LD) among the derived eQTLs (bulk or single-cell) or GWAS SNPs make identifying the putative causal variants difficult. High-throughput functional validation approaches16,17,18,19 as well as statistical approaches20,21,22,23,24,25,26,27,28,29,30,31 were developed to identify or prioritize putatively causal variants for different diseases and cell types.

然而，衍生的eQTL（体细胞或单细胞）或GWAS SNP之间的高度连锁不平衡（LD）使得鉴定推定的因果变异变得困难。开发了高通量功能验证方法16,17,18,19以及统计方法20,21,22,23,24,25,26,27,28,29,30,31，以识别或优先考虑不同疾病和细胞类型的推定因果变异。

Another set of approaches overlap these eQTLs or GWAS SNPs with cell type-specific maps of regulatory elements to further annotate their relation to gene expression and disease risk32,33,34,35,36.In parallel, breakthroughs in capturing the 3D genome structure using various chromatin conformation capture (3C) technologies such as Hi-C37,38 and its variants including Promoter Capture Hi-C39, PLAC-seq/HiChIP40,41 led to genome-wide maps of cell-type-specific chromatin interactions/loops across many cell types and conditions42,43.

另一组方法将这些eQTL或GWAS SNP与调控元件的细胞类型特异性图谱重叠，以进一步注释它们与基因表达和疾病风险的关系32,33,34,35,36。同时，使用各种染色质构象捕获（3C）技术捕获3D基因组结构的突破，如Hi-C37,38及其变体，包括启动子捕获Hi-C39，PLAC-seq/HiChIP40,41，导致了跨许多细胞类型和条件的细胞类型特异性染色质相互作用/环的全基因组图谱42,43。

These studies demonstrated the importance of cell-type-specific physical proximity between regulatory elements (e.g.

这些研究证明了调节元件之间细胞类型特异性物理接近的重要性（例如。

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Default model: considers both genotype-dependent variation of HiChIP contact counts and the variation of allele-specific reads from heterozygous donors. We have used the symbol FDRDef to denote the significance values generated from this model.

默认模型：考虑HiChIP接触计数的基因型依赖性变异和杂合供体等位基因特异性读数的变异。我们使用符号FDRDef来表示从该模型生成的显着性值。

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Genotype dependent model (RASQUAL command line option --population-only): uses only the genotype-dependent variation of HiChIP contact counts but does not consider the allele-specific variation of HIChIP reads. We have used the symbol FDRPop to denote the significance values from this model.

基因型依赖模型（RASQUAL命令行选项-仅限人群）：仅使用HiChIP接触计数的基因型依赖性变异，但不考虑HiChIP读数的等位基因特异性变异。我们使用符号FDRPop来表示该模型的显着性值。

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Allele-specific model (RASQUAL command line option --as-only): uses only the allele-specific variation of HiChIP reads from heterozygous donors to compute the statistical significance values which we denote by the symbol FDRAS.

等位基因特异性模型（RASQAL命令行选项-仅限）：仅使用杂合供体的HiChIP读数的等位基因特异性变异来计算我们用符号FDRAS表示的统计显着性值。

In addition to RASQUAL’s allele-specific model, we have also performed a paired t-test between the allele-specific reads for individual heterozygous SNPs, and denoted the resulting p-values by the symbol PAS. Reason for employing three different models of RASQUAL was to assess the contribution of both genotype dependent and allele-specific variations of HiChIP contacts and reads.

除了RASQUAL的等位基因特异性模型外，我们还对单个杂合SNP的等位基因特异性读数进行了配对t检验，并用符号PAS表示了所得的p值。。

Initially we selected the iQTL - loop pairs which were significant by the default model (FDRDef < 0.05). However, lots of entries significant by the default model were not significant in either genotype dependent or allele-specific models of RASQUAL. In view of this, to eliminate the potential false positive entries, we selected only those SNP - loop pairs which satisfy all of the following conditions:.

最初，我们选择了默认模型下显着的iQTL循环对（FDRDef<0.05）。但是，在默认模型下显着的许多条目在RASQUAL的基因型依赖性或等位基因特异性模型中均不显着。鉴于此，为了消除潜在的假阳性条目，我们仅选择满足以下所有条件的SNP循环对：。

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SNPs should have all three genotypes (reference homozygous or 0|0, heterozygous or 0|1, and alternate homozygous or 1|1) present in at least two donors.

SNP应具有至少两个供体中存在的所有三种基因型（参考纯合子或0 | 0，杂合子或0 | 1，替代纯合子或1 | 1）。

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If the current SNP-loop pair is not significant by the genotype dependent model (FDRPop >= 0.05), allele-specific reads should produce statistical significance by the paired t-test analysis (PAS < 0.05). Note that we did not consider the statistical significance in allele-specific model of RASQUAL (FDRAS) because we observed that the allele-specific model of RASQUAL often returns false positive entries without any specific trends of the allele-specific reads and these trends get rejected by the paired t-test.

如果当前的SNP环对在基因型依赖模型中不显著（FDRPop>=0.05），则等位基因特异性读数应通过配对t检验分析产生统计学意义（PAS<0.05）。请注意，我们没有考虑RASQUAL等位基因特异性模型（FDRAS）的统计显着性，因为我们观察到RASQUAL等位基因特异性模型通常返回假阳性条目，而没有等位基因特异性读数的任何特定趋势，并且这些趋势被配对t检验拒绝。

In view of this, we used the paired t-test as a criterion to select the SNP-loop pairs significant by the allele-specific reads, even if they are not declared significant by the RASQUAL’s allele-specific model..

有鉴于此，我们使用配对t检验作为标准来选择等位基因特异性读数显着的SNP环对，即使它们没有被RASQUAL的等位基因特异性模型声明为显着。。

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If the SNP-loop pair is significant by the genotype dependent model (FDRPop < 0.05) and the allele-specific read trend is not significant by the paired t-test (PAS >= 0.05), either mean or median of the raw and normalized HiChIP contact counts (normalized by sequencing depths) per genotype should exhibit either strictly ascending or strictly descending trends between three different genotypes, namely reference homozygous, heterozygous and alternate homozygous.

如果SNP环对通过基因型依赖模型显着（FDRPop<0.05），而等位基因特异性读数趋势通过配对t检验不显着（PAS>=0.05），则每个基因型的原始和标准化HiChIP接触计数（通过测序深度标准化）的平均值或中位数应在三种不同基因型之间表现出严格的上升或下降趋势，即参考纯合子，杂合子和替代纯合子。

Otherwise, the entry would be filtered out..

否则，条目将被过滤掉。。

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If the SNP-loop pair is significant only by the paired t-test of allele-specific reads (FDRPop >= 0.05 and PAS < 0.05), allele-specific reads need to follow the same trend as the genotype dependent HiChIP contacts. For example, considering a SNP with two alleles are A and G such that the number of reads for A are higher than those for G, the normalized HiChIP counts for the genotype A|A should also be higher than the genotype G|G.

如果SNP环对仅通过等位基因特异性读数的配对t检验显着（FDRPop>=0.05和PAS<0.05），则等位基因特异性读数需要遵循与基因型依赖性HiChIP接触相同的趋势。例如，考虑到具有两个等位基因的SNP是a和G，因此a的读数高于G的读数，基因型a | a的标准化HiChIP计数也应高于基因型G | G。

Otherwise, those SNP-loop pairs would be filtered out..

否则，这些SNP循环对将被过滤掉。。

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Finally, the SNP-loop pairs significant in the default RASQUAL model and satisfying all of the conditions 1 to 4 are retained.

最后，保留在默认RASQUAL模型中重要且满足所有条件1至4的SNP循环对。

Aggregate peak analysis (APA) of HiChIP and Hi-C loopsWe performed APA using the R package GENOVA108. For the HiChIP data, the background set of HiChIP contacts were derived by merging all the valid read pairs (generated from HiC-pro) from all input 30 donors and applying ICE109 normalization on the merged HiChIP contacts using the FitHiChIP64 pipeline.

HiChIP和Hi-C Loops的聚集峰分析（APA）使用R软件包GENOVA108进行APA。对于HiChIP数据，HiChIP触点的背景集是通过合并来自所有输入30个供体的所有有效读取对（由HiC pro生成）并使用FitHiChIP64管道对合并的HiChIP触点应用ICE109归一化而得出的。

HiChIP loops associated with iQTLs were compared against the union of significant FitHiChIP-L loops from all donors. For a candidate loop, we used the background HiChIP contacts 50kb up- and downstream, thus generating a matrix of 21 × 21 dimension corresponding to 5kb resolution (as suggested in ref.

将与IQTL相关的HiChIP环与来自所有供体的显着FitHiChIP-L环的结合进行比较。对于候选循环，我们使用了上下游50kb的背景HiChIP触点，从而生成了一个21×21维的矩阵，对应于5kb的分辨率（如参考文献所示）。

110). Loops within 150kb – 1mb distance were only considered. For the Hi-C data, we downloaded the hg38 merged CD4 T cell Hi-C data provided in ref. 59 from the link http://bartzabel.ls.manchester.ac.uk/orozcolab/SNP2Mechanism/hic/merged/, performed KR normalization and used as the background Hi-C contacts.Pathway analysisWe used the protein-coding genes having expression > 1 TPM in CD4 Naive cell type (according to the DICE database8) and performed exact overlap with the iQTL loop anchors (5kb bins) to derive the candidate genes for pathway analysis conducted using Metascape111.

110）。仅考虑150kb–1mb距离内的环路。对于Hi-C数据，我们从链接下载了参考文献59中提供的hg38合并的CD4 T细胞Hi-C数据http://bartzabel.ls.manchester.ac.uk/orozcolab/SNP2Mechanism/hic/merged/，执行KR归一化并用作背景Hi-C触点。。

Results of gene ontology analysis with respect to the biological processes were reported.Analysis of published eQTLs for CD4 Naïve and other CD4 T cell subsetsWe downloaded eQTLs for CD4 Naïve and various other CD4 T cell subsets (CD4 Stimulated, Tfh, Th1, Th2, Th17, Th1/17, Treg Memory and Treg Naïve) from the DICE8 database (https://dice-database.org).

报道了有关生物过程的基因本体分析结果。分析已发表的CD4幼稚和其他CD4 T细胞亚群的eQTL我们从DICE8数据库下载了CD4幼稚和其他各种CD4 T细胞亚群（CD4刺激，Tfh，Th1，Th2，Th17，Th1/17，Treg记忆和Treg幼稚）的eQTL(https://dice-database.org)。

We also downloaded the conditional eQTLs (FDR < 0.05) for the CD4 Naive and other CD4 T cell types (Memory CD4, Tfh, Th17, Th1, Th2, Fr-I-nTreg, Fr-II-eTreg and Fr-III-T) from the ImmuNexUT7 databa.

我们还从ImmuNexUT7数据库下载了CD4幼稚和其他CD4 T细胞类型（记忆CD4，Tfh，Th17，Th1，Th2，Fr-I-nTreg，Fr-II-eTreg和Fr-III-T）的条件eQTL（FDR<0.05）。

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CD4 Naïve H3K27ac ChIP-seq peaks: We utilized the aggregate ChIP-seq peaks file obtained across all DICE donors (unpublished data).

CD4幼稚的H3K27ac ChIP-seq峰：我们利用了在所有DICE供体中获得的聚合ChIP-seq峰文件（未发表的数据）。

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CD4 Naïve H3K27ac HiChIP peaks: We merged the valid paired-end HiChIP cis reads within the distance range 10kb – 3mb from all iQTL donors, and applied this merged set of reads to the utility routine PeakInferHiChIP.sh in FitHiChIP64 to call the HiChIP peaks.

CD4幼稚H3K27ac HiChIP峰：我们合并了来自所有iQTL供体的10kb-3mb距离范围内的有效配对末端HiChIP顺式读数，并将该合并读数集应用于FitHiChIP64中的实用程序PeakInferHiChIP.sh以调用HiChIP峰。

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ChIP-seq peaks for various CD4 T cell subsets: We downloaded the ChIP-seq peaks for various CD4 T cell subsets and transcription factors (reference genome: hg19) from the ChIP-Atlas67 database (https://chip-atlas.org/), using the peaks with q-value < 1e-5 (threshold of significance = 50).

(https://chip-atlas.org/)，使用q值小于1e-5的峰（显着性阈值=50）。

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ChIP-seq peaks from ReMap2022 database: We also downloaded ChIP-seq peaks from ReMap DNA binding database88. Specifically, we considered the ChIP-seq peaks marked for CD4 T cells.

来自ReMap2022数据库的ChIP-seq峰：我们还从ReMap DNA结合数据库88下载了ChIP-seq峰。具体而言，我们考虑了标记为CD4 T细胞的ChIP-seq峰。

We used the exact overlap criterion to identify the QTLs (iQTLs or eQTLs) falling within any of these peaks.Overlap of iQTLs with reference transcription factor binding sites and motifsTo identify the TF binding motifs overlapping iQTLs, we employed multiple approaches, as described below:

我们使用精确的重叠标准来识别落在任何这些峰内的QTL（IQTL或eQTL）。IQTL与参考转录因子结合位点和基序的重叠为了鉴定与IQTL重叠的TF结合基序，我们采用了多种方法，如下所述：

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ADASTRA database: We downloaded the Bill Cypher version (5.1.2) of the ADASTRA database71,72 (https://adastra.autosome.org/) having information of various human TF binding sites for different cell lines and cell types, and overlapped these binding sites with the iQTLs.

ADASTRA数据库：我们下载了ADASTRA数据库的Bill Cypher版本（5.1.2）71,72(https://adastra.autosome.org/)具有不同细胞系和细胞类型的各种人类TF结合位点的信息，并将这些结合位点与IQTL重叠。

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De novo TF motif enrichment by FIMO: For each chromosome, we first extracted +/− 20 bp around individual iQTLs (thus extracted 41 bp sequences) and created two different fasta files such that the first fasta file contains the reference alleles of the input iQTLs while the second file contains the alternate alleles.

通过FIMO从头富集TF基序：对于每个染色体，我们首先在单个IQTL周围提取+/-20 bp（因此提取了41 bp序列），并创建了两个不同的fasta文件，使得第一个fasta文件包含输入IQTL的参考等位基因，而第二个文件包含替代等位基因。

Individual fasta files per chromosome were then applied to FIMO69. We used three different motif databases and corresponding reference motifs: 1) cisbp112 (version 1.02, file Homo_sapiens.meme), 2) hocomoco113 (file HOCOMOCOv11_core_HUMAN_mono_meme_format.meme) and 3) jaspar114 (file JASPAR2018_CORE_vertebrates_non-redundant.meme), to identify the motifs.

然后将每个染色体的单个fasta文件应用于FIMO69。我们使用了三个不同的主题数据库和相应的参考主题：1）cisbp112（版本1.02，文件Homo\u sapiens.meme），2）hocomoco113（文件HOCOMOCOv11\u core\u HUMAN\u mono\u meme\u format.meme）和3）jaspar114（文件JASPAR2018\u core\u脊椎动物\u non-redundant.meme），以识别主题。

Motifs with p-value < 1e-5 were considered significant. Statistically significant motifs in at least one database were reported. To plot the motifs, genes with expression > 1 TPM in the CD4 Naïve cell type (according to the DICE8 database) and the most significant motifs (having p-value < 1e-6) were only considered.

p值小于1e-5的基序被认为是重要的。报告了至少一个数据库中具有统计学意义的基序。为了绘制基序，仅考虑在CD4幼稚细胞类型（根据DICE8数据库）中表达>1 TPM的基因和最重要的基序（p值<1e-6）。

Similar procedure was employed to compute the TF motif enrichment of CD4 Naïve eQTLs in the DICE database. Specifically, we used the DICE eQTLs for autosomal chromosomes and considered motifs with p-value < 1e-5 as significant. Fisher’s exact test was used to compute the enrichment (p-value) of TF binding motif overlap between iQTLs and DICE CD4 Naïve eQTLs..

采用类似的程序来计算DICE数据库中CD4幼稚eQTL的TF基序富集。具体而言，我们将DICE eQTL用于常染色体，并认为p值小于1e-5的基序具有重要意义。Fisher精确检验用于计算IQTL和DICE CD4幼稚eQTL之间TF结合基序重叠的富集（p值）。。

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Allele-specific TF binding motifs by AME: Allele-specific motif enrichment analysis was performed by the procedure outlined in various reference studies51,115. We used AME70 from the MEME suite68, and used position weight matrices (PWM) from the above mentioned three motif databases (cisbp, hocomoco and JASPAR) to predict the motif enrichment.

AME的等位基因特异性TF结合基序：通过各种参考研究51115中概述的程序进行等位基因特异性基序富集分析。我们使用MEME套件68中的AME70，并使用上述三个基序数据库（cisbp，hocomoco和JASPAR）中的位置权重矩阵（PWM）来预测基序富集。

We extracted 41 bp sequences (+/− 20 bp) around individual iQTLs (SNPs) for all the autosomal chromosomes together. We used sequences from the effect alleles (alternate alleles) as the test set, and used the shuffled background as the control sequences. Genes with expression > 1 TPM in the CD4 Naïve cell type (according to the DICE database) and motifs with p-value < 1e-6 were only plotted..

我们一起提取了所有常染色体的单个IQTL（SNP）周围的41 bp序列（+/-20 bp）。我们使用来自效应等位基因（替代等位基因）的序列作为测试集，并使用改组背景作为对照序列。仅绘制了CD4幼稚细胞类型中表达>1 TPM的基因（根据DICE数据库）和p值<1e-6的基序。。

Overlap and LD with fine-mapped GWAS SNPsWe downloaded the fine-mapped GWAS SNPs from the CausalDB73 database (http://www.mulinlab.org/causaldb/). Specifically, we downloaded the file credible_set.20220315.tar.gz and selected only the GWAS studies involving various immune diseases. The SNPs reported in the credible sets of corresponding studies were overlapped with the iQTLs and DICE CD4 Naive eQTLs using the fields like rsID, chromosome and SNP position.

重叠和LD与精细映射的GWAS SNPs我们从CausalDB73数据库下载了精细映射的GWAS SNPs(http://www.mulinlab.org/causaldb/)。具体来说，我们下载了文件creatible\u set.20220315.tar.gz，并仅选择了涉及各种免疫疾病的GWAS研究。使用rsID，染色体和SNP位置等字段，在可信的相应研究集中报告的SNP与iQTLs和DICE CD4幼稚eQTL重叠。

We used Fisher’s exact test to compute the enrichment (p-value) of overlap between iQTLs and reference DICE CD4 Naive eQTLs. Linkage Disequilibrium (LD) analysis was carried out using the tools LDmatrix and LDpair of the LDlink web repository (https://ldlink.nci.nih.gov/) and using the European population as the reference.Significance of various overlap analysesOverlap of iQTLs with reference CD4 Naïve T cell eQTLs and Blueprint Histone QTLsAs discussed in Section A, ~64% (5993 out of 9426) iQTLs overlap with either CD4 Naïve T cell eQTLs or Blueprint histone QTLs.

我们使用Fisher精确检验来计算IQTL和参考骰子CD4幼稚eQTL之间重叠的富集（p值）。使用LDlink web repository的工具LDmatrix和LDpair进行连锁不平衡（LD）分析(https://ldlink.nci.nih.gov/)并以欧洲人口为参考。各种重叠分析的意义IQTL与参考CD4幼稚T细胞eQTL和蓝图组蛋白QTL的重叠如A部分所述，约64%（9426个中的5993个）IQTL与CD4幼稚T细胞eQTL或蓝图组蛋白QTL重叠。

To create a null distribution of SNPs, we checked each iQTL associated HiChIP loops (total 2292 loops) and for each loop, sampled the same number of SNPs as the number of iQTLs associated with this loop. For each loop, we considered only the SNPs either overlapping or within +/− 20kb of the loop anchors, for sampling (without replacement) such that the sampled SNPs did not overlap with the list of iQTLs.

为了创建SNP的空分布，我们检查了每个与iQTL相关的HiChIP循环（总共2292个循环），并为每个循环采样了与该循环相关的iQTL数量相同的SNP数量。对于每个环，我们仅考虑重叠或在环锚的+/-20kb内的SNP进行采样（无需替换），以使采样的SNP与IQTL列表不重叠。

Finally, the sampled 9426 SNPs (same number of SNPs as the number of iQTLs) were tested for overlap with the reference CD4 Naïve T cell eQTLs in either DICE or ImmuNexUT databases, or the list of BLUEPRINT histone QTLs. The 2X2 contingency table for the SNPs overlapping with either eQTLs or hQTLs is reported in Supplementary Data 11A.

最后，在DICE或ImmuNexUT数据库或BLUEPRINT组蛋白QTL列表中测试了采样的9426个SNP（与IQTL数量相同的SNP数量）与参考CD4幼稚T细胞eQTL的重叠。补充数据11A中报告了与eQTL或hQTL重叠的SNP的2X2列联表。

The same procedure is a.

相同的程序是a。

1.

1.

Treg eQTLs in ref. 9: We downloaded 3301 Treg eQTLs from the Supplementary Table 2 of the Bossini-Castillo, L. et al. manuscript9, where the assay is mentioned RNA. This is because, the database contains a mixture of eQTL and chromatin QTL. As the eQTLs follow hg38 coordinates, we extracted the rsIDs using custom routines to compare their overlap with iQTLs..

参考文献9中的Treg eQTL：我们从Bossini-Castillo，L。等人的手稿9的补充表2中下载了3301个Treg eQTL，其中提到了RNA。这是因为数据库包含eQTL和染色质QTL的混合物。由于eQTL遵循hg38坐标，因此我们使用自定义例程提取了RSID，以比较它们与IQTL的重叠。。

2.

2.

MPRA prioritized variants (emVars) in ref. 10: We downloaded 313 MPRA validated functional variants (emVars) subject to FDR < 0.1 from the Supplementary Table 3 of the Mouri, K. et al. manuscript10. We also downloaded the PICS fine mapping SNPs (95% credible causal set) from the supplementary table 9 of the Mouri, K.

参考文献10中的MPRA优先变体（emVars）：我们从Mouri，K。等人的手稿10的补充表3下载了313个MPRA验证的功能变体（emVars），受FDR<0.1的影响。我们还从K.Mouri的补充表9下载了PICS精细定位SNP（95%可信因果集）。

et al. manuscript10..

等人手稿10。。

3.

3.

Single cell eQTLs for memory T cells11: We downloaded the single cell eQTLs (FDR < 0.05) from the supplementary table 1 of the Nathan, A. et al. manuscript11. As the eQTLs follow hg38 coordinates, we extracted the rsIDs using custom routines to compare their overlap with iQTLs.

记忆T细胞的单细胞eQTL 11：我们从Nathan，A。等人的手稿11的补充表1下载了单细胞eQTL（FDR<0.05）。由于eQTL遵循hg38坐标，我们使用自定义例程提取了RSID，以比较它们与IQTL的重叠。

4.

4.

Single cell dynamic eQTLs for naïve T cells13: We downloaded the single cell dynamic eQTLs (FDR < 0.05) corresponding to the T_naive cell type from the supplementary table 8 of the Soskic, B. et al. manuscript13.

幼稚T细胞的单细胞动态eQTL 13：我们从Soskic，B。等人的手稿13的补充表8下载了对应于T\u幼稚细胞类型的单细胞动态eQTL（FDR<0.05）。

5.

5.

Single cell eQTLs for CD4 T cell subsets15: We downloaded the single cell eQTLs (FDR < 0.05) for various CD4 T cell subsets from the supplementary table 10 of the Yazar, S. et al. manuscript15.

CD4 T细胞亚群的单细胞eQTLs 15：我们从Yazar，S。等人的手稿15的补充表10下载了各种CD4 T细胞亚群的单细胞eQTLs（FDR<0.05）。

6.

6.

Single cell CD4 T eQTL from sceQTLGen consortium14: We downloaded the CD4 T cell eQTLs from sceQTLGen (https://www.eqtlgen.org/sc/datasets/vanderwijst2018.html) and extracted the significant eQTLs (FDR < 0.05) for CD4 T cells.

来自sceQTLGen联盟的单细胞CD4 T eQTL 14：我们从sceQTLGen下载了CD4 T细胞eQTL(https://www.eqtlgen.org/sc/datasets/vanderwijst2018.html)并提取CD4 T细胞的显着eQTL（FDR＜0.05）。

7.

7.

Single cell eQTLs from DICE database12: We used the single-cell eQTLs for various CD4 T cell subsets published in the DICE database (https://dice-database.org/).

来自DICE数据库的单细胞eQTL 12：我们使用DICE数据库中发布的各种CD4 T细胞亚群的单细胞eQTL(https://dice-database.org/)。

Colocalization analysisWe performed colocalization between eQTL and reference GWAS SNPs as described in our previous study50. We employed the COLOC framework24 in our custom implementation of colocalization (https://github.com/ay-lab/Colocalization). We used significant GWAS variants with association p-value < 5 × 10-8 for colocalization.

共定位分析如我们之前的研究50所述，我们在eQTL和参考GWAS SNP之间进行了共定位。我们在定制的共定位实现中使用了COLOC框架24(https://github.com/ay-lab/Colocalization)。我们使用了显着的GWAS变体，其关联p值<5×10-8用于共定位。

For all eGenes of DICE CD4 Naive eQTL data, we used the eQTL summary statistics, specifically the association p-values, effect size (β), standard error of β, and the minor allele frequency (MAF) for all variants within 1 Mb of TSS of these eGenes. We excluded all variants within the MHC locus (chr6: 28,477,897 - 33,448,354).

。我们排除了MHC基因座内的所有变体（chr6:28477897-33448354）。

For each eGene, both eQTL and GWAS summary statistics were then applied to the coloc.abf routine of the COLOC package. We used the default setting (p1 or p2 = 1 × 10−4) for the prior probability of a variant being associated with either GWAS (p1) or gene expression (p2). The prior probability (p12) of a variant to be associated with both GWAS and gene expression was set at 1 × 10−5.

。我们使用默认设置（p1或p2）=1×10-4）作为变体与GWAS（p1）或基因表达（p2）相关的先验概率。与GWAS和基因表达相关的变体的先验概率（p12）设置为1×10-5。

An eGene was declared to have colocalization when the posterior probability of colocalization of a GWAS variant and an eQTL linked to the eGene (PP4) was greater than 0.75.Fine mapping DICE eQTLsWe employed our custom fine-mapping pipeline (https://github.com/ay-lab/finemap) based on the package FINEMAP20 with default settings to perform statistical fine-mapping of DICE eQTLs of various CD4 T cell subsets and their LD SNPs.

当GWAS变体与与eGene相关的eQTL（PP4）共定位的后验概率大于0.75时，eGene被宣布具有共定位。精细映射骰子eQTL我们使用了我们的自定义精细映射管道(https://github.com/ay-lab/finemap)基于具有默认设置的FineMapp20软件包，对各种CD4 T细胞亚群及其LD SNP的骰子eQTL进行统计精细定位。

We used the reference and alternate alleles, MAF, P value, and beta values of individual SNPs. Fine-mapping was applied on individual genes and all the SNPs having association statistics with that gene. The package LDstore116 was used to compute the LD statistics. FINEMAP was employed by allowing a maximum of ten cau.

我们使用了单个SNP的参考和替代等位基因，MAF，P值和β值。精细定位应用于单个基因以及与该基因具有关联统计的所有SNP。包LDstore116用于计算LD统计信息。FINEMAP的使用最多允许十个cau。

1.

1.

The SNP is associated with more than one HiChIP loops.

SNP与多个HiChIP环相关。

2.

2.

All these loops exhibit similar trend (and direction) of HiChIP contact counts for different genotypes, and

所有这些环在不同基因型的HiChIP接触计数上表现出相似的趋势（和方向），并且

3.

3.

Genotype-dependent variation of the HiChIP contact counts combined for all the loops should be statistically significant.

。

To check whether a iQTL X is a multi-loop-iQTL, we obtained the ratio values of observed and expected contact counts (from FitHiChIP statistics) from all its associated HiChIP loops and from all the input donors. We then computed the association between these ratios of contact counts with the genotypes of X using linear regression (applying the lm() function of R).

为了检查iQTL X是否是多回路iQTL，我们从其所有相关的HiChIP回路和所有输入供体中获得了观察到的和预期的接触计数（来自FitHiChIP统计）的比值。然后，我们使用线性回归（应用R的lm（）函数）计算了这些接触计数比率与X基因型之间的关联。

Associations with p-values < 1e-5 were considered significant and corresponding iQTLs were labeled as the multi-loop-iQTLs.Derivation of Connectivity-iQTLsWe defined a connectivity-iQTL as a iQTL if it exhibits significant association between its genotype and the contact counts of a group of HiChIP contacts (significant by FitHiChIP in at least one input sample) in the same direction, with respect to a broad region or locus R.

p值<1e-5的关联被认为是显着的，相应的IQTL被标记为多环IQTL。连接性IQTLS的推导将连接性iQTL定义为iQTL，如果它在同一方向上表现出其基因型与一组HiChIP接触（在至少一个输入样本中通过FitHiChIP显着）的接触计数之间的显着关联，则相对于宽区域或基因座R。

To define such a testable region R for a given iQTL X associated with a HiChIP loop L, we adapted the connected component labeling algorithm (see89 and also our previous work64) for identifying the set of HiChIP loops S which are significant by FitHiChIP in at least one input sample and are spatially adjacent to the loop L.

为了为与HiChIP循环L相关的给定iQTL X定义这样的可测试区域R，我们采用了连接组件标记算法（参见89以及我们之前的工作64）来识别HiChIP循环S的集合，这些循环S在至少一个输入样本中是重要的，并且在空间上与循环L相邻。

By spatial adjacency, we mean that the interacting bins of these loops are proximal. Formally, as the SNP X is a iQTL of the loop L, X exhibits genotype-dependent variation of HiChIP contact counts for L. We first initialized S with the loop L. If the interacting 5kb bins of the loop L are denoted by b1 and b2 (where, without loss of generality, b1 < b2), using all the loops (and their constituent bins) in S, we defined the following quantities: r1s = min(b1 ∀ L ∈ S); r1e = max(b1 ∀ L ∈ S); r2s = min(b2 ∀ L ∈ S); r2e = max(b2 ∀ L ∈ S), where min and max denote the conventional minimum and maximum operations.

通过空间邻接，我们的意思是这些循环的相互作用箱是近端的。形式上，由于SNP X是环L的iQTL，因此X表现出L的HiChIP接触计数的基因型依赖性变异。我们首先用环L初始化S。如果环L的相互作用5kb仓用b1和b2表示（其中，不失一般性，b1<b2），使用S中的所有环（及其组成仓），我们定义了以下数量：r1s=min（b1∀L∈S）；r1e=max（b1∀L∈S）；r2s=min（b2∀L∈S）；。

We defined the interacting segments A and B as A = [r1s, r1.

我们将相互作用的片段A和B定义为A=[r1s，r1。

1.

1.

r1e < r2s, i.e., the interacting segments A and B are disjoint. Here we define S by the complete set of HiChIP loops (significant by FitHiChIP in at least one input sample) such that one ends of these loops belong to the segment A while the other ends belong to the segment B.

。在这里，我们通过完整的HiChIP循环集（在至少一个输入样本中由FitHiChIP显着）定义S，使得这些循环的一端属于段A，而另一端属于段B。

2.

2.

r1e >= r2s, i.e., the interacting segments A and B actually overlap. So, here we consider S as the set of loops whose endpoints belong to the complete span [r1s, r2e] (i.e., union of A and B).

r1e>=r2s，即相互作用的片段A和B实际上重叠。因此，在这里，我们将S视为其端点属于完整跨度[r1s，r2e]（即A和B的并集）的循环集。

Once the set of loops S is constructed, we defined an iQTL X as a connectivity-iQTL associated with the loops in S provided the following conditions were satisfied: 1) ratios of observed and expected HiChIP contact counts for all the HiChIP contacts in S followed similar trends of genotype-dependent variation, and their combined trend was also statistically significant, and 2) HiChIP contact matrices formed by using all the contacts between the regions A and B (when r1e < r2s) or within the region spanned by A and B (when r1e >= r2s) showed a substantial difference between genotypes (i.e., reference homozygous, heterozygous, and alternate homozygous) with respect to the iQTL X.

一旦构建了一组循环S，我们将iQTL X定义为与S中循环相关的连通性iQTL，前提是满足以下条件：1）S中所有HiChIP接触的观察和预期HiChIP接触计数的比率遵循类似的基因型依赖性变异趋势，其组合趋势也具有统计学意义；2）使用区域a和B之间（当r1e<r2s时）或区域a和B之间（当r1e>=r2s时）的所有接触形成的HiChIP接触矩阵显示基因型（即参考纯合子、杂合子和替代纯合子）之间的iQTL X存在显著差异。

Formally, we first derived the ratios of observed and expected contact counts (obtained from FitHiChIP statistics) for all the loops L ∈ S. These ratio values were then fitted against the genotypes of individual donors (with respect to the iQTL X) by linear regression (we used the R function lm()). For statistically significant regression outputs (p-value < 1e-5 and Bonferroni-corrected p-value < 0.005), we used the complete set of HiChIP contacts (contact count > 0, may or may not be significant by FitHiChIP in any of the input samples) within the regions A and B to define an aggregated contact map.

正式地，我们首先得出所有环L∈S的观察到的和预期的接触计数的比率（从FitHiChIP统计获得）。然后通过线性回归（我们使用R函数lm（））将这些比值与个体供体的基因型（相对于iQTL X）拟合。对于统计学上显着的回归输出（p值<1e-5和Bonferroni校正的p值<0.005），我们使用了区域A和B内的完整HiChIP触点集（触点计数>0，FitHiChIP在任何输入样本中可能显着也可能不显着）来定义聚集的触点图。

Specifically, if the regions A and B were distinct (r1e < r2s), the set of contacts had their first interacting bins at A and the other interacting bins at B. On the other hand, if the regions A and B overlapped (r1e >= r2s), we used all the contacts within the span [r1s, r2e]. These HiChIP contacts were used to define three aggregated contact maps Mref, Mhet, Malt with respect to three different genotypes (reference homozygous, heterozygous and alternate homozygous) with respect to the iQTL X.

具体来说，如果区域A和B是不同的（r1e<r2s），则接触集的第一个相互作用箱位于A，另一个相互作用箱位于B。另一方面，如果区域A和B重叠（r1e>=r2s），我们使用了跨度[r1s，r2e]内的所有接触。这些HiChIP接触用于定义三个聚集的接触图Mref，Mhet，Malt相对于iQTL X的三种不同基因型（参考纯合子，杂合子和替代纯合子）。

Rows .

行。

Data availability

数据可用性

Sequencing data: HiChIP sequencing data has been uploaded in dbGAP (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/molecular.cgi?study_id=phs001703.v5.p1) and the corresponding HiChIP samples are listed in Supplementary Data 12. ChIP-seq peaks and HiChIP loops: ChIP-seq peaks used in this study are shared in Supplementary Data 2.

测序数据：HiChIP测序数据已上传到dbGAP中(https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/molecular.cgi?study_id=phs001703.v5.p1)。ChIP-seq峰和HiChIP环：本研究中使用的ChIP-seq峰在补充数据2中共享。

Complete set of FitHiChIP loops from all the donors are provided at the Zenodo repository https://doi.org/10.5281/zenodo.13127086 (file Complete_FitHiChIP_Loops_iQTL_Input.xlsx). Complete set of iQTLs and connectivity-QTLs derived from this work are provided in Supplementary Data 4 and 10, respectively.

Zenodo存储库提供了来自所有捐赠者的全套FitHiChIP循环https://doi.org/10.5281/zenodo.13127086。补充数据4和10分别提供了从这项工作中获得的完整的IQTL和连通性QTL。

WashU epigenome browser tracks: To visualize the shared tracks, user needs to open the WashU epigenome browser99,100,101 (https://epigenomegateway.wustl.edu/browser/), and then load the session ID c730ec60-4de0-11ef-8802-7f6b1b69f09b containing the iQTLs, associated HiChIP loops, aggregate ChIP-seq peaks, FitHiChIP loops for individual donors, and the reference DICE nCD4 eQTLs.

WashU epigenome浏览器轨迹：要可视化共享轨迹，用户需要打开WashU epigenome浏览器99100101(https://epigenomegateway.wustl.edu/browser/)，然后加载会话ID c730ec60-4de0-11ef-8802-7f6b1b69f09b，其中包含IQTL，相关的HiChIP环，聚合ChIP-seq峰，单个供体的FitHiChIP环以及参考DICE nCD4 eQTL。

Web browser: We also created a web browser https://ay-lab-tools.lji.org/iQTL/ listing all the derived iQTLs, connectivity-QTLs, corresponding looping information, genotype and allele-specific trend plots, and the WashU browser tracks for individual SNP-loop pairs. Source data are provided with this paper..

Web浏览器：我们还创建了一个Web浏览器https://ay-lab-tools.lji.org/iQTL/列出所有衍生的IQTL，连通性QTL，相应的循环信息，基因型和等位基因特定的趋势图，以及WashU浏览器跟踪单个SNP循环对。本文提供了源数据。。

Code availability

代码可用性

All the developed software and scripts are available through a GitHub repository for this project (https://github.com/ay-lab/iQTL). The scripts for fine mapping GWAS and eQTL are hosted at https://github.com/ay-lab/finemap. Script for colocalization between eQTL and GWAS summary statistics is provided at https://github.com/ay-lab/Colocalization.

所有开发的软件和脚本都可以通过该项目的GitHub存储库获得(https://github.com/ay-lab/iQTL)。精细映射GWAS和eQTL的脚本位于https://github.com/ay-lab/finemap.eQTL和GWAS摘要统计数据之间的共定位脚本提供于https://github.com/ay-lab/Colocalization.

The script for stratified LD Score Regression (SNP specific) is provided at https://github.com/ay-lab/S_LDSC_SNP. We have also uploaded the source code at the Zenodo repository https://doi.org/10.5281/zenodo.13127086 (folder: SourceCode_GitHub)..

分层LD得分回归（SNP特定）的脚本提供于https://github.com/ay-lab/S_LDSC_SNP.我们还将源代码上传到了Zenodo存储库https://doi.org/10.5281/zenodo.13127086（文件夹：SourceCode\u GitHub）。。

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Download referencesAcknowledgementsWe thank the La Jolla Institute (LJI) Flow Cytometry Core for assisting with cell sorting; the LJI’s Clinical Studies Core for organizing sample collection; LJI’s NGS core and Dr. Gregory Seumois for sequencing. We thank Dr. Benjamin Schmiedel for sample processing, cell sorting and ChIP-seq data, and Dr.

下载参考文献致谢我们感谢拉荷亚研究所（LJI）流式细胞术核心协助细胞分选；LJI的临床研究核心，用于组织样本采集；LJI的NGS核心和Gregory Seumois博士进行测序。我们感谢Benjamin Schmiedel博士的样品处理，细胞分选和ChIP-seq数据，以及Dr。

Vivek Chandra for performing HiChIP experiments and contributing to the initial conception of the project under the supervision of Dr. Pandurangan Vijayanand (LJI). Additionally, we thank Dr. Chi-Hua Chen for valuable comments and edits on the manuscript. We also thank the members of Ay and Vijayanand labs for their valuable support.

Vivek Chandra在Pandurangan Vijayanand博士（LJI）的监督下进行了HiChIP实验并为项目的初步构想做出了贡献。此外，我们感谢陈奇华博士对稿件的宝贵评论和编辑。。

This work was funded by NIH grants R35-GM128938 (F.A.) and R24-AI108564 (MPI – F.A.). Utilized equipment was supported by the NIH grants no. S10-RR027366 (BD FACSAria II) and no. S10-OD016262 (Illumina HiSeq 2500).Author informationAuthors and AffiliationsLa Jolla Institute for Immunology, La Jolla, CA, USASourya Bhattacharyya & Ferhat AyDepartment of Pediatrics, University of California, San Diego, La Jolla, CA, USAFerhat AyAuthorsSourya BhattacharyyaView author publicationsYou can also search for this author in.

这项工作由美国国立卫生研究院拨款R35-GM128938（F.A.）和R24-AI108564（MPI-F.A.）资助。使用的设备得到了美国国立卫生研究院拨款S10-RR027366（BD FACSAria II）和S10-OD016262（Illumina HiSeq 2500）的支持。作者信息作者和附属机构a Jolla免疫研究所，加利福尼亚州拉荷亚，USASourya Bhattacharyya＆Ferhat Ay加利福尼亚大学圣地亚哥分校儿科，加利福尼亚州拉荷亚，USAFerhat AYAUTHORSOURYA BhattacharyyaView作者出版物您也可以在中搜索这位作者。

PubMed Google ScholarFerhat AyView author publicationsYou can also search for this author in

PubMed Google ScholarFerhat AyView作者出版物您也可以在

PubMed Google ScholarContributionsF.A. and S.B. conceived the work. S.B developed the computational algorithms and performed bioinformatic analysis under the supervision of F.A. S.B and F.A. wrote the manuscript. All authors have read and approved the contents of the manuscript.Corresponding authorsCorrespondence to.

PubMed谷歌学术贡献。A、 S.B.构思了这项工作。S.B在F.A.S.B的监督下开发了计算算法并进行了生物信息学分析，F.A.撰写了手稿。所有作者都阅读并批准了手稿的内容。通讯作者通讯。

Sourya Bhattacharyya or Ferhat Ay.Ethics declarations

Sourya Bhattacharyya或Ferhat Ay。道德宣言

Competing interests

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The authors declare no competing interests.

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Nature Communications thanks Biola Javierre, Ralph Stadhouders and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

《自然通讯》感谢Biola Javierre、Ralph Stadhouders和另一位匿名审稿人对这项工作的同行评审做出的贡献。同行评审文件可用。

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Reprints and permissionsAbout this articleCite this articleBhattacharyya, S., Ay, F. Identifying genetic variants associated with chromatin looping and genome function.

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Nat Commun 15, 8174 (2024). https://doi.org/10.1038/s41467-024-52296-4Download citationReceived: 08 September 2023Accepted: 30 August 2024Published: 18 September 2024DOI: https://doi.org/10.1038/s41467-024-52296-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Nat Commun 158174（2024）。https://doi.org/10.1038/s41467-024-52296-4Download引文收到日期：2023年9月8日接受日期：2024年8月30日发布日期：2024年9月18日OI：https://doi.org/10.1038/s41467-024-52296-4Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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