AbstractStudying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF).

。在这里，作为日本新型冠状病毒肺炎工作组（JCTF）的一部分，我们报告了来自1405个具有血液mRNA的基因分型样品和2932个蛋白质表达的血浆样品的顺式eQTL和顺式pQTL统计精细定位。

Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes.

精细定位的eQTL（n=3464）富集了932个变体，并通过大规模平行报告基因检测进行了验证。精细定位的pQTL（n=582）富含结构化和细胞外结构域的错义变异，尽管表位结合伪影的可能性仍然存在。反式eQTL和反式pQTL分析突出了I类HLA等位基因变异与KIR基因的关联。

We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs..

我们将血浆蛋白的多组织起源与血液mRNA进行了对比，这有助于有限的共定位水平，不同的调控机制以及eQTL和pQTL的性状相关性。我们报告了ABO mRNA和蛋白质表达之间的负相关，因为不同的附近eQTL和pQTL之间存在连锁不平衡。。

MainStudies of genetic regulation of mRNA expression (expression quantitative trait locus (eQTL) studies) is highly informative in interpreting associations between genetic variation and human diseases1,2. However, mRNA expression is a limited proxy of protein expression, which affects human phenotypes in a more direct manner3,4,5.Analysis of genetic regulation of protein expressions (protein QTL (pQTL) studies) is gaining popularity6,7,8,9,10,11, owing to the development of high-throughput affinity-based assays that enable one-shot measurements of thousands of proteins in large-scale biobank cohorts.

mRNA表达遗传调控的主要研究（表达数量性状基因座（eQTL）研究）在解释遗传变异与人类疾病之间的关联方面具有很高的信息量1,2。然而，mRNA表达是蛋白质表达的有限代表，它以更直接的方式影响人类表型3,4,5。蛋白质表达的遗传调控分析（蛋白质QTL（pQTL）研究）正在获得普及6,7,8,9,10,11，这是由于开发了基于高通量亲和力的检测方法，可以在大规模生物库队列中一次性测量数千种蛋白质。

Sun et al.7 performed a pQTL study in 3,301 samples on 3,622 plasma proteins measured using SOMAscan and identified nearly 2,000 pQTLs. Zhang et al.8 performed large-scale pQTL fine-mapping in European and African populations and discussed druggability. The UK Biobank (UKB) Pharma Proteomics Project (PPP) has performed pQTL mapping in more than 50,000 samples12,13,14, uncovering common and rare variant contributions to variation in protein expression that do not necessarily involve the effect of eQTLs in major tissues.Complementing such studies, having another layer of diversity by studying East Asian (EAS) populations would be promising because multi-cohort genetic and proteomic studies have been effective in identifying drug targets15,16,17,18.

Sun等[7]对3301个样本进行了pQTL研究，使用SOMAscan测量了3622个血浆蛋白，并鉴定了近2000个pQTL。Zhang等[8]在欧洲和非洲人群中进行了大规模pQTL精细定位，并讨论了可药用性。英国生物银行（UKB）制药蛋白质组学项目（PPP）已经在超过50000个样本中进行了pQTL作图12,13,14，揭示了蛋白质表达变异的常见和罕见变异贡献，这些变异不一定涉及eQTL在主要组织中的作用。作为这些研究的补充，通过研究东亚（EAS）人群获得另一层多样性将是有希望的，因为多队列遗传和蛋白质组学研究已经有效地鉴定了药物靶标15,16,17,18。

In addition, instead of using an external eQTL dataset to examine colocalization between eQTLs and pQTLs, building on earlier studies with multimodal measurements of mRNA and protein expression in the same sample set19 in this era of large-scale proteomics would be effective in identifying disease-causing variants in multiple layers as it minimizes the potential loss of discovery power due to differences in technical and clinical .

此外，在这个大规模蛋白质组学时代，建立在早期研究的基础上，对同一样本集中的mRNA和蛋白质表达进行多模式测量19，而不是使用外部eQTL数据集来检查eQTL和pQTL之间的共定位，这将有效地识别多层致病变异，因为它可以最大程度地减少由于技术和临床差异而导致的发现能力的潜在损失。

Data availability

数据可用性

The summary statistics of the QTL analyses and the RNA-seq expression matrix are available at the NBDC Human Database (accession no. hum0343). The QTL summary statistics are also available at https://japan-omics.jp/. Individual genotype data are available at the European Genome-phenome Archive (accession no.

QTL分析和RNA-seq表达矩阵的汇总统计数据可在NBDC人类数据库（登录号hum0343）中获得。QTL摘要统计数据也可在https://japan-omics.jp/.个体基因型数据可在欧洲基因组-表型档案馆（登录号：。

EGAS00001006284). Publicly available datasets used are: BBJ and UKB fine-mapping; NBDC Human Database (accession no. hum0197) and https://www.finucanelab.org/data; the expression modifier score (https://www.finucanelab.org/data); the GTEx cis-eQTL data (https://gtexportal.org/home/datasets); the hg38 reference genome (https://hgdownload.soe.ucsc.edu/goldenPath/hg38/); protein-specific annotations from UniProt, obtained through the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgTables); protein QTL data from the ARIC study (http://nilanjanchatterjeelab.org/pwas/); and protein QTL data from the UKB PPP study (https://www.synapse.org/#!Synapse:syn51365301)..

EGAS0001006284）。；NBDC人类数据库（登录号hum0197）和https://www.finucanelab.org/data；表达式修饰符得分(https://www.finucanelab.org/data)；GTEx顺式eQTL数据(https://gtexportal.org/home/datasets)；hg38参考基因组(https://hgdownload.soe.ucsc.edu/goldenPath/hg38/)；UniProt的蛋白质特异性注释，通过UCSC基因组浏览器获得(https://genome.ucsc.edu/cgi-bin/hgTables)；来自ARIC研究的蛋白质QTL数据(http://nilanjanchatterjeelab.org/pwas/)；和来自UKB PPP研究的蛋白质QTL数据(https://www.synapse.org/#哦！突触：syn51365301）。。

Code availability

代码可用性

The code used in this study is available at https://github.com/QingboWang/japan_covid_taskforce_multi_omics and has been deposited via Zenodo at https://doi.org/10.5281/zenodo.11169201 (ref. 69). The software and tools used for data analysis and visualization are: DEEP*HLA v.1.0.0 (https://zenodo.org/record/4478902)70; fastQTL v.2.165 (http://fastqtl.sourceforge.net); FINEMAP v.1.3.1 (http://www.christianbenner.com/); GATK v.4.1.9.0 LiftoverVcf (https://gatk.broadinstitute.org/); the GTEx pipeline (https://github.com/broadinstitute/gtex-pipeline); LeafCutter v.0.2.7 (https://davidaknowles.github.io/leafcutter/index.html); matplotlib v.3.3.4 (https://matplotlib.org); MPRAflow v.2.3.5 (https://mpraflow.readthedocs.io/en/latest/index.html); NumPy v.1.20.1 (https://numpy.org); OlinkAnalyze v.3.4.1 (https://cran.r-project.org/web/packages/OlinkAnalyze/index.html); pandas v.1.1.4 (https://pandas.pydata.org); pybedtools v.0.9.0 (https://daler.github.io/pybedtools/); PyWGCNA v.1.20.3 (https://github.com/mortazavilab/PyWGCNA); RSEM v.1.3.0 (https://deweylab.github.io/RSEM/); scikit-learn v.0.24.1 (https://scikit-learn.github.io/stable); SciPy v.1.6.2 (https://scipy.org/); seaborn v.0.11.1 (https://seaborn.pydata.org); STAR v.2.5.3a and v.2.6.0 (https://github.com/alexdobin/STAR); susieR v.0.11.43 (https://github.com/stephenslab/susieR); tensorQTL v.1.0.5 (https://github.com/broadinstitute/tensorqtl); TwoSampleMR v.0.5.7 (https://mrcieu.github.io/TwoSampleMR/articles/introduction.html); and VEP v.108 (https://asia.ensembl.org/Homo_sapiens/Tools/VEP/)..

这项研究中使用的代码可以在https://github.com/QingboWang/japan_covid_taskforce_multi_omics并已通过Zenodo存放在https://doi.org/10.5281/zenodo.11169201（参考文献69）。用于数据分析和可视化的软件和工具是：DEEP*HLA v.1.0.0(https://zenodo.org/record/4478902)70；fastQTL v.2.165(http://fastqtl.sourceforge.net)；FINEMAP v.1.3.1(http://www.christianbenner.com/)；GATK v.4.1.9.0提升超过VCF(https://gatk.broadinstitute.org/)；GTEx管道(https://github.com/broadinstitute/gtex-pipeline)；(https://davidaknowles.github.io/leafcutter/index.html)；matplotlib v.3.3.4(https://matplotlib.org)；MPRAflow v.2.3.5(https://mpraflow.readthedocs.io/en/latest/index.html)；NumPy v.1.20.1(https://numpy.org)；OlinkAnalyze v.3.4.1(https://cran.r-project.org/web/packages/OlinkAnalyze/index.html)；熊猫v.1.1.4(https://pandas.pydata.org)；pybedtools v.0.9.0(https://daler.github.io/pybedtools/)；PyWGCNA v.1.20.3(https://github.com/mortazavilab/PyWGCNA)；RSEM v.1.3.0(https://deweylab.github.io/RSEM/)；scikit学习v.0.24.1(https://scikit-learn.github.io/stable)；SciPy v.1.6.2(https://scipy.org/)；seaborn v.0.11.1(https://seaborn.pydata.org)；STAR v.2.5.3a和v.2.6.0(https://github.com/alexdobin/STAR)；苏西尔v.0.11.43(https://github.com/stephenslab/susieR)；tensorQTL v.1.0.5(https://github.com/broadinstitute/tensorqtl)；两个样本MR v.0.5.7(https://mrcieu.github.io/TwoSampleMR/articles/introduction.html)；和VEP v.108(https://asia.ensembl.org/Homo_sapiens/Tools/VEP/)。。

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Wang, Q. S. QingboWang/japan_covid_taskforce_multi_omics: v1.0 (v1.0). Zenodo https://doi.org/10.5281/zenodo.11169202 (2024).tatsuhikonaito/DEEP-HLA: First release of DEEP*HLA (v.1.0.0). Zenodo https://zenodo.org/records/4478902 (2021).Download referencesAcknowledgementsWe thank all the participants involved in this study, and all the members of the JCTF for their support.

Wang，Q.S.QingboWang/japan\u covid\u taskforce\u multi\u组学：v1.0（v1.0）。泽诺多https://doi.org/10.5281/zenodo.11169202（2024）。tatsuhikonaito/DEEP-HLA：DEEP*HLA的首次发布（v.1.0.0）。泽诺多https://zenodo.org/records/4478902（2021年）。下载参考文献致谢我们感谢参与本研究的所有参与者以及JCTF的所有成员的支持。

We thank J. Kitano, the e-Parcel Corporation and the Ascend Corporation for supporting the JCTF. This study was supported by the Japan Agency for Medical Research and Development (AMED) (nos. JP23kk0305022, JP22ek0410075, JP23km0405211, JP23km0405217, JP23ek0109594, JP23ek0410113, JP223fa627002, JP223fa627010, JP233fa627011, JP23zf0127008, JP23tm0524002, JP22fk0108510, JP21fk0108553, JP21fk0108431, JP20fk0108415 and JP20fk0108452), JST CREST (no.

我们感谢J.Kitano、e-Parcel Corporation和Ascend Corporation对JCTF的支持。。

JPMJCR20H2), JST FOREST (no. JPMJFR225Y), JST PRESTO (no. JPMJPR21R7), JST Moonshot R&D (nos. JPMJMS2021 and JPMJMS2024), MHLW (no. 20CA2054), JSPS KAKENHI (nos. 22H00476 and 23K14233), the Nakajima Foundation, the Uehara Memorial Foundation, the Takeda Science Foundation, the Mitsubishi Foundation and the Bioinformatics Initiative of the Osaka University Graduate School of Medicine, the Institute for Open and Transdisciplinary Research Initiatives, the Center for Infectious Disease Education and Research and the Center for Advanced Modality and DDS, Osaka University.

JPMJCR20H2），JST FOREST（编号JPMJFR225Y），JST PRESTO（编号JPMJPR21R7），JST Moonshot R＆D（编号JPMJMS2021和JPMJMS2024），MHLW（编号20CA2054），JSPS KAKENHI（编号22H00476和23K14233），中岛基金会，上原纪念基金会，武田科学基金会，三菱基金会和大阪大学医学研究生院的生物信息学计划，开放和跨学科研究计划研究所，传染病教育与研究中心以及先进模式和大阪大学DDS。

The super-computing resource was provided by the Human Genome Center (University of Tokyo).Author informationAuthors and AffiliationsDepartment of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, JapanQingbo S. Wang, Kyuto Sonehara & Yukinori OkadaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, JapanQingbo S.

超级计算资源由人类基因组中心（东京大学）提供。作者信息作者和附属机构东京大学医学研究生院基因组信息学系，东京，日本庆波S.Wang，Kyuto Sonehara＆Yukinori OkadaDepartment of Statistical Genetics，大阪大学医学研究生院，Suita，日本庆波S。

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PubMed Google ScholarConsortiaJapan COVID-19 Task ForceQingbo S. Wang, Takanori Hasegawa, Ho Namkoong, Ryunosuke Saiki, Ryuya Edahiro, Kyuto Sonehara, Hiromu Tanaka, Shuhei Azekawa, Shotaro Chubachi, Shinichi Namba, Kenichi Yamamoto, Yasuhito Nannya, Ryuji Koike, Tomomi Takano, Makoto Ishii, Akinori Kimura, Takanori Kanai, Koichi Fukunaga, Seishi Ogawa, Seiya Imoto, Satoru Miyano & Yukinori OkadaContributionsQ.S.W.

PubMed Google ScholarConsortiaJapan COVID-19特别工作组王庆波，长谷川孝，何南光，斋木龙之介，枝野良彦，松原京人，田中弘，泽川淑平，竹木昭太郎，南坂信一，山本健一，南洋安仁，小池龙治，高野智美，石井Makoto Ishi，木村明彦，金奈高野，福永光一，小川精工，井本清一，宫野佐藤和由纪RI冈田贡献Q。S、 W。

and Y.O. designed the study. Q.S.W., Y.T., H.N., T.H., S.I., S.M. and Y.O. analyzed the data. Q.S.W. wrote the manuscript. Y.O. reviewed and edited the manuscript. H.N. and Y.O. supervised the work. All authors and the JCTF contributed to the generation of the primary data incorporated in the study, provided inputs and approved the manuscript.Corresponding authorsCorrespondence to.

Y.O.设计了这项研究。Q、 S.W.，Y.T.，H.N.，T.H.，S.I.，S.M.和Y.O.分析了数据。Q、 S.W.写了手稿。Y、 O.审查并编辑了手稿。H、 N.和Y.O.监督了这项工作。所有作者和JCTF都为研究中纳入的主要数据的产生做出了贡献，提供了投入并批准了手稿。通讯作者通讯。

Qingbo S. Wang, Ho Namkoong or Yukinori Okada.Ethics declarations

Qingbo S.Wang，Ho Namkoong或Yukinori Okada。道德宣言

Competing interests

相互竞争的利益

Q.S.W. is an employee of Calico Life Sciences. The other authors declare no competing interests.

Q、 S.W.是Calico Life Sciences的员工。其他作者声明没有利益冲突。

Peer review

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Peer review information

同行评审信息

Nature Genetics thanks Anders Malarstig, Clint Miller and Maik Pietzner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

自然遗传学感谢Anders Malarstig，Clint Miller和Maik Pietzner以及另一位匿名审稿人对这项工作的同行评审做出的贡献。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Overview of the study.We performed mRNA expression QTL (eQTL) fine-mapping from 1,019 RNA-sequenced samples, pQTL fine-mapping from 1,384 protein measured samples, as well as mRNA or protein specific QTL fine-mapping from 998 samples with both measures, all genotyped and processed in a single platform as part of the Japan COVID-19 Task Force20.

Additional informationPublisher的注释Springer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。扩展数据扩展数据图1研究概述。我们从1019个RNA测序样品中进行了mRNA表达QTL（eQTL）精细定位，从1384个蛋白质测量样品中进行了pQTL精细定位，以及从998个样品中进行了mRNA或蛋白质特异性QTL精细定位，这两种测量均在单一平台上进行了基因分型和处理，作为日本新型冠状病毒肺炎工作组20的一部分。

Massive parallel reporter assay (MPRA) was performed for validation of a subset of fine-mapped eQTLs.Extended Data Fig. 2 eQTL fine-mapping expanded.a. Comparison of the numbers of eQTLs in our dataset compared to the previous release. b. Functional score (the expression modifier score = EMS) enrichment in eQTLs along with the posterior inclusion probability (PIP).

进行大规模平行报告基因测定（MPRA）以验证精细定位的eQTL的子集。扩展数据图2扩展了eQTL精细映射。a.与之前版本相比，我们数据集中eQTL数量的比较。b、 eQTL中的功能评分（表达修饰评分=EMS）富集以及后验包含概率（PIP）。

c. Percentage of expression modifying variants (emvars) experimentally validated in massive parallel reporter assay (MPRA). Tier 1 corresponds to FDR < 0.01 and tier 2 to FDR < 0.1. n in each bin = 7,418, 2,060, 685, 885 and 317 variants. d. Percentage of agreement between the direction of variant effects in eQTL or MPRA study.

c、 在大规模平行报告基因测定（MPRA）中通过实验验证的表达修饰变体（EMVAR）的百分比。第1层对应于FDR<0.01，第2层对应于FDR<0.1。每个箱中的n=7418、2060、685、885和317个变体。d、 eQTL或MPRA研究中变异效应方向之间的一致性百分比。

n in each bin = 7,418, 2,992 and 955 variants.Supplementary informationSupplementary InformationSupplementary Tables 1 and 2, Figs. 1–26 and Note.Reporting SummarySupplementary Data 1–5Supplementary Data 1: List of tier 1 and 2 expression-modifying variants (EMVars) identified in the massively parallel reporter assay (MPRA).

每个箱中的n=74182992和955个变体。补充信息补充信息补充表1和2，图1-26和注释。报告摘要补充数据1-5补充数据1：在大规模并行报告分析（MPRA）中鉴定出的第1层和第2层表达修饰变体（EMVAR）列表。

Supplementary Data 2: Classification of per-gene regulatory patterns into mRNA/protein-specific or shared regulations. Supplementary Data 3: List of complex trait-colocalizing putative causal QTLs. Supplementary Data 4: List of significa.

补充数据2：将每个基因调控模式分类为mRNA/蛋白质特异性或共享调控。补充数据3：复杂性状共定位推定因果QTL的列表。补充数据4：重要信息列表。

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Reprints and permissionsAbout this articleCite this articleWang, Q.S., Hasegawa, T., Namkoong, H. et al. Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance.

转载和许可本文引用本文Wang，Q.S.，Hasegawa，T.，Namkoong，H。等人。来自1405名人类的统计和功能精细定位的血液eQTL和pQTL揭示了不同的调控模式和疾病相关性。

Nat Genet (2024). https://doi.org/10.1038/s41588-024-01896-3Download citationReceived: 21 July 2023Accepted: 06 August 2024Published: 24 September 2024DOI: https://doi.org/10.1038/s41588-024-01896-3Share 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.

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