Extended data: Tissue-specific multi-omics analysis of atrial fibrillation

Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation: Assum, I., Krause, J., Scheinhardt, M.O. <em>et al.</em> Tissue-specific multi-omics analysis of atrial fibrillation. <em>Nat Commun </em><strong>13, </strong>441 (2022). https://doi.org/10.1038/s41467-022-27953-1 For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial. Ines Assum^1,2,†, Julia Krause^3,4,†, Markus O. Scheinhardt⁵, Christian Müller^3,4, Elke Hammer^6,7, Christin S. Börschel^4,8, Uwe Vöker^6,7, Lenard Conradi⁹, Bastiaan Geelhoed^4,8,10, Tanja Zeller^3,4,*, Renate B. Schnabel^4,8,*, Matthias Heinig^1,2,11,* ^†,* These authors contributed equally. ¹ Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.<br> ² Department of Informatics, Technical University Munich, München, Germany.<br> ³ University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> ⁴ Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.<br> ⁵ Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.<br> ⁶ Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.<br> ⁷ Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany.<br> ⁸ Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> ⁹ Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> ¹⁰Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.<br> ¹¹Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany. ABSTRACT: Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization. This version contains a reference file identifying effect alleles for all QTL results and adds additional genotype and allele frequency information for all QTL SNPs. TABLE OF CONTENTS: Reference for effect alleles<br> <em>map_AFHRI_B_effect_alleles.txt</em> Reference for genotype and allele frequencies (derived using PLINK) <em>genotype_allele_frequencies_eQTL_SNPs.txt</em> <em>genotype_allele_frequencies_pQTL_SNPs.txt</em> <em>genotype_allele_frequencies_resQTL_SNPs.txt</em> Single-omic <em>cis</em>-QTL results <em>cis</em>-eQTLs (all pairs, incl. LD clump info)<br> <em>eQTL_right_atrial_appendage_allpairs_clump.txt</em> <em>cis</em>-pQTLs (all pairs, incl. LD clump info)<br> <em>pQTL_right_atrial_appendage_allpairs_clump.txt</em> <em>cis</em>-res eQTLs (all pairs, incl. LD clump info)<br> <em>res_eQTL_right_atrial_appendage_allpairs_clump.txt</em> <em>cis</em>-res pQTLs (all pairs, incl. LD clump info)<br> <em>res_pQTL_right_atrial_appendage_allpairs_clump.txt</em> <em>cis</em>-ratioQTLs (all pairs, incl. LD clump info)<br> <em>ratioQTL_right_atrial_appendage_allpairs_clump.txt</em> Functional <em>cis</em>-QTL categories and eQTL/pQTL overlap: All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR&lt;0.05)<br> <em>Fig2a_source_data_Shared_eQTL_pQTL_clump.txt</em> All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR&lt;0.05)<br> <em>Fig2b_source_data_Independent_eQTL_clump.txt</em> All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR&lt;0.05)<br> <em>Fig2c_source_data_Independent_pQTL_clump.txt</em> QTS rankings and enrichment results eQTS rankings and enrichments<br> <em>TableS6_source_data_eQTS_ranking.txt<br> TableS7_source_data_eQTS_GSEA_results.txt</em> pQTS rankings and enrichments<br> <em>TableS8_source_data_pQTS_ranking.txt<br> TableS9_source_data_pQTS_GSEA_results.txt</em> <em>Trans</em>-QTLs<br> all tested pairs including <em>trans</em>-pQTLs for <em>trans</em>-eQTLs and <em>trans</em>-eQTLs for <em>trans</em>-pQTLs<br> <em>Table2_source_data_Trans-QTL_results.txt</em>本数据集为论文《Tissue-specific multi-omics analysis of atrial fibrillation》的汇总统计与结果仓库，原文作者为Assum, I., Krause, J., Scheinhardt, M.O. 等。论文发表于*Nat Commun*（《自然-通讯》）**13**, 441 (2022)，DOI：https://doi.org/10.1038/s41467-022-27953-1。相关源代码可参见https://doi.org/https://doi.org/10.5281/zenodo.5094276 或 https://github.com/heiniglab/symatrial。 作者列表：Ines Assum^1,2,†、Julia Krause^3,4,†、Markus O. Scheinhardt⁵、Christian Müller^3,4、Elke Hammer^6,7、Christin S. Börschel^4,8、Uwe Vöker^6,7、Lenard Conradi⁹、Bastiaan Geelhoed^4,8,10、Tanja Zeller^3,4,*、Renate B. Schnabel^4,8,*、Matthias Heinig^1,2,11,*。注：^†代表共同第一作者，^*代表共同通讯作者。 作者单位： ¹ 计算健康中心，亥姆霍兹慕尼黑中心——德国亥姆霍兹联合会健康与环境研究有限公司（Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)），纽赫贝格，德国。 ² 信息学系，慕尼黑工业大学，慕尼黑，德国。 ³ 心血管科学大学中心，汉堡大学心脏与血管中心，汉堡，德国。 ⁴ 汉堡/基尔/吕贝克合作站点，德国心血管研究中心（Deutsches Zentrum für Herz-Kreislauf-Forschung, DZHK），汉堡，德国。 ⁵ 医学生物统计学与统计研究所，吕贝克大学，吕贝克，德国。 ⁶ 跨学科遗传学与功能基因组学研究所，格赖夫斯瓦尔德大学医学中心，格赖夫斯瓦尔德，德国。 ⁷ 格赖夫斯瓦尔德合作站点，德国心血管研究中心（DZHK），格赖夫斯瓦尔德，德国。 ⁸ 心脏科，汉堡大学心脏与血管中心，汉堡，德国。 ⁹ 心血管外科，汉堡大学心脏与血管中心，汉堡，德国。 ¹⁰ 心脏科，格罗宁根大学医学中心，格罗宁根，荷兰。 ¹¹ 慕尼黑合作站点，德国心血管研究中心（DZHK），慕尼黑，德国。 ## 摘要 心房颤动（atrial fibrillation, AF）的全基因组关联研究（genome-wide association study, GWAS）已发现众多疾病相关遗传变异，但其潜在分子机制，尤其是对信使RNA（messenger RNA, mRNA）和蛋白质表达的调控效应仍不甚明晰。因此，亟需采用精细化多组学方法解析其潜在分子调控网络。本研究通过一项横断面研究，整合人类心房组织的基因组学、转录组学与蛋白质组学数据，以探明遗传变异对转录本（顺式表达数量性状位点，cis-eQTL）和蛋白质（顺式蛋白质数量性状位点，cis-pQTL）丰度的广泛调控作用。我们进一步建立了一种基于多基因风险评分（polygenic risk score, PRS）的新型靶向反式数量性状位点（transQTL）分析方法，以筛选心房颤动核心候选基因。通过该方法，我们针对心房颤动GWAS关联位点鉴定出2个反式eQTL（trans-eQTL）和5个反式pQTL（trans-pQTL），并阐明了转录因子NKX2-5作为GWAS单核苷酸多态性（single nucleotide polymorphism, SNP）rs9481842与心房颤动之间的调控桥梁的作用。综上，本研究提出了一种可在小样本数据集中鉴定反式调控网络的整合多组学方法，并为心血管疾病基因优先级排序提供了丰富的心房组织特异性转录本和蛋白质水平调控变异资源。本版本新增了所有QTL结果的效应等位基因参考文件，并为所有QTL SNPs补充了基因型与等位基因频率信息。 ## 目录 1. 效应等位基因参考文件 * `map_AFHRI_B_effect_alleles.txt` 2. 基因型与等位基因频率参考文件（基于PLINK软件计算） * `genotype_allele_frequencies_eQTL_SNPs.txt` * `genotype_allele_frequencies_pQTL_SNPs.txt` * `genotype_allele_frequencies_resQTL_SNPs.txt` 3. 单组学顺式QTL（cis-QTL）结果 * 顺式eQTL（cis-eQTL，所有位点对，含连锁不平衡（linkage disequilibrium, LD）聚类信息） * `eQTL_right_atrial_appendage_allpairs_clump.txt` * 顺式pQTL（cis-pQTL，所有位点对，含LD聚类信息） * `pQTL_right_atrial_appendage_allpairs_clump.txt` * 顺式res eQTL（cis-res eQTL，所有位点对，含LD聚类信息） * `res_eQTL_right_atrial_appendage_allpairs_clump.txt` * 顺式res pQTL（cis-res pQTL，所有位点对，含LD聚类信息） * `res_pQTL_right_atrial_appendage_allpairs_clump.txt` * 顺式比值QTL（cis-ratioQTL，所有位点对，含LD聚类信息） * `ratioQTL_right_atrial_appendage_allpairs_clump.txt` 4. 功能性cis-QTL分类及eQTL/pQTL重叠分析 * 同时存在显著eQTL和pQTL的所有SNP-基因对的eQTL、pQTL、res eQTL、res pQTL及ratioQTL结果（假发现率false discovery rate, FDR<0.05） * `Fig2a_source_data_Shared_eQTL_pQTL_clump.txt` * 仅存在显著eQTL但无pQTL的所有SNP-基因对的eQTL、pQTL、res eQTL、res pQTL及ratioQTL结果（FDR<0.05） * `Fig2b_source_data_Independent_eQTL_clump.txt` * 仅存在显著pQTL但无eQTL的所有SNP-基因对的eQTL、pQTL、res eQTL、res pQTL及ratioQTL结果（FDR<0.05） * `Fig2c_source_data_Independent_pQTL_clump.txt` 5. QTS排名与富集分析结果 * eQTS排名与富集分析 * `TableS6_source_data_eQTS_ranking.txt` * `TableS7_source_data_eQTS_GSEA_results.txt` * pQTS排名与富集分析 * `TableS8_source_data_pQTS_ranking.txt` * `TableS9_source_data_pQTS_GSEA_results.txt` 6. 反式QTL（trans-QTL）结果 * 所有检测到的位点对，包括针对trans-eQTL的trans-pQTL结果及针对trans-pQTL的trans-eQTL结果 * `Table2_source_data_Trans-QTL_results.txt`