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通过将临床表型与可量化的免疫库成分联系起来解释GWAS

Interpretable GWAS by linking clinical phenotypes to quantifiable immune repertoire components

Nature 等信源发布 2024-10-20 00:45

可切换为仅中文

AbstractBridging the gap between genotype and phenotype in GWAS studies is challenging. A multitude of genetic variants have been associated with immune-related diseases, including cancer, yet the interpretability of most variants remains low. Here, we investigate the quantitative components in the T cell receptor (TCR) repertoire, the frequency of clusters of TCR sequences predicted to have common antigen specificity, to interpret the genetic associations of diverse human diseases.

摘要弥合GWAS研究中基因型和表型之间的差距具有挑战性。许多遗传变异与包括癌症在内的免疫相关疾病有关，但大多数变异的可解释性仍然很低。在这里，我们研究了T细胞受体（TCR）库中的定量成分，即预测具有共同抗原特异性的TCR序列簇的频率，以解释多种人类疾病的遗传关联。

We first developed a statistical model to predict the TCR components using variants in the TRB and HLA loci. Applying this model to over 300,000 individuals in the UK Biobank data, we identified 2309 associations between TCR abundances and various immune diseases. TCR clusters predicted to be pathogenic for autoimmune diseases were significantly enriched for predicted autoantigen-specificity.

我们首先开发了一个统计模型，以使用TRB和HLA基因座中的变体预测TCR成分。将该模型应用于英国生物库数据中的300000多个人，我们确定了TCR丰度与各种免疫疾病之间的2309个关联。预测对自身免疫性疾病具有致病性的TCR簇显着富集了预测的自身抗原特异性。

Moreover, four TCR clusters were associated with better outcomes in distinct cancers, where conventional GWAS cannot identify any significant locus. Collectively, our results highlight the integral role of adaptive immune responses in explaining the associations between genotype and phenotype..

此外，四个TCR集群与不同癌症的更好结果相关，传统的GWAS无法识别任何重要的基因座。总的来说，我们的结果突出了适应性免疫反应在解释基因型和表型之间的关联中的不可或缺的作用。。

IntroductionGenome-wide association studies (GWAS) have successfully identified numerous genetic variants linked to autoimmune diseases and cancers1,2. However, the mechanisms underlying these associations remain largely obscure3. Identification of disease-associated genes and cellular processes have partially explained disease etiology4,5,6,7, and immune phenotypes, such as immune cell proportion and cytokine production, also provided mechanistic insights into some diseases8,9.

引言全基因组关联研究（GWAS）已成功鉴定出许多与自身免疫性疾病和癌症相关的遗传变异1,2。然而，这些关联背后的机制在很大程度上仍然不清楚3。疾病相关基因和细胞过程的鉴定部分解释了疾病的病因4,5,6,7，免疫表型，如免疫细胞比例和细胞因子产生，也为某些疾病提供了机制见解8,9。

However, direct associations between the T cell receptor (TCR) repertoire, a critical component in the immune system, remain insufficiently explored. The TCR is pivotal in adaptive immune response10,11, as it recognizes antigen epitopes bounded by the human leukocyte antigen (HLA) molecule, which is encoded by the major histocompatibility complex (MHC) locus in Chromosome 6.

然而，T细胞受体（TCR）库（免疫系统的关键组成部分）之间的直接关联仍未得到充分探索。TCR在适应性免疫反应中至关重要10,11，因为它识别由人类白细胞抗原（HLA）分子结合的抗原表位，该分子由6号染色体上的主要组织相容性复合体（MHC）基因座编码。

T cells participate in a wide spectrum of human diseases, including autoimmune diseases, cancers, and infectious diseases12. Therefore, understanding the genetic influence of TCR repertoire will provide insights into the disease susceptibility, etiology and clinical outcome in the general population.

。因此，了解TCR库的遗传影响将为普通人群的疾病易感性，病因和临床结果提供见解。

Genetic variants influencing TCR sequence generation have been identified13,14,15,16, setting the stage to investigate the impact of these genotype-TCR associations on disease predisposition.A recent study provided genetic evidence supporting the hypothesis that HLA risk alleles shape T cell repertoire during thymic selection15.

。最近的一项研究提供了遗传证据，支持HLA风险等位基因在胸腺选择过程中形成T细胞库的假设15。

However, their association analysis was limited to three autoimmune diseases (celiac disease (CD), type 1 diabetes (T1D), and rheumatoid arthritis (RA)), without covering the effects in other diseases. Besides, they examined only known autoimmune risk variants, whereas TCR repertoires could be influenced.

然而，他们的关联分析仅限于三种自身免疫性疾病（乳糜泻（CD），1型糖尿病（T1D）和类风湿性关节炎（RA）），而没有涵盖其他疾病的影响。此外，他们仅检查了已知的自身免疫风险变异，而TCR库可能会受到影响。

Data availability

数据可用性

Raw data analyzed in this study are available at the following locations: dbGaP: phs001442, phs001918; ImmuneAccess database: https://doi.org/10.21417/B7001Z, https://doi.org/10.21417/B7H01M, https://doi.org/10.21417/B7C88S, https://doi.org/10.21417/LWL2022JCP. Access to UK Biobank individual-level data can be requested from https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.

本研究中分析的原始数据可在以下位置获得：dbGaP:phs001442，phs001918；ImmuneAccess数据库：https://doi.org/10.21417/B7001Z，https://doi.org/10.21417/B7H01M，https://doi.org/10.21417/B7C88S，https://doi.org/10.21417/LWL2022JCP.访问英国生物库个人层面的数据可以从https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.

The weights of the lasso models are deposited at GitHub: https://github.com/YuhaoTan2/RfuWAS/blob/main/models/lasso_weights_tsv.zip..

套索模型的权重存放在GitHub：https://github.com/YuhaoTan2/RfuWAS/blob/main/models/lasso_weights_tsv.zip..

Code availability

代码可用性

All code used in the study is available at https://github.com/YuhaoTan2/RfuWAS and deposit at Zenodo (https://doi.org/10.5281/zenodo.13646450)68. Other software used includes TRUST4; GRAF-pop 1.0 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetZip.cgi?zip_name=GrafPop1.0.tar.gz); plink 2.0 (https://www.cog-genomics.org/plink/2.0/); Michigan Imputation Server 1.2.4 (https://imputationserver.sph.umich.edu/index.html#!); PEER 1.3 (https://github.com/PMBio/peer.git); GCTA 1.94.1 (https://yanglab.westlake.edu.cn/software/gcta/bin/gcta-1.94.1-linux-kernel-3-x86_64.zip); CreateUKBphenome (https://github.com/umich-cphds/createUKBphenome); MatrixEQTL 2.3 (https://cran.r-project.org/web/packages/MatrixEQTL/index.html); PrediXcan 0.6.11 (https://github.com/hakyimlab/MetaXcan); GSEA 4.3.2 (https://www.gsea-msigdb.org/gsea/index.jsp); EnrichmentMap 3.3.5 (https://enrichmentmap.readthedocs.io/en/latest/); AutoAnnotate 1.4 (https://autoannotate.readthedocs.io/en/latest/)..

研究中使用的所有代码均可在https://github.com/YuhaoTan2/RfuWAS并在Zenodo存款(https://doi.org/10.5281/zenodo.13646450)68、使用的其他软件包括TRUST4；GRAF pop 1.0(https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetZip.cgi?zip_name=GrafPop1.0.tar.gz)；plink 2.0(https://www.cog-genomics.org/plink/2.0/)；密歇根插补服务器1.2.4(https://imputationserver.sph.umich.edu/index.html#！）；同行1.3(https://github.com/PMBio/peer.git)；GCTA 1.94.1(https://yanglab.westlake.edu.cn/software/gcta/bin/gcta-1.94.1-linux-kernel-3-x86_64.zip)；创建UKBphenome(https://github.com/umich-cphds/createUKBphenome)；(https://cran.r-project.org/web/packages/MatrixEQTL/index.html)；PrediXcan 0.6.11(https://github.com/hakyimlab/MetaXcan)；GSEA 4.3.2(https://www.gsea-msigdb.org/gsea/index.jsp)；富集图3.3.5(https://enrichmentmap.readthedocs.io/en/latest/)；自动注释1.4(https://autoannotate.readthedocs.io/en/latest/)。。

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Tan, Y. The code repo for “Interpretable GWAS by linking clinical phenotypes to quantifiable immune repertoire components”. Zenodo, https://doi.org/10.5281/zenodo.13646450 (2024).Download referencesAcknowledgementsThis work is supported by NCI R01 CA258524 (B.L.), CA245318 (B.L.), NIAID U01 AI169298 (X.Z.), R01 AI174108 (D.J.L.), U01 AI185638 (D.J.L.), and NHGRI R01 HG011035 (D.J.L.).

Tan，Y.“通过将临床表型与可量化的免疫库成分联系起来来解释GWAS”的代码repo。泽诺多，https://doi.org/10.5281/zenodo.13646450（2024年）。下载参考文献致谢这项工作得到了NCI R01 CA258524（B.L.），CA245318（B.L.），NIAID U01 AI169298（X.Z.），R01 AI174108（D.J.L.），U01 AI185638（D.J.L.）和NHGRI R01 HG011035（D.J.L.）的支持。

We acknowledge Philip Bradley for discussion on the project.Author informationAuthor notesThese authors jointly supervised this work: Dajiang J. Liu, Xiaowei Zhan, Bo Li.Authors and AffiliationsGraduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USAYuhao Tan & Bo LiCenter for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USAYuhao Tan, Hongyi Zhang, Mingyao Pan & Bo LiDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USAYuhao Tan, Hongyi Zhang, Mingyao Pan & Bo LiInstitute for Personalized Medicine, College of Medicine, Pennsylvania State University, Hershey, PA, USALida Wang & Dajiang J.

我们感谢菲利普·布拉德利对该项目的讨论。作者信息作者注意到这些作者共同监督了这项工作：刘大江J.刘，詹晓伟，李波。作者和附属机构宾夕法尼亚大学佩雷尔曼医学院基因组学和计算生物学研究生组，宾夕法尼亚州费城，19104年，美国宾夕法尼亚州费城儿童医院计算和基因组医学许可中心，宾夕法尼亚州费城，美国宾夕法尼亚大学病理学和检验医学系宾夕法尼亚州立大学，宾夕法尼亚州赫尔希，USALida Wang和Dajiang J。

LiuQuantitative Biomedical Research Center, Peter O’Donnell School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USAXiaowei ZhanAuthorsYuhao TanView author publicationsYou can also search for this author in.

美国德克萨斯州达拉斯德克萨斯大学西南医学中心Peter O'Donnell公共卫生学院LiuQuantitative生物医学研究中心詹晓伟作者余浩TanView作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsB.L., X. Z., D.J.L., and Y.T. conceived the project. Y.T. developed the framework and performed analysis. L.W., H.Z., M.P., B.L., X. Z., and D.J.L. helped with data interpretation. B.L., X.Z., D.J.L., and Y.T. facilitated dataset access. Y.T., B.L., X.Z., and D.J.L.

PubMed谷歌学术贡献b。五十、 ，X.Z.，D.J.L。和Y.T.构思了这个项目。Y、 T.开发了框架并进行了分析。五十、 W.，H.Z.，M.P.，B.L.，X.Z。和D.J.L.帮助进行数据解释。B、 L.，X.Z.，D.J.L。和Y.T.促进了数据集访问。Y、 T.，B.L.，X.Z。和D.J.L。

prepared the manuscript. B.L., X.Z., and D.J.L. supervised the study.Corresponding authorsCorrespondence to.

准备了手稿。B、 。通讯作者通讯。

Dajiang J. Liu, Xiaowei Zhan or Bo Li.Ethics declarations

刘大江，詹晓伟或李波。道德宣言

Competing interests

相互竞争的利益

The authors declare no competing interests.

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