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Genotype-Tissue Expression Project (GTEx)

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Genotype-Tissue Expression Project (GTEx)

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Genotype-Tissue Expression Project (GTEx)

An NIH Common Fund Project

The aim of the Genotype - Tissue Expression (GTEx) Project is to increase our understanding of how changes in our genes contribute to common human diseases, in order to improve health care for future generations.

GTEx Publishes Final Dataset (V8)

On Sept. 11, 2020, the final set of analyses from the GTEx Consortium were published in Science.  The latest GTEx data release represents the largest atlas of human gene expression and catalog of trait loci to date.

Overview

Launched by the National Institutes of Health (NIH) in September 2010 (See: NIH launches Genotype-Tissue Expression project), GTEx will create a resource that researchers can use to study how inherited changes in genes lead to common diseases. It will establish a database and a tissue bank that can be used by many researchers around the world for future studies.

GTEx researchers are studying genes in different tissues obtained from many different people. Thus every donor's generous gift of tissues and medical information to the GTEx project makes possible research that will help improve our understanding of diseases, giving hope that we will find better ways to prevent, diagnose, treat and eventually cure these diseases in the future.

In addition, the GTEx project includes a study to explore the effectiveness of the GTEx donor consent process. We hope to better understand how participating in the study might affect the attitudes, beliefs and feelings of donors and the families of deceased donors using interviews and surveys of participants and their families. This study will help ensure that the consent process and other aspects of the project effectively address the concerns and expectations of participants in the study.

GTEx is a pioneering project that uses state-of-the-art protocols for obtaining and storing a large range of organs and tissues and for testing them in the lab. These tissues and organs are collected and stored through the National Cancer Institute's cancer Human Biobank initiative on behalf of GTEx. Until now, no project has analyzed genetic variation and expression in as many tissues in such a large population as planned for GTEx.

GTEx is funded through the NIH Common Fund, which supports innovative projects involving multiple NIH Institutes. GTEx is managed by the NIH Office of the Director, in partnership with the National Human Genome Research Institute, National Institute of Mental Health, National Cancer Institute, and numerous other NIH institutes. Additional information about the NIH Common Fund can be found at http://commonfund.nih.gov.

To learn more about the science behind the GTEx project, we invite you to visit: http://commonfund.nih.gov/GTEx.

Overview

Launched by the National Institutes of Health (NIH) in September 2010 (See: NIH launches Genotype-Tissue Expression project), GTEx will create a resource that researchers can use to study how inherited changes in genes lead to common diseases. It will establish a database and a tissue bank that can be used by many researchers around the world for future studies.

GTEx researchers are studying genes in different tissues obtained from many different people. Thus every donor's generous gift of tissues and medical information to the GTEx project makes possible research that will help improve our understanding of diseases, giving hope that we will find better ways to prevent, diagnose, treat and eventually cure these diseases in the future.

In addition, the GTEx project includes a study to explore the effectiveness of the GTEx donor consent process. We hope to better understand how participating in the study might affect the attitudes, beliefs and feelings of donors and the families of deceased donors using interviews and surveys of participants and their families. This study will help ensure that the consent process and other aspects of the project effectively address the concerns and expectations of participants in the study.

GTEx is a pioneering project that uses state-of-the-art protocols for obtaining and storing a large range of organs and tissues and for testing them in the lab. These tissues and organs are collected and stored through the National Cancer Institute's cancer Human Biobank initiative on behalf of GTEx. Until now, no project has analyzed genetic variation and expression in as many tissues in such a large population as planned for GTEx.

GTEx is funded through the NIH Common Fund, which supports innovative projects involving multiple NIH Institutes. GTEx is managed by the NIH Office of the Director, in partnership with the National Human Genome Research Institute, National Institute of Mental Health, National Cancer Institute, and numerous other NIH institutes. Additional information about the NIH Common Fund can be found at http://commonfund.nih.gov.

To learn more about the science behind the GTEx project, we invite you to visit: http://commonfund.nih.gov/GTEx.

Donors

The generosity of donors and donor families make this project possible. The goal of GTEX is to increase our understanding of how changes in genes contribute to common human diseases. This knowledge will improve health care for future generations.

GTEx will create information that will be useful to many researchers, studying many different diseases. The gift of your tissue or your loved one's tissue may lead to research which could help improve treatment for many people in the future.

There are two types of donor groups that participate in the GTEx project: 1) organ and tissue donors, and 2) surgical donors.

Organ and tissue donors include individuals who have agreed to donate organs (like kidneys, heart, and liver) and/or tissues (like bone and cornea) for use as medical transplants after they died. Family members may also make the decision to give consent for organ or tissue donation after their loved one has passed on. These donors or their family members have the opportunity to indicate whether any organs or tissues ineligible for transplants may be donated to benefit research studies like GTEx. Donating to GTEx would not interfere with the use of the organ or tissues for transplantation, which takes priority. Compared to surgical donors, many more types of tissues can be obtained for research studies from organ and tissue donors. People who may not qualify to donate organs or tissue for transplants may still qualify to donate tissues to GTEx for research.

 

Surgical tissue donors include people who undergo certain kinds of surgery. If a surgery patient agrees ahead of time, tiny amounts of tissue removed during surgery, such as fat, skin, or muscle, can be donated for use in the GTEx project. Only tissue which needs to be removed for medical reasons can be donated to the GTEx project. Donating to the GTEx project will not cause any additional tissue to be removed.

 

GTEx Findings

It has been said that someone has "good genes" when they are particularly healthy, but what does that mean? How does understanding of genetics translate into better health? NIH designed the Genotype Tissue Expression (GTEx) project to start to answer this question. The project is looking at the differences in people's genes.

Genes are made up of DNA and DNA is made up of different pieces too. One of GTEx's goals is to identify the pieces of DNA that control how genes behave. These pieces of DNA are called expression quantitative trait loci or eQTLs. These eQTLs control the behavior of genes like a thermostat regulates the temperature of a home. GTEx studies found that the number of eQTLs varies from person to person and from tissue to tissue. Researchers also discovered eQTLs act in different ways. Some eQTLs may affect a set of genes in one tissue, while other eQTLs affect genes in many tissues.

The GTEx consortium has also built an eQTL web-browser (http://www.gtexportal.org/home/) to help visualize and discover new relationships between genes and the DNA that affects them. This website provides a resource for the many researchers who are exploring the human genome. Understanding how the eQTLs change gene behavior in different tissues can help us understand how diseases develop in people. This knowledge, in turn, may help us develop new therapies and treatments, improving our health overall.

Donors

The generosity of donors and donor families make this project possible. The goal of GTEX is to increase our understanding of how changes in genes contribute to common human diseases. This knowledge will improve health care for future generations.

GTEx will create information that will be useful to many researchers, studying many different diseases. The gift of your tissue or your loved one's tissue may lead to research which could help improve treatment for many people in the future.

There are two types of donor groups that participate in the GTEx project: 1) organ and tissue donors, and 2) surgical donors.

Organ and tissue donors include individuals who have agreed to donate organs (like kidneys, heart, and liver) and/or tissues (like bone and cornea) for use as medical transplants after they died. Family members may also make the decision to give consent for organ or tissue donation after their loved one has passed on. These donors or their family members have the opportunity to indicate whether any organs or tissues ineligible for transplants may be donated to benefit research studies like GTEx. Donating to GTEx would not interfere with the use of the organ or tissues for transplantation, which takes priority. Compared to surgical donors, many more types of tissues can be obtained for research studies from organ and tissue donors. People who may not qualify to donate organs or tissue for transplants may still qualify to donate tissues to GTEx for research.

 

Surgical tissue donors include people who undergo certain kinds of surgery. If a surgery patient agrees ahead of time, tiny amounts of tissue removed during surgery, such as fat, skin, or muscle, can be donated for use in the GTEx project. Only tissue which needs to be removed for medical reasons can be donated to the GTEx project. Donating to the GTEx project will not cause any additional tissue to be removed.

 

GTEx Findings

It has been said that someone has "good genes" when they are particularly healthy, but what does that mean? How does understanding of genetics translate into better health? NIH designed the Genotype Tissue Expression (GTEx) project to start to answer this question. The project is looking at the differences in people's genes.

Genes are made up of DNA and DNA is made up of different pieces too. One of GTEx's goals is to identify the pieces of DNA that control how genes behave. These pieces of DNA are called expression quantitative trait loci or eQTLs. These eQTLs control the behavior of genes like a thermostat regulates the temperature of a home. GTEx studies found that the number of eQTLs varies from person to person and from tissue to tissue. Researchers also discovered eQTLs act in different ways. Some eQTLs may affect a set of genes in one tissue, while other eQTLs affect genes in many tissues.

The GTEx consortium has also built an eQTL web-browser (http://www.gtexportal.org/home/) to help visualize and discover new relationships between genes and the DNA that affects them. This website provides a resource for the many researchers who are exploring the human genome. Understanding how the eQTLs change gene behavior in different tissues can help us understand how diseases develop in people. This knowledge, in turn, may help us develop new therapies and treatments, improving our health overall.

Progress

As of December 2015, GTEx finished enrollment of the additional donors, for a total of 961 donors. Analysis of the samples and data will continue for another 18 months. Over 30,000 samples have been collected.

In fall of 2015, information on gene expression for over 450 donors was released to the scientific community through the database of Genotype and Phenotype (dbGaP). Additionally, the new version of the GTEx Genome Browser has been launched and features new visualization tools.

In 2014, The National Institutes of Health awarded eight new grants to researchers to use tissues donated to GTEx to explore how human genes are expressed and regulated in different tissues.

In 2020, the GTEx Consortium published its final set of studies analyzing genotype data from approximately 948 post-mortem donors and approximately 17,382 RNA-seq samples across 54 tissue sites and 2 cell lines, with adequate power to detect Expression Quantitative Trait Loci in 48 tissues.

Progress

As of December 2015, GTEx finished enrollment of the additional donors, for a total of 961 donors. Analysis of the samples and data will continue for another 18 months. Over 30,000 samples have been collected.

In fall of 2015, information on gene expression for over 450 donors was released to the scientific community through the database of Genotype and Phenotype (dbGaP). Additionally, the new version of the GTEx Genome Browser has been launched and features new visualization tools.

In 2014, The National Institutes of Health awarded eight new grants to researchers to use tissues donated to GTEx to explore how human genes are expressed and regulated in different tissues.

In 2020, the GTEx Consortium published its final set of studies analyzing genotype data from approximately 948 post-mortem donors and approximately 17,382 RNA-seq samples across 54 tissue sites and 2 cell lines, with adequate power to detect Expression Quantitative Trait Loci in 48 tissues.

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GTEx数据库简介（1） - 知乎

GTEx数据库简介（1） - 知乎切换模式写文章登录/注册GTEx数据库简介（1）HuaMD医学大数据分享医学大数据知识----医学大数据及其综合分析（四）Hua+医学大数据 出品（转载请注明出处链接，翻版必究）（HuaPlusMD通过整合多种人类和动物数据库，建立了可靠的大数据库，为您提供疾病动物模型和临床大数据综合分析。链接：https://www.huaplusmd.com）前言：“大数据”概念早已出现，目前我们对（医学）大数据了解有多少呢？本平台将对医学大数据进行系统的介绍，并对大数据综合分析进行分享（每周更新）。分享的内容将主要涵盖大数据库（基因、蛋白数据库等）/生物银行介绍（UK Biobank, Finnish Biobanks, China Kadoorie Biobank, BioBank Japan, TCGA, GWAS catalog，GTEx等），疾病动物模型数据库（如GeneNetwork, BXD），大数据库的综合使用（如Mendelian randomization），组学数据分析等。（分享的其他系列内容请见：https://www.huaplusmd.com/knowledge） 每个个体的不同的器官组织的基因（Gene）都是相同的，但为什么有的表型为肝脏组织，帮助人类代谢？有的是肌肉组织，帮助人类运动？其原因是，不同的人体组织表达的基因并不相同。GTEx项目，通过收集健康人体的不同组织样本，尝试了解人类不同组织/器官的特异性基因表达。 从本期开始，我们将介绍GTEx数据库。这是一个值得大家深度学习的数据库。GTEx项目，全称Genotype-Tissue Expression (基因型-组织表达) ，主要由美国NIH（国立卫生研究院）的公共基金计划连续资助了10年（2010-2019）的项目。（特别希望我国也能支持，这种长期的大队列的人体基础研究，能使非敏感数据开源，接受国际同行的评议。功在当代、利在千秋！） GTEx项目是用来研究人类不同组织的特异性基因表达和调节的。GTEx 项目最终的数据库（第八版，V8），包括来自于838位生前健康的人类捐献者的DNA数据（包含Whole Genome Sequencing (WGS) 和 Whole Exome Sequencing (WES)）；17382 份RNA-seq 数据，其来自于近1000个人类个体，涵盖54个不同组织器官部位（目前世界唯一能收集这么全的健康人体组织样本）；以及2个来自捐献者血液和皮肤的细胞系。该数据库应用：· 评价不同组织特异性基因表达和调节；· 进行GWAS研究 (genome-wide association study)；· 可以用来探索遗传变异对复杂疾病和特征的影响。应用举例：GTEx的研究人员，通过GTEx数据库，设计一种统计方法，称为PrediXcan，该方法能够通过基因序列，推测基因的活性或表达量；然后，PrediXcan能够将推测的基因活性和观测到的疾病特征相关联，从而预测疾病。PrediXcan已经成功地发现与多种疾病相关的特异基因，这些疾病包括 冠状动脉疾病、克罗恩病、类风湿性关节炎、 1 型糖尿病 和 双相情感障碍。 该项目创建了GTEx Portal（https://gtexportal.org/home/），该平台提供开放获取的数据，包括基因表达、QTLs 及 生理组织学 图片。 GTEx项目，也同时建立了自己的生物银行（https://gtexportal.org/home/biobank），包含来自约960位生前健康的捐赠者的组织标本的，包括肺脏、脑、胰腺、皮肤等等。如果需要，还可以申请获取留存的生物样本。GTEx联盟，在世界顶刊上Science, Nature上发表的代表性文章列表：· 2015年，GTEx项目发布了第一个阶段性成果，一次性在Science上发表3篇研究成果：The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humansThe GTEx Consortium.Science. 8 May 2015. 348(6235):648-660. doi:10.1126/science. PMID: 25954001 The human transcriptome across tissues and individualsMelé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J et al.Science. 8 May 2015. 348(6235):660-665. doi: 10.1126/science.aaa0355 Effect of predicted protein-truncating genetic variants on the human transcriptomeRivas MA, Pirinen M, Conrad DF, Lek M, Tsang EK et al.Science. 8 May 2015. 348(6235):666-669. doi:10.1126/science.1261877. · 2017年，GTEx项目发布了进一步成果，一次性在Nature发表4篇研究成果:Genetic effects on gene expression across human tissuesThe GTEx Consortium.Nature. 12 Oct 2017. 550: 204-213. Epub 11 Oct 2017. doi:10.1038/nature24277The impact of rare variation on gene expression across tissuesLi X, Kim Y, Tsang EK, Davis JR, Damani FN et al.Nature. 12 Oct 2017. 550: 239-243. Epub 11 Oct 2017. doi:10.1038/nature24267Landscape of X chromosome inactivation across human tissuesTukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL et al.Nature. 12 Oct 2017. 550: 244-248. Epub 11 Oct 2017. doi:10.1038/nature24265Dynamic landscape and regulation of RNA editing in mammalsTan MH, Li Q, Shanmugam R, Piskol R, Kohler J et al.Nature. 12 Oct 2017. 550:249-254. Epub 11 Oct 2017. doi:10.1038/nature24041· 2019-2022年，GTEx项目又连续发布了项目的成果，在Science发表7篇研究成果:2022Single-nucleus cross-tissue molecular reference maps toward understanding disease gene functionEraslan G, et al.Science. 376 (abl4290), 13 May 2022. doi:10.1126/science.abl42902020The GTEx Consortium atlas of genetic regulatory effects across human tissuesThe GTEx Consortium.Science. 369 (1318-1330), 10 Sep 2020. doi:10.1126/science.aaz1776Cell type specific genetic regulation of gene expression across human tissuesKim-Hellmuth* S, Aguet* F, Oliva M, Muñoz-Aguirre M, Kasela S, et al.Science. 369 (eaaz8528), 10 Sep 2020. doi:10.1126/science.aaz8528Transcriptomic signatures across human tissues identify functional rare genetic variationFerraro* NM, Strober* BJ, Einson J, Abell NS, Aguet F, et al.Science. 369 (aaz5900), 10 Sep 2020. doi:10.1126/science.aaz5900Determinants of telomere length across human tissuesDemanelis K, Jasmine F, Chen LS, Chernoff M, Tong L, et al.Science. 369 (aaz6876), 10 Sep 2020. doi:10.1126/science.aaz6876The impact of sex on gene expression across human tissuesOliva* M, Muñoz-Aguirre* M, Kim-Hellmuth* S, Wucher V, Gewirtz ADH, et al.Science. 369 (aba3066), 10 Sep 2020. doi:10.1126/science.aba30662019RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissuesYizhak K, Aguet F, Kim J, Hess JM, Kübler K et al.Science. 07 June 2019. 364(6444). doi:10.1126/science.aaw0726 如果你可以看youtube视频，可以看一下Prof. Eric Lander (Funding director, Broad Institute) 等对GTEx的简单介绍：https://www.youtube.com/watch?v=PhK186A7Ryo---end---—如果喜欢，快分享给你的朋友们吧—关注公众号，更多精彩内容等着你！原文链接：https://www.huaplusmd.com/knowledgeHua+医学大数据 出品 （医学大数据综合分析，HuaPlusMD坚持专业和认真）。如果您有医学大数据综合分析方面需求欢迎联系我们：https://www.huaplusmd.com/往期回顾：医学大数据及其综合分析（总纲）医学大数据及其综合分析（一）—— GEO数据库介绍 (1)医学大数据及其综合分析（一）—— GEO数据库介绍 (2)医学大数据及其综合分析（二）—— BXD小鼠数据库介绍 (1)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (2)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (3)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (4)医学大数据及其综合分析（三）—— eQTLGen Consortium数据库简介（1）医学大数据及其综合分析（三）—— eQTLGen Consortium数据库简介（2）医学大数据及其综合分析（X）—— 实例分析1：中年发福：人体代谢率 不背此锅新冠肺炎（COVID-19）的致死率参考文献：[1] https://commonfund.nih.gov/GTex.[2] https://gtexportal.org/home/发布于 2022-10-24 04:27大数据​赞同 18​​2 条评论​分享​喜欢​收藏​申请

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2小时搞定TCGA+GTEx联合分析，多1分钟算我输 - 知乎

2小时搞定TCGA+GTEx联合分析，多1分钟算我输 - 知乎切换模式写文章登录/注册2小时搞定TCGA+GTEx联合分析，多1分钟算我输益加医益加医——专注做医学科研与临床技能培训视频分享传播的医学公众号需要脚本文件的点击下面附件~——TCGA+GTEx联合分析脚本文件——.docx190.7K · 百度网盘导语通常我们在挖掘TCGA数据库的时候，会发现该项目纳入的正常组织测序结果是非常少的，也就是说很多病人都不会有他的正常组织的转录组测序结果比如说乳腺癌吧，1200个左右的转录组数据，其中1100左右都是肿瘤组织的测序数据，只有区区100个左右的正常对照。这个时候我们就需要想办法加大正常组织测序样本量，既然TCGA数据库没有，我们就从其他数据库着手。这里值得大力推荐的是GTEx数据库 ,Genotype-Tissue Expression (GTEx)1 数据准备GTEx（Genotype-Tissue Expression,基因型-组织表达）数据库，研究从来自449名生前健康的人类捐赠者的7000多份尸检样本，涵盖44个组织（42个不同的组织类型），包括31个实体器官组织、10个脑分区、全血、2个来自捐赠者血液和皮肤的细胞系，作者利用这些样本研究基因表达在不同组织和个体中有何差异。数据下载直接在GTEx官网下载，网站会较难进入，我们可以在UCSC xene网站对GTEx及TCGA的数据进行下载。首先，点击Launch Xena，进入到数据下载页面，然后点击上方的DATA STES，进入到数据集页面。在数据集页面，就包括有TCGA，TARGET及GTEx等多个数据库的界面。点击GTEX，进入到GTEX的数据下载页面。需要下载FPKM文件及表型文件。以FPKM文件为例，直接点击TOIL RSEM fpkm，进入到下载页面当中，然后点击下载栏的链接，就可以开始下载。同样的，表型文件的下载方法也是一样的。TCGA的数据，在数下载页面有两个，一个是GDC TCGA，一个是TCGA，一般选择GDC TCGA进行下载。进入GDC TCGA后，界面和GTEX的类似，不过表型文件，包括两个，一个是表型文件，一个是临床数据，这两个数据在后续分析中均会用到。表达文件同样也是下载FPKM文件。数据下载完成后，就可以进行数据的整理了。首先对GTEX的数据进行ID转换，首先将下载的压缩包进行解压，然后直接用脚本进行ID转换，注释文件为human.gtf，方法和我们之前对TCGA进行ID转换类似，通过命令提示符进行脚本的运行。运行结束后，会在文件夹中新生成一个GTExSymbol的文件。即转换后的文件。由于GTEX是对所有的组织的样本进行的测序，所以我们需要提取对应的组织样本的的表达信息。样本信息可以直接从之前下载的样本文件获得。解压后打开。在site中找到对应组织，然后将选好的样本编号放到新建的TXT文档中。然后运行脚本，将我们所需要的样本的表达数据提取出来。并且给出样本的数目。样本数目需要记住，因为后期差异分析需要用到。接下来，就可以整理TCGA文件了，将TCGA的FPKM压缩包解压，然后用perl处理文件。运行完成后，会给出正常样本和肿瘤样本的数目。和GTEX不同的是，需要在perl脚本后加上需要处理的文件的名字。整理完后的TCGA文件，会将正常样本和肿瘤样本分开。然后对TCGA的数据进行ID转换，方法和之前的TCGA方法转换基本相同。准备好注释文件human.gtf及脚本GTEx.symbol.pl。然后通过命令提示符运行脚本。这个脚本的名称和之前GTEx的ID转换脚本名称相同，但是脚本内容不同，在TCGA中，不需要对FPKM进行+1处理，而GTEX数据由于原始的FPKM是没有进行+1的，所以在ID转换时，进行了FPKM+1的处理。GTEx和TCGA的数据都整理好时候，就可以对GTEX和TCGA的数据进行合并了。输入文件包括两个，一个是GTEX中提取的数据文件和TCGA转换后的文件。数据的合并是采用的R语言，修改路径后直接运行即可。运行结束后，在文件夹中会生成一个新的mere文件，即为合并后的GTEX和TCGA的合并文件。有了这个文件，就可以进行后续的差异分析等步骤了。2GTEX图形绘制因为GTEX是对人体中各个组织的表达数据，因此我们可以统计基因在每个组织中的表达量，因此我们可以绘制解剖图，箱型图等图形。首先统计每个组织中的表达情况。首先准备好GTEX的表型文件及基因表达文件。对表型文件进行整理，将表型文件中的病人ID，组织及性别复制到一个新的txt文档中。文档命名为site，因为后续脚本会识别文件名称。准备好位点文件和表达文件后，就可以运行脚本，对表达文件和位点文件进行合并了，并输出后续绘图所需的文件。因为绘制解剖图，只能针对某一个基因绘制，因此我们在合并时需要输入基因名称，这个基因一定要存在于表达文件中，并且要保持名字和表达文件中的名字一致。比如TP53。运行完成后会将男性和女性分别生存一个文件，并生存一个表达和位点的合并文件。接下来，就可以绘制解剖图和箱图了。解剖图包括两个，一个是男性的，一个女性的。修改脚本中的运行路径，直接运行即可。直接运行脚本，就可以看到TP53基因在各个组织中的表达情况了。随后，我们还可以绘制TP53在各个组织中的表达箱图。同样的，修改路径后直接运行脚本即可。3差异分析差异分析所用到的文件就是之前合并好的merge文件。这里要注意修改正常样本和肿瘤样本，其中正常样本应该是TCGA正常样本数加GTEX正常样本数。运行结束后，会和我们之前做差异分析的结果一样，会给出差异表达文件，差异基因表达值文件等等。然后我们就可以绘制常见的表达热图。热图绘制修改好路径及样本数目后，直接运行脚本即可。差异分析后，我们就可以进行生存分析，一次性将所有的差异基因的生存分析结果进行输出，首先准备生存分析所需的文件。生存文件从之前下载的TCGA生存文件下载下来就可以了。仅保存生存状态，生存时间和样本ID。然后把表头进行一下修改，把生存时间挪动到第二列。其中生存状态1表示死亡，0表示存活。将整理好的文件重新复制粘贴到新建的一个time.txt文件中。这样生存分析所需的文件都准备好了。接下来就可以进行临床数据和表达数据的合并了。然后通过命令提示符，运行GTEx.mergeExpTime.pl脚本。运行完成后，就可以获得合并后的文件。获得合并后的文件后，就可以对差异基因进行批量的生存分析了。就可以获得所有差异基因的生存曲线了，但是图片只生存生存显著性p<0.05图片，同时会生存一个survival文件，该文件就包括差异基因的生存p值。4功能分析首先进行ID转换，转换方法跟之前分享过的方法是一样的，将Genesymbol和logFC粘贴到新的txt文档中，然后运行R脚本。获得转换后的ID后，就可以进行GO和KEGG富集分析，并生存GO和KEGG的富集图。这里提供了两种图形输出的脚本，一个是输出常见的柱状图和气泡图，这两个图形采用GO和KEGG脚本即可。另外一种，则是输出GO和KEGG的圈图。全图脚本中是没有富集的脚本的，但是出图时需要富集结果，所以单独绘制圈图的时候，需要先进行GO和KEGG富集，并生成相关文件，富集脚本参考上一个主图和气泡图绘制的脚本即可。本文由公众号益加医原创，如需转载请在公众号后台回复“转载”即可。需要脚本文件的点击下面附件~——TCGA+GTEx联合分析脚本文件——.docx190.7K · 百度网盘编辑于 2020-08-10 16:23医学教育临床医学医学院​赞同 96​​49 条评论​分享​喜欢​收藏​申请

GTEx - Database Commons

GTEx - Database Commons

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GTEx

General information

URL:

https://www.gtexportal.org

Full name:

Genotype-Tissue Expression

Description:

GTEx established a data resource and tissue bank to study the relationship between genetic variation and gene expression in multiple human tissues. This release includes genotype data from approximately 714 donors and approximately 11688 RNA-seq samples across 53 tissue sites and 2 cell lines, with adequate power to detect Expression Quantitative Trait Loci in 48 tissues.

Year founded:

2013

Last update:

2019-7-24

Version:

v8

Accessibility:

Manual:

Accessible

Real time :

Checking...

Country/Region:

United States

Classification & Tag

Data type:

DNA

RNA

Data object:

Animal

Database category:

Expression

Genotype phenotype and variation

Major species:

Homo sapiens

Keywords:

normal tissue

tissue site

eQTL

RNA-seq

Contact information

University/Institution:

Broad Institute

Address:

9000 Rockville Pike, Bethesda, Maryland 20892

City:

Bethesda

Province/State:

Maryland

Country/Region:

United States

Contact name (PI/Team):

GTEx consortium

Contact email (PI/Helpdesk):

volpis@mail.nih.gov

Publications

29334591

GTEx project maps wide range of normal human genetic variation: A unique catalog and follow-up effort associate variation with gene expression across dozens of body tissues. [PMID: 29334591]

Abstract

Am J Med Genet A. 2018:176(2)

| 4 Citations (from Europe

PMC, 2024-03-02)

29019975

Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. [PMID: 29019975]

eGTEx Project.

Abstract

Genetic variants have been associated with myriad molecular phenotypes that provide new insight into the range of mechanisms underlying genetic traits and diseases. Identifying any particular genetic variant's cascade of effects, from molecule to individual, requires assaying multiple layers of molecular complexity. We introduce the Enhancing GTEx (eGTEx) project that extends the GTEx project to combine gene expression with additional intermediate molecular measurements on the same tissues to provide a resource for studying how genetic differences cascade through molecular phenotypes to impact human health.

Nat Genet. 2017:49(12)

| 92 Citations (from Europe

PMC, 2024-03-02)

25954001

Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. [PMID: 25954001]

GTEx Consortium.

Abstract

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Science. 2015:348(6235)

| 2866 Citations (from Europe

PMC, 2024-03-02)

25954002

Human genomics. The human transcriptome across tissues and individuals. [PMID: 25954002]

Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R, Segrè AV, Djebali S, Niarchou A, GTEx Consortium, Wright FA, Lappalainen T, Calvo M, Getz G, Dermitzakis ET, Ardlie KG, Guigó R.

Abstract

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.

Science. 2015:348(6235)

| 695 Citations (from Europe

PMC, 2024-03-02)

26484571

A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. [PMID: 26484571]

Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET, Guan P, Korzeniewski GE, Lockhart NC, Rabiner CA, Rao AK, Robinson KL, Roche NV, Sawyer SJ, Segrè AV, Shive CE, Smith AM, Sobin LH, Undale AH, Valentino KM, Vaught J, Young TR, Moore HM, GTEx Consortium.

Abstract

The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.

Biopreserv Biobank. 2015:13(5)

| 423 Citations (from Europe

PMC, 2024-03-02)

23715323

The Genotype-Tissue Expression (GTEx) project. [PMID: 23715323]

GTEx Consortium.

Abstract

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

Nat Genet. 2013:45(6)

| 4144 Citations (from Europe

PMC, 2024-03-02)

Ranking

All databases:

13/5981

(99.799%)

Genotype phenotype and variation:

4/850

(99.647%)

Expression:

3/1137

(99.824%)

13

Total Rank

8,148

Citations

740.727

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Word cloud

Tags

DNA

RNA

Genotype phenotype and variation

Expression

normal tissue

tissue site

eQTL

RNA-seq

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Record metadata

Created on: 2019-07-30

Curated by:

Lina Ma [2019-07-31]

Lina Ma [2019-07-30]

GTEx

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GTEx数据库简介（3）：数据的获取 - 知乎

GTEx数据库简介（3）：数据的获取 - 知乎切换模式写文章登录/注册GTEx数据库简介（3）：数据的获取HuaMD医学大数据分享医学大数据知识----医学大数据及其综合分析（四）Hua+医学大数据 出品（转载请注明出处链接，翻版必究）（HuaPlusMD通过整合多种人类和动物数据库，建立了可靠的大数据库，为您提供疾病动物模型和临床大数据综合分析。链接：https://www.huaplusmd.com）前言：“大数据”概念早已出现，目前我们对（医学）大数据了解有多少呢？本平台将对医学大数据进行系统的介绍，并对大数据综合分析进行分享（每周更新）。分享的内容将主要涵盖大数据库（基因、蛋白数据库等）/生物银行介绍（UK Biobank, Finnish Biobanks, China Kadoorie Biobank, BioBank Japan, TCGA, GWAS catalog等），疾病动物模型数据库（如GeneNetwork, BXD），大数据库的综合使用（如Mendelian randomization），组学数据分析等。同时也会定期对一些医学大数据的使用进行实例分析。（分享的其他系列内容请见：https://www.huaplusmd.com/knowledge） 本期将对GTEx的数据下载和使用进行简介。GTEx的主要优势是：可以获取人类各种组织器官的基因表达。一般当我们做研究或药物开发时，往往希望药物/干预发生在特定的组织器官，降低副作用。例如，关于肥胖研究，我们往往会将研究的重点放在脂肪组织。而目前大多数数据库，并不能获取特异组织表达器官的基因表达，尤其是人类数据库，可谓非常难得。· 如何获得GTEx数据库的数据：ü 打开GTEx Portal: https://gtexportal.org/home/点击download >>Open Access Dataü 进入下载页面，如下图所示。在左侧（红框中），我们可以看到不同的分析版本，我们都可以用，但推荐使用V8 和V9。其中V9目前只提供snRNA-Seq data（单细胞核RNA测序技术）和Long Read RNASeq data（长读转录组，这个转录组主要是研究遗传变异在转录副本结构中的作用）。ü 这里重点说一下V8版的数据，如下图。V8数据主要有：1) RNAseq的BAM文件，全外显子Seq，全基因组Seq2) 基因型Calls3) OMNI SNP 阵列文件4) Affymetrix表达阵列, 等ü 注释文件（Annotations）：下载红框的文件就可以，主要是介绍样本的基本信息，包括样本ID，组织器官类型，RIN，测试使用的技术。ü RNAseq数据：也是我们最常使用的数据。包括Read counts, TPM, Exon-exon junction read counts, transcript read count/TPM, Exon read counts。数据也可以分组织进行下载（有read counts 和 TPM两种数据）。ü 另外，GTEx还做了很多的QTL分析（不了解QTL的同学，请翻书到前面 eQTL, cis-eQTL, trans-eQTL介绍和获取）：包括Single-Tissue cis-QTL Data，Single-Tissue trans-QTL Data，Multi-Tissue QTL Data，Single Tissue cis-RNA Editing QTL Data等等--------------end--------------—如果喜欢，快分享给你的朋友们吧—关注公众号，更多精彩内容等着你！原文链接：https://www.huaplusmd.com/knowledgeHua+医学大数据 出品 （医学大数据综合分析，HuaPlusMD坚持专业和认真）。如果您有医学大数据综合分析方面需求欢迎联系我们：https://www.huaplusmd.com/往期回顾：医学大数据及其综合分析（总纲）医学大数据及其综合分析（一）—— GEO数据库介绍 (1)医学大数据及其综合分析（一）—— GEO数据库介绍 (2)医学大数据及其综合分析（二）—— BXD小鼠数据库介绍 (1)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (2)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (3)医学大数据及其综合分析（二）—— BXD小鼠数据库/GeneNetwork介绍 (4)医学大数据及其综合分析（三）—— eQTLGen Consortium数据库简介（1）医学大数据及其综合分析（三）—— eQTLGen Consortium数据库简介（2）医学大数据及其综合分析（四）—— GTEx数据库简介（1）医学大数据及其综合分析（四）—— GTEx数据库简介（2）医学大数据及其综合分析（五）---- 国际原子能机构“双标水”数据库 (IAEA DLW)医学大数据及其综合分析（X）—— 实例分析1：中年发福：人体代谢率 不背此锅新冠肺炎（COVID-19）的致死率参考文献：[1] https://gtexportal.org/home/发布于 2022-12-21 10:09・IP 属地加拿大数据库数据获取​赞同 11​​3 条评论​分享​喜欢​收藏​申请

The Genotype-Tissue Expression (GTEx) project | Nature Genetics

The Genotype-Tissue Expression (GTEx) project | Nature Genetics

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The Genotype-Tissue Expression (GTEx) project

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Published: 29 May 2013

The Genotype-Tissue Expression (GTEx) project

John Lonsdale1, Jeffrey Thomas1, Mike Salvatore1, Rebecca Phillips1, Edmund Lo1, Saboor Shad1, Richard Hasz2, Gary Walters3, Fernando Garcia4, Nancy Young5, Barbara Foster6, Mike Moser6, Ellen Karasik6, Bryan Gillard6, Kimberley Ramsey6, Susan Sullivan7, Jason Bridge7, Harold Magazine8, John Syron8, Johnelle Fleming8, Laura Siminoff9, Heather Traino9, Maghboeba Mosavel9, Laura Barker9, Scott Jewell10, Dan Rohrer10, Dan Maxim10, Dana Filkins10, Philip Harbach10, Eddie Cortadillo10, Bree Berghuis10, Lisa Turner10, Eric Hudson10, Kristin Feenstra10, Leslie Sobin11, James Robb11, Phillip Branton12, Greg Korzeniewski11, Charles Shive11, David Tabor11, Liqun Qi11, Kevin Groch11, Sreenath Nampally11, Steve Buia11, Angela Zimmerman11, Anna Smith11, Robin Burges11, Karna Robinson11, Kim Valentino11, Deborah Bradbury11, Mark Cosentino11, Norma Diaz-Mayoral11, Mary Kennedy11, Theresa Engel11, Penelope Williams11, Kenyon Erickson12, Kristin Ardlie13, Wendy Winckler13, Gad Getz13,14, David DeLuca13, Daniel MacArthur13,14, Manolis Kellis13,15, Alexander Thomson13, Taylor Young13, Ellen Gelfand13, Molly Donovan13, Yan Meng13, George Grant13, Deborah Mash16, Yvonne Marcus16, Margaret Basile16, Jun Liu17, Jun Zhu18, Zhidong Tu18, Nancy J Cox19, Dan L Nicolae19, Eric R Gamazon19, Hae Kyung Im19, Anuar Konkashbaev19, Jonathan Pritchard19,20, Matthew Stevens19, Timothèe Flutre19, Xiaoquan Wen19, Emmanouil T Dermitzakis21, Tuuli Lappalainen21, Roderic Guigo22, Jean Monlong22, Michael Sammeth22, Daphne Koller23, Alexis Battle23, Sara Mostafavi23, Mark McCarthy24, Manual Rivas24, Julian Maller24, Ivan Rusyn25, Andrew Nobel25, Fred Wright25, Andrey Shabalin25, Mike Feolo26, Nataliya Sharopova26, Anne Sturcke26, Justin Paschal26, James M Anderson26, Elizabeth L Wilder26, Leslie K Derr27, Eric D Green28, Jeffery P Struewing28, Gary Temple28, Simona Volpi28, Joy T Boyer28, Elizabeth J Thomson28, Mark S Guyer28, Cathy Ng28, Assya Abdallah28, Deborah Colantuoni28, Thomas R Insel29, Susan E Koester29, A Roger Little29, Patrick K Bender29, Thomas Lehner29, Yin Yao29, Carolyn C Compton30, Jimmie B Vaught30, Sherilyn Sawyer30, Nicole C Lockhart30, Joanne Demchok30 & …Helen F Moore30 Show authors

Nature Genetics

volume 45, pages 580–585 (2013)Cite this article

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AbstractGenome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

MainIn the past decade, genome-wide association studies (GWAS) have documented a strong statistical association between common genetic variation at thousands of loci and more than 250 human traits1. Yet, the functional effects of most GWAS-implicated variants remain largely unexplained. The finding that nearly 90% of these sites occur outside of protein-coding sequences2 suggests that many associated variants may instead have a role in gene regulation. The careful examination of gene expression and its relationship to genetic variation has thus become a critical next step in the elucidation of the genetic basis of common disease. Cell context is a key determinant of gene regulation; but, to date, the challenge of collecting large numbers of diverse tissues in humans has largely precluded such studies outside of a few easily sampled cell types.Expression quantitative trait locus (eQTL) mapping offers a powerful approach to elucidate the genetic component underlying altered gene expression3. Studies primarily in blood, skin, liver, adipose and brain indicate that eQTLs are common in humans4,5,6. Genetic variation can also influence gene expression through alterations in splicing, noncoding RNA expression and RNA stability7,8,9. eQTLs regulating nearby or distant genes are commonly referred to as cis eQTLs and trans eQTLs, respectively3. Gene expression is differentially regulated across tissues, and many human transcripts are expressed in a limited set of cell types or during a limited developmental stage. Several studies have reported tissue-specific eQTLs10,11, and combining eQTL studies with network analyses across multiple tissues has helped to define complex networks of gene interaction12,13.Complementing eQTL data with information on other molecular phenotypes, for example, from epigenomic assays14, on the same tissues and linking to resources such as the Encyclopedia of DNA Elements (ENCODE)15 will provide a powerful means of dissecting gene-regulatory and higher-order networks across multiple tissues. Analyzing multiple tissues will be important because evaluation of the functional consequences of a disease-associated SNP is ideally performed in a disease-relevant cell context. However, for most tissue types, human biospecimens are very difficult to obtain from living donors (for example, brain, heart and pancreas), and most eQTL studies so far have been performed with RNA isolated from immortalized lymphoblasts or lymphocytes6 and a few additional readily sampled tissues.To fully enable this critical next step in the study of the genetic basis of common disease, it will be of enormous value to have a resource of blood samples from individuals who have been comprehensively genotyped (and eventually completely sequenced), with genotyping data linked to genome-wide gene expression patterns across a wide range of tissue types. Initially, this resource would enable the research community to perform a comprehensive search for eQTLs (both tissue-type specific and across tissue types) and establish their association with disease-associated variants from GWAS or sequencing studies. Eventually, as other molecular phenotypes are added, the relationship between genetic variation and gene expression could expand to include correlations with epigenetics and proteomics data as well as other molecular characteristics. Although such a catalog would have been unthinkable a few years ago, new genomic technologies are now making the problem approachable.This convergence of unmet scientific need and new technologies prompted a US National Institutes of Health (NIH) workshop held in June 2008 to discuss the advisability and feasibility of a large-scale public resource for human genetic variation and gene expression across tissues. On the basis of the output from this workshop and ongoing consultation, the NIH developed the concept of the GTEx project (Box 1). Many of the specifics of the pilot project described here were contributed by funded investigators and were influenced by early, experimental biospecimen collections.

Design of the GTEx project

The GTEx project of the NIH Common Fund aims to establish a resource database and associated tissue bank in which to study the relationship between genetic variation and gene expression and other molecular phenotypes in multiple reference tissues (Supplementary Fig. 1). The GTEx project began with a 2.5-year pilot phase to test the feasibility of establishing a rapid autopsy program that would yield high-quality nucleic acids and robust gene expression measurements. Having met milestones of donor enrollment, RNA quality and eQTL findings, the project is scaling up to include approximately 900 post-mortem donors by the end of 2015. The power to detect eQTLs is dependent on multiple factors that are difficult to quantify precisely, but power estimates over a range of effect sizes and allele frequencies are described (Fig. 1).Figure 1: Effect of sample size and MAF on power to detect eQTLs.(a) Power for cis-eQTL analysis in which we assume α = 0.05/200,000, reflecting Bonferroni correction for 200,000 hypotheses based on 20,000 genes and an average of 10 non-redundant SNPs in the region ±100 kb of each gene. (b) Power for trans-eQTL analysis in which we test 20,000 genes against 5 million SNPs in a total of 1 × 1011 tests with α = 5 × 10−13.Full size imageGTEx donors are identified through low-PMI (post-mortem-interval) autopsy or organ and tissue transplantation settings. To compare the quality of results for tissues derived from autopsy and surgery, a small subset of tissue types routinely discarded during surgical amputation, such as skin, fat and muscle, are also collected. In addition, peripheral blood samples are collected and used as both a source of DNA for whole-genome SNP and copy number variant (CNV) genotyping and to establish lymphoblastoid cell lines. Skin samples are collected from the same region of the lower leg, both for measurement of gene expression and to establish fibroblast cultures. Quantification of gene expression is performed primarily through massively parallel sequencing of RNA, but some pilot-phase tissues were analyzed both by sequencing and by gene expression array to enable a comparison of the results with different technologies. eQTLs are identified and will be made accessible to the scientific community through the National Center for Biotechnology (NCBI) GTEx database and a GTEx data portal. In addition, GTEx raw data will be made available through the database of Genotypes and Phenotypes (dbGaP) on a periodic basis.GTEx project structure during the pilot phase (Supplementary Fig. 2) included entities for biospecimen acquisition, processing, storage and verification; a study on ethical, legal and social issues (ELSI study); the Laboratory, Data Analysis and Coordinating Center (LDACC); the GTEx-eQTL browser; novel statistical methods development grants; and a brain bank. The scale-up is organized similarly to the pilot; the current structure of the project and information on funding opportunities are available from the NIH Common Fund website.

Biospecimen acquisition

These functions are designed and organized under the Cancer Human Biobank (caHUB) of the National Cancer Institute. caHUB has enrolled under contract several Biospecimen Source Sites (BSSs), a Comprehensive Biospecimen Resource (CBR), a Comprehensive Data Resource (CDR) and pathology and quality management teams to perform acquisition of biospecimens and associated data. Details on all standard operating procedures for donor enrollment and sample collection are available from the caHUB website.Donors of either sex from any ancestry group are eligible if they are aged 21–70 and if biospecimen collection can start within 24 h of death. There are few medical exclusionary criteria: human immunodeficiency virus (HIV) infection or high-risk behaviors, viral hepatitis, metastatic cancer, chemotherapy or radiation therapy for any condition within the past 2 years, whole-blood transfusion in the past 48 h or body mass index of >35 or <18.5. Each BSS collects, where feasible, aliquots from many predesignated tissue sites and organs (Supplementary Table 1), including the brain of deceased donors who were not on a ventilator for the 24 h before death. Immediately after excision, most aliquots are stabilized in a solution containing alcohols (ethanol and methanol), acetic acid and a soluble organic compound that fixes primarily by protein precipitation (PAXgene Tissue, Qiagen) and shipped to the CBR. Only blood samples and full-thickness skin biopsies are sent unfixed to the LDACC for cell line initiation. The majority of the brain and brainstem are also left unfixed and shipped overnight on wet ice to a brain bank. Further details of donor recruitment and sample collection, including standard operating procedures, are available through caHUB.

Pathology review and clinical annotation

At the CBR, an aliquot from each sampled tissue is paraffin embedded, sectioned and stained for histological analysis. A dedicated team of pathologists reviews slides from all tissue specimens to verify the organ source and to characterize both general pathological characteristics, such as autolysis, as well as organ-specific pathological states and inflammation. Of course, not all organs will be entirely normal, but donor eligibility is broad and is not restricted to specific diseases or conditions, and it is expected that many organs will be free of major disease processes. An aliquot of each tissue, fixed and stabilized in PAXgene Tissue solution but without paraffin embedding, is sent to the LDACC for molecular analysis. Policies and systems for accessing stored tissue samples are currently being developed. The CBR's histological sections are viewed as digitally scanned images, allowing precise annotations to be made to indicate where downstream studies, for example, tissue microarray and laser-capture microextraction, on selected portions of a specimen can focus (for example, lymphoid nodules in the ileal mucosa or squamous epithelium in the esophageal mucosa).The clinical data collected for each GTEx donor belong to one of two categories: donor-level data or sample-level data. Donor-level data encompass all clinical measures of the donor, which include basic demographics, medication use, medical history, laboratory test results and the circumstances surrounding the donor's death. These data are collected from the donor (surgical biospecimens) or next of kin (post-mortem biospecimens) and verified against the donor's medical record, when readily available. Summary frequency distributions for clinical variables are available in dbGaP. Sample-level data are attributes belonging to each sample collected and include the tissue type, ischemic time, comments from the prosector and pathology reviewer, and process metadata such as batch ID and the amount of time spent in PAXgene Tissue fixative. Both donor- and sample-level data are examined for quality and completeness before being released.

Brain bank

Aliquots from single regions of the cortex and cerebellum are sampled and preserved in PAXgene Tissue at the BSS, and the remaining whole brain, with attached brain stem and cervical spinal cord, when possible, is shipped on wet ice to an NIH-funded brain bank. After sectioning of the brain at the brain bank, frozen samples from additional anatomical regions of the brain are analyzed at the LDACC, and the remaining brain is banked for future uses.

LDACC

The LDACC performs nucleic acid extractions and quality assessment, DNA genotyping and RNA expression analysis. The LDACC integrates results from the molecular analysis with phenotype data, performs eQTL analysis, deposits data into dbGaP and provides a portal for open-access data, standard operating procedures for sample processing and data generation, and results.DNA is genotyped using the Illumina HumanOmni5M-Quad BeadChip to collect whole-genome SNP and CNV information from each donor's blood sample (or an alternate tissue, if blood is unavailable). The Illumina assay contains over 4 million probes, with robust coverage of both SNPs and CNVs. DNA is also characterized using the Illumina Infinium HumanExome BeadChip to obtain high-quality SNP calls in coding regions.A portion of each tissue is processed for RNA and DNA extraction, quantification and quality assessment. Extraction protocols that preserve both mRNA and microRNA are being used and are available from the data portal. For measurement of gene expression during the pilot phase, the LDACC analyzed approximately 1,000 samples using both microarrays (Affymetrix Human Gene 1.1 ST Array) and next-generation RNA sequencing (Illumina HiSeq 2000) to establish the comparability of these methods using post-mortem tissue. RNA sequencing (RNA-seq) uses a 76-base, paired-end Illumina TruSeq RNA protocol, averaging ∼50 million aligned reads per sample. This read depth was selected to maximize sequencing value with the budget available and should make it possible to accurately measure moderately expressed transcripts, as well as some with low-level expression, but will have limited ability to accurately quantify rare transcripts and splice isoforms. It should provide gene expression measurements that have equivalent or better accuracy than those obtained with expression arrays and should include a higher dynamic range (with coefficient of variation < 0.1 for at least 12,000 genes; Supplementary Fig. 3). RNA-seq allows one to evaluate allele-specific expression in heterozygous individuals, improving the power to identify cis regulatory variants. With the target depth of 50 million aligned reads, we expect to have sufficient power to detect allele-specific expression in the top tertile of expressed genes (Supplementary Fig. 4 and Supplementary Note). As the cost of RNA-seq drops, greater read depth will be possible, but, with current resources, the strategy is to maximize the number of samples analyzed.The fresh-blood and full-thickness skin samples are used to establish Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines and primary fibroblast cell lines. Because many existing human eQTL studies have used EBV-immortalized cell lines, having these lines in addition to all the other peripheral tissues will allow researchers to evaluate the limitations of using only a lymphoblastoid cell line.

GTEx-eQTL browser

eQTLs are available and can be queried in browsers hosted both at the LDACC GTEx portal and at NCBI, who will verify the eQTL results provided by the project and both display them and make them available to other genome browsers and the scientific community.

Statistical analysis development

To promote the analysis of eQTL results across a wide range of human tissues, the NIH funded five centers to develop improved methods for statistical analysis. Investigators funded through this request for applications (RFA) form an analysis consortium that will provide innovative approaches for the analyses of GTEx data and other similar data sets. Investigators also collaborate with the LDACC to perform data quality assessment and quality control before release of the data into dbGaP. The initial GTEx Consortium publications, anticipated in 2013, will include genome-wide analysis of cis and trans eQTLs, allele-specific expression and splicing quantitative trait loci and a comparison of gene expression results obtained by array and RNA-seq.

Sample access and molecular analyses

The NIH is interested in making maximal use of this unique biospecimen resource, rich with clinical and genomic information. An access system, including mechanisms for requesting samples, is under development. Except for the fibroblast and lymphoblastoid cell lines, biospecimens are of limited quantity and are non-renewable. Potential uses that are comprehensive (for example, genomic versus single gene or small gene network and proteomic versus single protein or small protein network) and complementary to existing gene expression and variation data are preferred. Scientific questions that are equally well addressed using other sample sets will probably not be suitable, whereas those that take full advantage of the unique aspects of GTEx data, such as the multiple tissues from each donor and the gene expression information, are particularly sought. All data resulting from the analysis of GTEx samples must be made widely available to the scientific community. In addition to scientific review, all proposals to use GTEx samples would also go through a Biospecimen Access Committee (currently being formed).

Power analysis

To set expectations and guide the design of the full GTEx project, we built a framework to evaluate the statistical power to detect eQTLs. Statistical power depends on various parameters, some known more accurately than others. These parameters include the number of donors, the eQTL effect size and the presence of noise, as well as the significance threshold selected, which is chosen on the basis of the number of hypotheses tested. Assuming we are testing the cis eQTL effects of the 10 non-redundant SNPs (on average) in the vicinity (±100 kb of the start site) of each of 20,000 genes, the overall number of hypotheses is 200,000. Therefore, using Bonferroni correction, we set the significance threshold a to 0.05/200,000. For trans-eQTL analysis, a conservative estimate of a is ∼5 × 10−13 (20,000 transcripts tested against 5 million loci). We model the expression data as having a log normal distribution with a log standard deviation of 0.13 within each genotype class (AA, AB, BB). This level of noise is based on estimates from initial GTEx data. The effect size depends both on the minor allele frequency of the SNP (MAF) and the actual log expression change between genotype classes (D). Figure 1a shows the statistical power of cis-eQTL analysis, and Figure 1b shows trans-eQTL analysis, with each analysis using an ANOVA statistical test as a function of the number of subjects and MAF and assuming D = 0.13 (equivalent to detecting a log expression change similar to the standard deviation within a single genotype class). A final GTEx resource of 900 or more donors would realistically yield ∼750 samples of any given tissue, as not all organs are available for collection from each donor. At an effective sample size of 750, we would have 80% power to detect cis eQTLs with MAF as low as 2% and trans eQTLs with MAF as low as 4%. Statistical power may be higher using methods that leverage the fact that multiple tissues are collected and analyzed for each donor. Because the underlying parameters were merely rough estimates, we repeated power analysis with different values (10–20 SNPs and 20,000–100,000 transcripts) and showed that 80% power is achieved for MAFs between 3 and 4% for cis eQTLs. For trans eQTLs, this range in transcript numbers gives sufficient power with MAFs between 4 and 5% (Supplementary Fig. 5).

Data access and publication policy

GTEx is designated by NIH as a community resource and, as such, aims to share as much of the data (some of which will be unique and identifiable) as rapidly as possible, according to NIH guidelines. It is recognized that quantifying the risk of identifying a donor on the basis of genetic and other information lies on a continuum and is a complex issue dependent on many factors, such as the availability other sources of data and the evolution of analytical methods16,17. Sharing of any information unique to an individual carries a small but difficult-to-define risk of allowing identification of the donor, but this risk must be balanced with the benefits of data sharing to the advancement of science.Some data from the GTEx project is openly available, meaning that it can be accessed directly through the Internet. However, to reduce risks of sharing potentially identifying data, some data elements are available to the scientific community only through a controlled-access system, dbGaP. Standard operating procedures, details of data collection instruments, histopathological interpretations, molecular data that do not provide direct genetic variation information (for example, data from expression arrays, summary sequence-based gene expression estimates stripped of variant information and eQTL results), laboratory processing variables (for example, cDNA library preparation methods) and a very limited set of medical and sociodemographic variables (for example, sex and age at death in intervals) will be openly available. The LDACC will host an open-access data portal, and specimen acquisition standard operating procedures and information on associated data collection instruments will be available through caHUB. Medical and other epidemiological information, molecular results that contain direct genetic variation information (for example, SNP genotyping files and RNA-seq reads) and summary results that allow accurate inference of allele frequencies18 will be available only through controlled access. Direct HIPAA (Health Insurance Portability and Accountability Act) identifiers, such as dates that include the month and day, will not be available through either open or controlled access.Implementation of these data release policies and processes is a topic of ongoing discussion and may need to be modified as risks of identifiability are better quantified for various data types and as the size of the study increases. After initial processing of raw data (such as sequence reads and genotyping files), basic data quality checks are completed by the LDACC and statistical methods investigators, and data are then released immediately through dbGaP. The first dbGaP data release, consisting of data from 62 individuals, occurred in June 2012. For the pilot phase of the project, which concluded in January 2013, the data set comprised genotype data from 190 individuals from whom 1,814 total tissues (from 47 separate tissue sites) were profiled by RNA-seq to a median depth of 80 million aligned reads. These data are in the process of being released to dbGaP, and we anticipate releasing data two to four times per year until the project is completed. We expect total enrollment to increase to over 400 by 2013, to over 700 in 2014 and to approximately 900 by the end of 2015.The GTEx project falls under the Ft. Lauderdale meeting principles of rapid, prepublication data release. These principles involve publication of a manuscript near the outset to describe the scope and vision of the project and plans to make data available. The continued success of rapid prepublication data release relies on the scientific community to respect the data producer's interest to publish a full analysis of the data first. Although others are free to analyze GTEx data immediately upon release, the GTEx Consortium envisions publication of both a comprehensive description of the sample acquisition and processing system and a series of genome-wide analyses of genetic variation and gene expression, as described for statistical analysis and the development of methods.

Ethical, legal and social issues

The GTEx project involves potentially sensitive recruitment, institutional review board (IRB) and consent issues, particularly for deceased donors and their families. The collection of biospecimens from deceased individuals is not legally classified as human subjects research under 45 CFR 46; nonetheless, the depth of the genetic information obtained from the specimens of deceased donors has direct implications for the families of the donors. In recognition of this understanding, sites were required to obtain written or recorded verbal authorization from next of kin for the participation of deceased donors in GTEx, typically through an addendum or modification to an existing authorization form for donation of tissues and organs for research. This authorization included statements common in consent forms, such as the intention to perform genetic analyses, establish cell lines and share data with the scientific community. Work under way is more closely identifying familial concerns and may result in modifications to authorization procedures. Living surgery donors participate only after full, written informed consent is obtained.In addition, an ELSI study of the consent and authorization process is being carried out at one BSS to assess both the effectiveness of the process in informing participants of the risks and benefits of the study and its potential psychosocial effects on donors and/or their families. The ELSI study is fully integrated with biospecimen collection efforts and will be expanded during the scale-up of the GTEx program.Box 1: Goals of the GTEx project

To create a data resource to enable the systematic study of genetic variation and the regulation of gene expression in multiple reference human tissues

To provide the scientific community with a biospecimen resource including tissues, nucleic acids and cell lines upon which to determine other molecular phenotypes

To support and disseminate the results of a study of the ethical, legal and social issues related to donor recruitment and consent

To support the development of novel statistical methods for the analysis of human eQTLs, alone and in the context of other molecular phenotypes

To make data available to the research community as rapidly as possible

To support the dissemination of knowledge, standards and protocols related to biospecimen collection and analysis methods developed during the project

ConclusionsA large-scale GTEx resource will be a powerful tool in unraveling the complex patterns of genetic variation and gene regulation across diverse human tissue types. The GTEx project will aid in the interpretation of GWAS findings for translational research by providing data and resources on eQTLs in a wide range of tissues of relevance to many diseases. But the value of a large GTEx resource, especially one that includes other molecular phenotypes, goes well beyond GWAS follow-up, by providing a deeper understanding of the functional elements of the genome and their underlying biological mechanisms.URLs. Catalog of published GWAS, http://www.genome.gov/gwastudies; GTEx LDACC data portal, http://www.broadinstitute.org/gtex/; caHUB, http://cahub.cancer.gov/; caHUB standard operating procedures, http://biospecimens.cancer.gov/resources/sops/default.asp; GTEx project on dbGaP, http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424; US GTEx project on NIH Common Fund, http://commonfund.nih.gov/GTEx/; GTEx on National Human Genome Research Institute, http://genome.gov/gtex/; NCBI GTEx eQTL Browser, http://www.ncbi.nlm.nih.gov/gtex/test/GTEX2/gtex.cgi/; Request for information for the GTEx project, http://grants.nih.gov/grants/guide/notice-files/NOT-RM-12-028.html; seeQTL, http://www.bios.unc.edu/research/genomic_software/seeQTL/; SCAN, http://www.scandb.org/newinterface/about.html; US NIH community resource policy for GWAS, http://gwas.nih.gov/03policy2.html; Sharing Data from Large-Scale Biological Research Projects: a System of Tripartite Responsibility (Wellcome Trust), http://www.wellcome.ac.uk/About-us/Publications/Reports/Biomedical-science/WTD003208.htm; US NIH GTEx working group members, http://commonfund.nih.gov/GTEx/members.aspx.

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Download referencesAcknowledgementsThe authors would like to acknowledge and thank the donors and their families for making organ and tissue donations, both for transplantation and for the GTEx research study. The authors acknowledge the following funding sources: contracts X10S170, X10S171 and X10172, SAIC-Frederick, Inc., National Cancer Institute and NIH Common Fund, US NIH to the National Disease Research Interchange, the Roswell Park Cancer Institute and Science Care, Inc.; contract HHSN268201000029C, National Heart, Lung, and Blood Institute and NIH Common Fund, US NIH to the Broad Institute of Harvard and MIT (W.W., contact principal investigator); R01 DA006227-17, National Institute of Drug Abuse, National Institute of Mental Health and National Institute of Neurological Disorders and Stroke, US NIH to the University of Miami School of Medicine (D. Mash, principal investigator); contract 10ST1035, SAIC-Frederick, Inc., National Cancer Institute and NIH Common Fund, US NIH to the Van Andel Institute; prime contract HHSN261200800001E, National Cancer Institute and NIH Common Fund, US NIH to SAIC-Frederick, Inc.; R01 MH090941, National Institute of Mental Health and NIH Common Fund, US NIH to the University of Geneva (E.T.D., contact principal investigator); R01 MH090951, National Institute of Mental Health and NIH Common Fund, US NIH to the University of Chicago (J. Pritchard, principal investigator); R01 MH090937, National Institute of Mental Health, National Human Genome Research Institute, National Heart, Lung, and Blood Institute and NIH Common Fund, US NIH to the University of Chicago (N.J.C., contact principal investigator); R01 MH090936, National Institute of Mental Health and NIH Common Fund, US NIH to the University of North Carolina at Chapel Hill (I.R., contact principal investigator); and R01 MH090948, National Institute of Mental Health, National Human Genome Research Institute and NIH Common Fund, US NIH to Harvard University (J. Liu, contact principal investigator). This research was supported in part by the Intramural Research Program of the National Library of Medicine at the US NIH. The views presented in this article do not necessarily reflect those of the US NIH.Author informationAuthors and AffiliationsNational Disease Research Interchange, Philadelphia, Pennsylvania, USAJohn Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo & Saboor ShadGift of Life Donor Program, Philadelphia, Pennsylvania, USARichard HaszLifeNet Health, Virginia Beach, Virginia, USAGary WaltersDrexel University College of Medicine, Philadelphia, Pennsylvania, USAFernando GarciaAlbert Einstein Medical Center, Philadelphia, Pennsylvania, USANancy YoungRoswell Park Cancer Institute, Buffalo, New York, USABarbara Foster, Mike Moser, Ellen Karasik, Bryan Gillard & Kimberley RamseyUpstate New York Transplant Service, Buffalo, New York, USASusan Sullivan & Jason BridgeScience Care, Inc., Phoenix, Arizona, USAHarold Magazine, John Syron & Johnelle FlemingVirginia Commonwealth University, Richmond, Virginia, USALaura Siminoff, Heather Traino, Maghboeba Mosavel & Laura BarkerVan Andel Institute, Grand Rapids, Michigan, USAScott Jewell, Dan Rohrer, Dan Maxim, Dana Filkins, Philip Harbach, Eddie Cortadillo, Bree Berghuis, Lisa Turner, Eric Hudson & Kristin FeenstraSAIC-Frederick, Inc., Frederick, Maryland, USALeslie Sobin, James Robb, Greg Korzeniewski, Charles Shive, David Tabor, Liqun Qi, Kevin Groch, Sreenath Nampally, Steve Buia, Angela Zimmerman, Anna Smith, Robin Burges, Karna Robinson, Kim Valentino, Deborah Bradbury, Mark Cosentino, Norma Diaz-Mayoral, Mary Kennedy, Theresa Engel & Penelope WilliamsSapient Government Services, Arlington, Virginia, USAPhillip Branton & Kenyon EricksonThe Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USAKristin Ardlie, Wendy Winckler, Gad Getz, David DeLuca, Daniel MacArthur, Manolis Kellis, Alexander Thomson, Taylor Young, Ellen Gelfand, Molly Donovan, Yan Meng & George GrantMassachusetts General Hospital Cancer Center, Boston, Massachusetts, USAGad Getz & Daniel MacArthurMassachusetts Institute of Technology, Cambridge, Massachusetts, USAManolis KellisUniversity of Miami School of Medicine, Miami, Florida, USADeborah Mash, Yvonne Marcus & Margaret BasileHarvard University, Boston, Massachusetts, USAJun LiuMount Sinai School of Medicine, New York, New York, USAJun Zhu & Zhidong TuUniversity of Chicago, Chicago, Illinois, USANancy J Cox, Dan L Nicolae, Eric R Gamazon, Hae Kyung Im, Anuar Konkashbaev, Jonathan Pritchard, Matthew Stevens, Timothèe Flutre & Xiaoquan WenHoward Hughes Medical Institute, Chicago, Illinois, USAJonathan PritchardUniversity of Geneva, Geneva, SwitzerlandEmmanouil T Dermitzakis & Tuuli LappalainenCenter for Genomic Regulation, Barcelona, SpainRoderic Guigo, Jean Monlong & Michael SammethStanford University, Palo Alto, California, USADaphne Koller, Alexis Battle & Sara MostafaviOxford University, Oxford, UKMark McCarthy, Manual Rivas & Julian MallerUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USAIvan Rusyn, Andrew Nobel, Fred Wright & Andrey ShabalinNational Center for Biotechnology Information, National Library of Medicine, US National Institutes of Health, Bethesda, Maryland, USAMike Feolo, Nataliya Sharopova, Anne Sturcke, Justin Paschal, James M Anderson & Elizabeth L WilderDivision of Program Coordination, Planning and Strategic Initiatives, Office of Strategic Coordination (Common Fund), Office of the Director, US National Institutes of Health, Bethesda, Maryland, USALeslie K DerrNational Human Genome Research Institute, Bethesda, Maryland, USAEric D Green, Jeffery P Struewing, Gary Temple, Simona Volpi, Joy T Boyer, Elizabeth J Thomson, Mark S Guyer, Cathy Ng, Assya Abdallah & Deborah ColantuoniNational Institute of Mental Health, Bethesda, Maryland, USAThomas R Insel, Susan E Koester, A Roger Little, Patrick K Bender, Thomas Lehner & Yin YaoUS National Cancer Institute, Bethesda, Maryland, USACarolyn C Compton, Jimmie B Vaught, Sherilyn Sawyer, Nicole C Lockhart, Joanne Demchok & Helen F MooreAuthorsJohn LonsdaleView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsBiospecimen and data collection, processing, quality control, storage and pathological review. caHUB-BSS: J. Lonsdale, J.T., M. Salvatore, R.P., E.L., S. Shad, R.H., G.W., F.G., N.Y., B.F., M. Moser, E.K., B.G., K. Ramsey, S. Sullivan, J.B., H.M., J.S. and J.F. caHUB-ELSI study: L. Siminoff, H.T., M. Mosavel and L.B. caHUB-CBR: S.J., D.R., D. Maxim, D.F., P.H., E.C., B.B., L.T., E.H. and K.F. caHUB-PRC: L. Sobin, J.R. and P.B. caHUB-CDR: G.K., C.S., D.T., L.Q., K.G. and S.N. caHUB–Operations Management: S.B., A.Z., A. Smith, R.B., K. Robinson, K.V., D.B., M.C., N.D.-M., M. Kennedy, T.E., P.W. and K.E. Laboratory analysis, data analysis and study coordination. K.A., W.W., G. Getz, D.D., D. MacArthur, M. Kellis, A.T., T.Y., E. Gelfand, M.D., Y. Meng and G. Grant. Brain bank operations. D. Mash, Y. Marcus and M.B. Statistical methods development and data analysis. J. Liu, J.Z., Z.T., E.T.D., T. Lappalainen, R.G., J. Monlong, M. Sammeth, D.K., A.B., S.M., M. McCarthy, M.R., J. Maller, I.R., A.N., F.W., A. Shabalin, N.J.C., D.L.N., E.R.G., H.K.I., A.K., J. Pritchard, M. Stevens, T.F. and X.W. Database. M.F., N.S., A. Sturcke and J. Paschal. Program management. J.M.A., E.L.W., L.K.D., E.D.G., J.P.S., G.T., S.V., J.T.B., E.J.T., M.S.G., C.N., A.A., D.C., T.R.I., S.E.K., A.R.L., P.K.B., T. Lehner, Y.Y., C.C.C., J.B.V., S. Sawyer, N.C.L., J.D. and H.F.M.Corresponding authorsCorrespondence to

Wendy Winckler, Gad Getz or Jeffery P Struewing.Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary informationSupplementary Text and FiguresSupplementary Figures 1–5, Supplementary Table 1 and Supplementary Note (PDF 777 kb)Rights and permissions

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Reprints and permissionsAbout this articleCite this articleLonsdale, J., Thomas, J., Salvatore, M. et al. The Genotype-Tissue Expression (GTEx) project.

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The Common Fund's Genotype-Tissue Expression (GTEx) Program established a data resource and tissue bank to study the relationship between genetic variants (inherited changes in DNA sequence) and gene expression (how genes are turned on and off) in multiple human tissues and across individuals. GTEx also increased our understanding of how gene expression varies between male and female. 

The GTEx program has transitioned from Common Fund support. Common Fund programs are strategic investments that achieve a set of high-impact goals within a 5-10 year timeframe. At the conclusion of each program, deliverables will transition to other sources of support or use within the scientific community.

The GTEx program supported by the Common Fund from 2010 to 2019. Currently, GTEx data are widely used as a reference dataset to design new methods and tools, such as a statistical method called PrediXcan. This novel method is used to predict the expression of a gene using DNA sequence data. PrediXcan also predicts visible traits of diseases. GTEx researchers used this method to identify specific genes associated with five diseases: bipolar disorder, coronary artery disease, Crohn's disease, rheumatoid arthritis and type 1 diabetes. The GTEx’s final dataset (V8) contains DNA data from 838 postmortem donors and 17,382 RNA-seq across 54 tissue sites and two cell lines. GTEx data is accessible through the National Center for Biotechnology Information’s database of Genotypes and Phenotypes (dbGaP), the National Human Genome Research Institute's (NHGRI) Genomic Analysis and Visualization and Informatics Labspace (AnVIL) and GTEx Portal.  GTEx resources are valuable tools for exploring the impact of genetic variation on complex traits and diseases.

Program Major Accomplishments

Highlights of the Genotype-Tissue Expression (GTEx) Program major accomplishments are:

Established a comprehensive catalog of genetics variants that effect gene expression across multiple tissue for the research community to evaluate tissue-specific gene expression and regulation in many different tissues. Genetic variants that influence how genes behave are called expression quantitative trait loci (eQTLs). Researchers are using GTEx data to enhance the functional interpretation of genome-wide association study (GWAS) findings from and identification of disease-relevant genes.

Created an online data resource (GTEx Portal) for storing, cataloging, searching, and sharing aggregated level data. Researchers used data from the GTEx Portal to publish over 7,000 papers.

GTEx data was integrated into genomics browsers including the UCSC Genome Browser and Ensembl to visualize gene and variant information. 

Developed a biobank of tissue biospecimens (e.g. lung, brain, pancreas, skin, etc) as well as RNA, DNA, blood samples and cell lines from ~960 donors. The GTEx biobank also features an image library of the tissue samples for researchers to browse the complete collection. These biospecimens are stored at the Broad Institute of Harvard and MIT. 

Please note that since the GTEx program is no longer supported by the Common Fund, the program website is being maintained as an archive and will not be updated on a regular basis. 

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Watch a video on the GTEx project for more details. 

The GTEx (Genotype-Tissue Expression) Project identified genetic variants that influence how genes are turned on and off in human tissues and organs. Genetic variants that influence how genes behave are called expression quantitative trait loci (eQTLs). These eQTLs regulate the behavior of genes like a light-switch turns on a light in a room. A GTEx pilot study found that the number of eQTLs differ in multiple tissues and individuals. 

GTEx collected multiple human tissues (i.e. brain, heart, lung, breast, skin and whole blood etc.) from ~960 donors and over 30,000 samples. These tissues and samples are stored through the National Cancer Institute's Cancer Human Biobank initiative on behalf of GTEx. The GTEx database is available to researchers through the GTEx Portal. GTEx is helping researchers understand the inherited susceptibility to common diseases such as cancer, heart disease, Parkinson’s and diabetes. 

GTEx also included a study to understand the ethical, legal and social issues (ELSI) related to donor recruitment and consent to tissue donation for biobanking purposes. In 2017, the GTEx ELSI researchers published a paper in Genetic Testing and Molecular Biomarkers. The findings indicated that a clear discussion about risks and benefits associated with participation in biobanking research is needed during the consent process.

Program Initiatives

The GTEx Program supported the following initiatives:

Online data resource (GTEx Portal) for storing, cataloging, searching, and sharing aggregated level data

Novel Statistical Methods for Human Gene Expression Quantitative Trait Loci (eQTL) Analysis

Laboratory, Data Analysis, and Coordinating Center (LDACC) for acquiring and analyzing DNA and RNA from multiple human tissues

Enhanced GTEx projects: including additional dimensions beyond gene expression to the GTEx data

Annoucements

Expanding Our View of The Genomic Landscape Using the Genotype-Tissue Expression (GTEx) Data Set

GTEx Data Set Used to Study Biological Changes After Death

GTEx Creates a Reference Data Set to Study Genetic Changes and Gene Expression

GTEx Data Uncovering How Genetic Alterations Contribute to Schizophrenia

GTEx Dataset Helps Determine How Gene Duplications Lead to Genes with New Biological Functions

The GTEx version 8 is now available 

The GTEx Portal has been updated to data release V8 (dbGaP accession phs000424.v8.p2)! This release includes genotype data from approximately 948 post-mortem donors and approximately 17,382 RNA-seq samples across 54 tissue sites and 2 cell lines, with adequate power to detect Expression Quantitative Trait Loci in 48 tissues. Full gene expression datasets are available for download through the GTEx Portal while genotypes and RNA-seq bam files are available via dbGaP.

Genotype-Tissue Expression Project (GTEx) Biospecimens Access Policy

The policy is a mechanism  to allow researchers access to tissues in the GTEx biobank. The policy and related forms can be found on the GTEx Portal. Go directly to GTEx Sample Request Forms.

This page last reviewed on

January 8, 2024

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GTEx:基因型和基因表达量关联数据库-腾讯云开发者社区-腾讯云

:基因型和基因表达量关联数据库-腾讯云开发者社区-腾讯云生信修炼手册GTEx:基因型和基因表达量关联数据库关注作者腾讯云开发者社区文档建议反馈控制台首页学习活动专区工具TVP最新优惠活动文章/答案/技术大牛搜索搜索关闭发布登录/注册首页学习活动专区工具TVP最新优惠活动返回腾讯云官网生信修炼手册首页学习活动专区工具TVP最新优惠活动返回腾讯云官网社区首页 >专栏 >GTEx:基因型和基因表达量关联数据库GTEx:基因型和基因表达量关联数据库生信修炼手册关注发布于 2019-12-19 10:50:507.7K0发布于 2019-12-19 10:50:50举报文章被收录于专栏：生信修炼手册生信修炼手册GTEx全称如下Genotype-Tissue Expression该项目对来自人体多个组合和器官的样本，同时进行了转录组测序和基因分型分析，构建了一个组织特异性的基因表达和调控的数据库。网址如下https://gtexportal.org/home/包含的组织类型和样本个数如下图所示对于所有的样本，主要进行了以下三种分析1. RNA seq通过illumina Truseq试剂盒构建polyA+文库，采用Hiseq 2000/2500进行测序，对于下机数据，采用STAR进行比对，参照选择的是gencode V19版本的gtf文件，进行了以下3个level的定量gene-level,采用RNAseQC软件，对基因的raw count和TPM两种方式进行定量exon-level, 对exon的raw count进行定量transcript-level，采用RSEM进行转录本水平的定量2. genotype通过WGS对样本进行分型, 采用的是GATK germline variants calling的流程，步骤如下bwa-mem alignmentpicard markduplicateBQSRindel realignhaplotypeCaller3. eQTL通过FastQTL软件进行cis-eQTL分析，将基因型和基因表达量进行关联。通过官网可以查看基因表达量和eQTL分析的结果，以TP53为例，每个基因给出了以下3个层级的表达量Isoform ExpressionExon ExpressionJunction Expression

分别对应转录本，外显子，剪切序列的表达量，对于不同组织中的表达量，以热图的形式进行展示，示意如下对于基因结构，也进行了可视化，示意如下eQTL的结果示意如下提供了以下两种可视化方式，第一种是在单个组织内的小提琴图，eQTL violin plot, 示意如下第二种用于多个组织间的比较，Multi-tissue eQTL plot, 示意如下所有的分析结果可以通过官网进行下载，GTEx数据库不仅仅是一个正常组织的基因表达量数据库，其eQTL分析的策略更值得我们借鉴。本文参与 腾讯云自媒体分享计划，分享自微信公众号。原始发表：2019-08-13，如有侵权请联系 cloudcommunity@tencent.com 删除express数据库sql本文分享自 生信修炼手册 微信公众号，前往查看如有侵权，请联系 cloudcommunity@tencent.com 删除。本文参与 腾讯云自媒体分享计划  ，欢迎热爱写作的你一起参与！express数据库sql评论登录后参与评论0 条评论热度最新登录 后参与评论推荐阅读LV.关注文章0获赞0目录1. RNA seq2. genotype3. eQTL相关产品与服务数据库云数据库为企业提供了完善的关系型数据库、非关系型数据库、分析型数据库和数据库生态工具。您可以通过产品选择和组合搭建，轻松实现高可靠、高可用性、高性能等数据库需求。云数据库服务也可大幅减少您的运维工作量，更专注于业务发展，让企业一站式享受数据上云及分布式架构的技术红利！产品介绍2024新春采购节领券社区专栏文章阅读清单互动问答技术沙龙技术视频团队主页腾讯云TI平台活动自媒体分享计划邀请作者入驻自荐上首页技术竞赛资源技术周刊社区标签开发者手册开发者实验室关于社区规范免责声明联系我们友情链接腾讯云开发者扫码关注腾讯云开发者领取腾讯云代金券热门产品域名注册云服务器区块链服务消息队列网络加速云数据库域名解析云存储视频直播热门推荐人脸识别腾讯会议企业云CDN加速视频通话图像分析MySQL 数据库SSL 证书语音识别更多推荐数据安全负载均衡短信文字识别云点播商标注册小程序开发网站监控数据迁移Copyright © 2013 - 2024 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有 深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 深公网安备号 44030502008569腾讯云计算（北京）有限责任公司 京ICP证150476号 |  京ICP备11018762号 | 京公网安备号11010802020287问题归档专栏文章快讯文章归档关键词归档开发者手册归档开发者手册 Section 归档Copyright © 2013 - 2024 Tencent Cloud.All Rights Reserved. 腾讯云 版权所有登录 后参与评论00

Reaching completion for GTEx | Nature Reviews Genetics

Reaching completion for GTEx | Nature Reviews Genetics

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Published: 15 October 2020

GENE EXPRESSIONReaching completion for GTEx

Darren J. Burgess1 

Nature Reviews Genetics

volume 21, page 717 (2020)Cite this article

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Disease geneticsGene expression analysisGene regulationGenetic variationGenome-wide analysis of gene expressionTranscriptomics

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The Genotype–Tissue Expression (GTEx) consortium has worked for more than a decade to characterize the genetic determinants of human gene expression across tissues. In five main papers in Science, as well as additional papers in other journals, the consortium recently reported results from the third and final phase of the project. They provide unprecedented detail of gene expression regulation, including expression-associated genetic variants for the majority of genes in the genome, mechanistic insights into complex traits, and influences of sex, ancestry and cell-type composition.The main summary paper by the GTEx consortium provides an overview of the latest data and findings. The data set of 838 donors and 15,201 samples across 49 tissues is approximately twice the size of the phase two results presented in 2017. Overall, 4,278,636 genetic variants were associated with gene expression. At least one associated cis expression quantitative trait locus (cis-eQTL) was identified for 94.7% of all protein-coding genes. Analysing the rate of discovery of new eQTLs with increasing sample size indicated that the latest sample size is sufficient to have largely saturated the discovery of those eQTLs with greater than twofold effect sizes on gene expression.

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ReferencesOriginal articlesThe GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020)Article 

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Download referencesAuthor informationAuthors and AffiliationsNature Reviews Genetics http://www.nature.com/nrg/Darren J. BurgessAuthorsDarren J. BurgessView author publicationsYou can also search for this author in

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Darren J. Burgess.Rights and permissionsReprints and permissionsAbout this articleCite this articleBurgess, D.J. Reaching completion for GTEx.

Nat Rev Genet 21, 717 (2020). https://doi.org/10.1038/s41576-020-00296-7Download citationPublished: 15 October 2020Issue Date: December 2020DOI: https://doi.org/10.1038/s41576-020-00296-7Share 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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