A resource for integrated genomic analysis of the human liver.
A resource for integrated genomic analysis of the human liver.
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DOI：
10.1038/s41598-022-18506-z
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发表时间：
2022-09-07
影响因子：
4.6
通讯作者：
Innocenti, Federico
中科院分区：
文献类型：
--
作者：
Zhou, Yi-Hui;Gallins, Paul J.;Etheridge, Amy S.;Jima, Dereje;Scholl, Elizabeth;Wright, Fred A.;Innocenti, Federico
在这项研究中，我们从n = 192个基因分型的肝脏样本中生成了全转录组RNA-Seq，并使用这些数据与来自GTEx项目（RNA-Seq）和以前的肝脏eQTL（微阵列）研究的现有数据，以在人类肝脏中创建增强的转录组序列资源。基因型-表达相关性分析显示与药物反应基因相关性显著增加。关联在两个RNA-Seq数据集之间基本上是一致的，具有一些适度的变化，这表明获得多个数据集以产生强大的资源的重要性。我们进一步使用经验贝叶斯模型来比较肝脏和另外20个GTEx组织中的eQTL模式，发现MHC基因，尤其是II类基因，富集了肝脏特异性eQTL模式。为了说明该资源在小样本量下增强GWAS分析的效用，我们开发了一种新的荟萃分析技术来结合联合收割机几个肝脏eQTL数据源。我们还说明了它的应用，使用转录组增强再分析的研究胰腺癌患者的中性粒细胞减少症。基因型与肝脏表达的关联，包括剪接变异及其遗传关联，可在可搜索的基因组浏览器中获得。
In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Fairfax, Benjamin P.;Makino, Seiko;Radhakrishnan, Jayachandran;Plant, Katharine;Leslie, Stephen;Dilthey, Alexander;Ellis, Peter;Langford, Cordelia;Vannberg, Fredrik O.;Knight, Julian C.
通讯作者：
Knight, Julian C.
影响因子：
4.8
作者：
Dolphin, CT;Beckett, DJ;Phillips, IR
通讯作者：
Phillips, IR
DOI：
10.1073/pnas.041616998
发表时间：
2001-02-13
影响因子：
11.1
作者：
Abrink, M;Ortiz, JA;Losson, R
通讯作者：
Losson, R
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium