Mapping the genetic architecture of gene expression in human liver.
Mapping the genetic architecture of gene expression in human liver.
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DOI：
10.1371/journal.pbio.0060107
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发表时间：
2008-05-06
期刊：
影响因子：
9.8
通讯作者：
Ulrich R
中科院分区：
文献类型：
--
作者：
Schadt EE;Molony C;Chudin E;Hao K;Yang X;Lum PY;Kasarskis A;Zhang B;Wang S;Suver C;Zhu J;Millstein J;Sieberts S;Lamb J;GuhaThakurta D;Derry J;Storey JD;Avila-Campillo I;Kruger MJ;Johnson JM;Rohl CA;van Nas A;Mehrabian M;Drake TA;Lusis AJ;Smith RC;Guengerich FP;Strom SC;Schuetz E;Rushmore TH;Ulrich R
与常见人类疾病相关的遗传变异并不直接导致疾病，而是作用于中间分子表型，进而诱导高级疾病性状的变化。因此，识别响应于DNA变化而变化并且还与疾病性状变化相关的分子表型具有提供所需的功能信息的潜力，所述功能信息不仅用于识别和验证直接受DNA变化影响的易感基因，而且用于理解这些基因在其中操作的分子网络以及这些网络中的变化如何导致疾病性状的变化。为此，我们分析了超过39，000个转录本，并对400多个人类肝脏样本中的782，476个独特的单核苷酸多态性（SNP）进行了基因分型，以表征人类肝脏中基因表达的遗传结构，人类肝脏是一种代谢活性组织，在许多常见的人类疾病中非常重要，包括肥胖症，糖尿病和动脉粥样硬化。这项基因表达的全基因组关联研究检测到SNP基因型与肝脏基因表达性状之间的6，000多个关联，其中许多已确定的相应基因已经与许多人类疾病有关。这些数据用于阐明常见人类疾病的原因的效用通过将它们与来自其他人类和小鼠群体的基因型和表达数据整合来证明。这为在越来越多的遗传基因座上鉴定的候选易感基因提供了急需的功能支持，这些基因座已被确定为疾病的关键驱动因素，来自疾病的全基因组关联研究。通过使用整合基因组学方法，我们强调了基因RPS26而不是ERBB3如何被我们的数据支持为最近在大规模全基因组关联研究中确定的新型1型糖尿病位点的最可能易感基因。我们还确定SORT 1和CELSR 2作为候选易感基因的位点最近与冠状动脉疾病和血浆低密度脂蛋白胆固醇水平的过程中。全基因组关联研究旨在确定特定人群中DNA变化与疾病、药物反应或其他感兴趣的表型相关的基因组区域。然而，与人类常见疾病等性状相关的DNA变化并不直接导致疾病，而是作用于中间分子表型，进而诱导高级疾病性状的变化。因此，识别响应于DNA变化而变化的分子表型也与疾病性状的变化相关，可以提供必要的功能信息，不仅可以识别和验证直接受DNA变化影响的易感基因，而且还可以了解这些基因运作的分子网络以及这些网络中的变化如何导致疾病性状的变化。为了实现这种方法，我们分析了427份人类肝脏样本中39，280个转录本的表达水平，并对782，476个SNP进行了基因分型，确定了数千种与肝脏基因表达密切相关的DNA变异。然后通过将这些关系与来自其他人类和小鼠群体的基因型和表达数据整合来利用这些关系，从而直接鉴定出与被鉴定为疾病关键驱动因素的遗传基因座相对应的候选易感基因。我们的分析能够为这些候选易感基因提供急需的功能支持。鉴定与人类组织中基因表达变化相关的DNA变化阐明了人类群体中基因表达的遗传结构，并能够直接鉴定与疾病相关的基因组区域中功能支持的候选易感基因。
Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. Genome-wide association studies seek to identify regions of the genome in which changes in DNA in a given population are correlated with disease, drug response, or other phenotypes of interest. However, changes in DNA that associate with traits like common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in the higher-order disease traits. Therefore, identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide the functional information necessary to not only identify and validate the susceptibility genes directly affected by changes in DNA, but to understand as well the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. To enable this type of approach we profiled the expression levels of 39,280 transcripts and genotyped 782,476 SNPs in 427 human liver samples, identifying thousands of DNA variants that strongly associated with liver gene expression. These relationships were then leveraged by integrating them with genotypic and expression data from other human and mouse populations, leading to the direct identification of candidate susceptibility genes corresponding to genetic loci identified as key drivers of disease. Our analysis is able to provide much needed functional support for these candidate susceptibility genes. Identifying changes in DNA that associate with changes in gene expression in human tissues elucidates the genetic architecture of gene expression in human populations and enables the direct identification of functionally supported candidate susceptibility genes in genomic regions associated with disease.
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与人类血液低密度脂蛋白胆固醇、高密度脂蛋白胆固醇或甘油三酯相关的六个新位点。
影响因子：
30.8
作者：
Kathiresan, Sekar;Melander, Olle;Guiducci, Candace;Surti, Aarti;Burtt, Noel P.;Rieder, Mark J.;Cooper, Gregory M.;Roos, Charlotta;Voight, Benjamin F.;Havulinna, Aki S.;Wahlstrand, Bjorn;Hedner, Thomas;Corella, Dolores;Tai, E. Shyong;Ordovas, Jose M.;Berglund, Goran;Vartiainen, Erkki;Jousilahti, Pekka;Hedblad, Bo;Taskinen, Marja-Riitta;Newton-Cheh, Christopher;Salomaa, Veikko;Peltonen, Leena;Groop, Leif;Altshuler, David M.;Orho-Melander, Marju
通讯作者：
Orho-Melander, Marju
影响因子：
30.8
作者：
Helgadottir, A;Manolescu, A;Stefansson, K
通讯作者：
Stefansson, K
影响因子：
56.9
作者：
Edwards, AO;Ritter, R;Farrer, LA
通讯作者：
Farrer, LA
影响因子：
1.8
作者：
Bleasby, K.;Castle, J. C.;Slatter, J. G.
通讯作者：
Slatter, J. G.
影响因子：
30.8
作者：
Grant, SFA;Thorleifsson, G;Stefansson, K
通讯作者：
Stefansson, K