Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines.
Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines.
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DOI：
10.1371/journal.pgen.1000287
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发表时间：
2008-11
期刊：
影响因子：
4.5
通讯作者：
Altshuler D
中科院分区：
文献类型：
--
作者：
Choy E;Yelensky R;Bonakdar S;Plenge RM;Saxena R;De Jager PL;Shaw SY;Wolfish CS;Slavik JM;Cotsapas C;Rivas M;Dermitzakis ET;Cahir-McFarland E;Kieff E;Hafler D;Daly MJ;Altshuler D
淋巴母细胞系（LCL），最初收集的DNA的可再生来源，现在被用作一个模型系统来研究人类细胞中的基因型-表型关系，包括搜索影响单个mRNA水平的QTL和对药物和辐射的反应。在尝试使用来自国际HapMap项目的269个LCL绘制药物反应基因的过程中，我们评估了生物噪声和非遗传混杂因素对LCL性状变异的影响程度。虽然药物反应在技术上可以在给定的一天很好地测量，我们观察到显着的日常变异性和非遗传混杂因素，如基线生长率和代谢状态的文化。在校正这些混杂因素后，我们无法检测到任何对药物反应具有全基因组意义的QTL。mRNA水平中更高比例的变异可归因于非遗传因素（个体内变异-即，生物噪声、用于转化细胞的EBV病毒的水平、ATP水平）与可检测的eQTL相比。最后，为了提高功效，我们集中分析了那些既有可检测eQTL又与药物反应相关的基因;我们无法检测到eQTL SNP与模型中药物反应令人信服相关的证据。虽然LCL是药物遗传学实验的一种有前途的模型，但生物噪声和体外伪影可能会降低功效，并有可能因混淆而产生虚假关联。淋巴母细胞系（LCL）的使用已经从DNA的可再生来源发展为体外模型系统，以在受控实验室环境中研究基因表达、药物反应和其他性状的遗传学。虽然SNPs和mRNA水平（eQTL）之间的令人信服的关系已被描述，在何种程度上非遗传变量也影响LCL的表型是不太好的特点。在尝试绘制体外药物反应基因图谱的过程中，我们评估了重复实验中体外性状的重现性、用于将B细胞转化为细胞系的EBV病毒的影响以及体外培养条件的影响。我们发现，对至少一些药物的反应和许多mRNA的水平在技术上可以很好地测量，但在实验中和非遗传混杂因素（如生长率，EBV水平和ATP水平）之间存在差异。这些非遗传因素的影响既可以降低检测DNA变异和性状之间真实关系的能力，也可以产生DNA变异和性状之间非遗传混杂和虚假关联的可能性。
Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype–phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance—i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding. The use of lymphoblastoid cell lines (LCLs) has evolved from a renewable source of DNA to an in vitro model system to study the genetics of gene expression, drug response, and other traits in a controlled laboratory setting. While convincing relationships between SNPs and mRNA levels (eQTLs) have been described, the degree to which non-genetic variables also influence phenotypes in LCLs is less well characterized. In the course of attempting to map genes for drug responses in vitro, we evaluated the reproducibility of in vitro traits across replicates, the impact of the EBV virus used to transform B cells into cell lines, and the effect of in vitro culture conditions. We found that responses to at least some drugs and levels of many mRNAs can be technically well measured, but vary both across experiments and with non-genetic confounders such as growth rates, EBV levels, and ATP levels. The influence of such non-genetic factors can both decrease power to detect true relationships between DNA variation and traits and create the potential for non-genetic confounding and spurious associations between DNA variants and traits.
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影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
20.3
作者：
Le Clorennec, C;Youlyouz-Marfak, I;Feuillard, J
通讯作者：
Feuillard, J
影响因子：
30.8
作者：
Haiman, Christopher A.;Patterson, Nick;Reich, David
通讯作者：
Reich, David
影响因子：
9.8
作者：
Huang, R. Stephanie;Duan, Shiwei;Dolan, M. Eileen
通讯作者：
Dolan, M. Eileen