A statistical framework for eQTL mapping using RNA-seq data.
A statistical framework for eQTL mapping using RNA-seq data.
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DOI：
10.1111/j.1541-0420.2011.01654.x
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发表时间：
2012-03
期刊：
影响因子：
1.9
通讯作者：
Sun W
中科院分区：
文献类型：
--
作者：
Sun W
在不久的将来，RNA-SEQ可能会取代基因表达微阵列。使用RNA-seq，可以使用映射到该基因的序列读取的总数量来估计该基因的表达，称为总读取计数(TREC)。传统的eQTL作图方法，如线性回归，经过适当的归一化后，可以应用于TREC测量。在这篇文章中，我们证明了eQTL作图，通过使用离散分布直接对TREC进行建模，比数据归一化和线性回归两步法具有更高的统计能力。此外，RNA-seq还提供了微阵列中无法获得的等位基因特异性表达(ASE)信息。通过结合TREC和ASE的信息，我们可以在计算上区分顺式和反式eQTL，从而进一步提高顺式-eQTL定位的能力。仿真和实际数据研究都证实了新方法的改进效果。我们还讨论了RNA-SEQ实验的设计问题。具体地说，我们表明，通过结合TREC和ASE测量，通过减少样本大小同时增加每个样本的序列读取数量，可以最大限度地降低成本，并保留顺-eQTL图谱的统计能力。除了RNA-SEQ数据外，我们的方法还可以用于研究其他类型测序数据的遗传基础，例如CHIP-SEQ(染色质免疫沉淀随后DNA测序)数据。在本文中，我们主要使用基于关联的方法对单基因进行eQTL定位。然而，我们的方法为使用RNA-seq数据的eQTL定位方法的未来发展(例如基于连锁的eQTL定位)以及多个遗传标记和/或多个基因的联合研究建立了一个统计框架。
RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the Total Read Count (TReC). Traditional eQTL mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this paper, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as ChIP-seq (chromatin immunoprecipitation followed by DNA sequencing) data. In this paper, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g. linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.
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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：
5.8
作者：
Pan, Wei
通讯作者：
Pan, Wei
影响因子：
4.5
作者：
Ronald J;Brem RB;Whittle J;Kruglyak L
通讯作者：
Kruglyak L
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
1.9
作者：
Kendziorski, CM;Chen, M;Attie, AD
通讯作者：
Attie, AD