Robust prediction of expression differences among human individuals using only genotype information.
Robust prediction of expression differences among human individuals using only genotype information.
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DOI：
10.1371/journal.pgen.1003396
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发表时间：
2013-03
期刊：
影响因子：
4.5
通讯作者：
Segal E
中科院分区：
文献类型：
--
作者：
Manor O;Segal E
许多与人类个体的基因表达变化显著相关的遗传变异已经被确定，但这些变异预测未知个体表达的能力很少被评估。在这里，我们设计了一种算法，给定一组个体的训练表达和基因型数据，预测看不见的测试个体的基因表达，仅考虑其在预测基因的局部基因组附近的基因型。值得注意的是，所得到的预测非常稳健，因为它们在训练集和测试集之间吻合得很好，即使训练集和测试集由来自不同人群的个体组成。因此，尽管可以预测的基因总数相对较少，正如我们选择忽略环境因素和反式序列变异等影响所预期的那样，预测的鲁棒性意味着可以预测基因的身份和定量程度是预先已知的。我们还提出了一个扩展，采用异质类型的基因组注释不同的权重的各种遗传变异的重要性，我们表明，分配更高的权重的变异与特定的注释，如接近基因和高区域G/C含量可以进一步提高预测。最后，成功预测的基因平均具有更高的表达和更大的个体变异性，从而可以从顺式遗传变异中预测基因类型的特征。已发现不同个体的基因表达差异在对不同疾病的易感性中起作用。此外，迄今已发现许多与表达变化相关的遗传变异。然而，他们的联合能力，准确地预测这些变化还没有得到很好的理解，很少被评估。在这里，我们设计了一种方法，使用多种遗传变异来解释个体间基因表达的差异。我们的方法的一个重要方面是它的鲁棒性，因为我们的预测在训练集和测试集之间吻合得很好。因此，虽然可以解释的基因数量相对较少，但可以预测的基因的身份和数量程度是事先已知的。我们还对我们的方法进行了扩展，该方法集成了不同的基因组注释，例如遗传变异的位置或其背景，以在我们的模型中对遗传变异进行差异化加权并改善预测。最后，成功预测的基因平均具有更高的表达和更大的个体变异性，这为我们的方法可以预测的基因类型的特征提供了深入了解。
Many genetic variants that are significantly correlated to gene expression changes across human individuals have been identified, but the ability of these variants to predict expression of unseen individuals has rarely been evaluated. Here, we devise an algorithm that, given training expression and genotype data for a set of individuals, predicts the expression of genes of unseen test individuals given only their genotype in the local genomic vicinity of the predicted gene. Notably, the resulting predictions are remarkably robust in that they agree well between the training and test sets, even when the training and test sets consist of individuals from distinct populations. Thus, although the overall number of genes that can be predicted is relatively small, as expected from our choice to ignore effects such as environmental factors and trans sequence variation, the robust nature of the predictions means that the identity and quantitative degree to which genes can be predicted is known in advance. We also present an extension that incorporates heterogeneous types of genomic annotations to differentially weigh the importance of the various genetic variants, and we show that assigning higher weights to variants with particular annotations such as proximity to genes and high regional G/C content can further improve the predictions. Finally, genes that are successfully predicted have, on average, higher expression and more variability across individuals, providing insight into the characteristics of the types of genes that can be predicted from their cis genetic variation. Variation in gene expression across different individuals has been found to play a role in susceptibility to different diseases. In addition, many genetic variants that are linked to changes in expression have been found to date. However, their joint ability to accurately predict these changes is not well understood and has rarely been evaluated. Here, we devise a method that uses multiple genetic variants to explain the variation in expression of genes across individuals. One important aspect of our method is its robustness, in that our predictions agree well between training and test sets. Thus, although the number of genes that could be explained is relatively small, the identity and quantitative degree to which genes can be predicted is known in advance. We also present an extension to our method that integrates different genomic annotations such as location of the genetic variant or its context to differentially weigh the genetic variants in our model and improve predictions. Finally, genes that are successfully predicted have, on average, higher expression and more variability across individuals, providing insight into the characteristics of the types of genes that can be predicted by our method.
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影响因子：
4.5
作者：
Lee SI;Dudley AM;Drubin D;Silver PA;Krogan NJ;Pe'er D;Koller D
通讯作者：
Koller D
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：
4.5
作者：
Stranger BE;Montgomery SB;Dimas AS;Parts L;Stegle O;Ingle CE;Sekowska M;Smith GD;Evans D;Gutierrez-Arcelus M;Price A;Raj T;Nisbett J;Nica AC;Beazley C;Durbin R;Deloukas P;Dermitzakis ET
通讯作者：
Dermitzakis ET
DOI：
10.1126/science.1183621
发表时间：
2010-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Kasowski M;Grubert F;Heffelfinger C;Hariharan M;Asabere A;Waszak SM;Habegger L;Rozowsky J;Shi M;Urban AE;Hong MY;Karczewski KJ;Huber W;Weissman SM;Gerstein MB;Korbel JO;Snyder M
通讯作者：
Snyder M
影响因子：
7
作者：
Fraser, Hunter B.;Xie, Xiaohui
通讯作者：
Xie, Xiaohui