Comparison of genotype clustering tools with rare variants.
Comparison of genotype clustering tools with rare variants.
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DOI：
10.1186/1471-2105-15-52
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发表时间：
2014-02-21
影响因子：
3
通讯作者：
Dubé MP
中科院分区：
文献类型：
--
作者：
Perreault LP;Legault MA;Barhdadi A;Provost S;Normand V;Tardif JC;Dubé MP
沿着高通量测序技术的改进，遗传学界对罕见变异/常见疾病假说表现出显著的兴趣。虽然测序对于大型研究仍然是禁止的，但针对罕见变体的市售基因分型阵列被证明是合理的替代方案。基于阵列的方法的技术挑战是通过聚类强度数据点来导出基因型类别（纯合或杂合）的任务。常见多态性的聚类工具的性能是公认的，而它们在用大比例的罕见变体（其中含有罕见等位基因的基因型的数据点稀疏）进行时的性能则不太为人所知。我们使用Illumina的HumanExome BeadChip比较了四种聚类工具（GenCall，GenoSNP，optiCall和zCall）对超过10，000个样本进行基因分型的性能，其中包括247，870个变体，其中90%在欧洲血统人群中的次要等位基因频率低于5%。测试了GenCall的不同参考参数和GenoSNP的不同初始参数。使用来自1000个基因组计划的数据作为金标准来评估基因分型的准确性，并测量工具之间的一致性。GenoSNP与金标准的一致性低于预期，并通过改变工具的初始参数而增加。虽然这四种工具对常见等位基因的一致性高于99%，但其中一些对罕见等位基因的一致性较差。使用提供高于99%的一致率的实验重复来评估每个工具的基因型调用的再现性。对于约95%的变体，基因型调用的工具间一致性很高。大多数工具产生类似的错误率（约0.02），除了zCall表现更好，平均错误率为0.00164。GenoSNP聚类工具不能与HumanExome BeadChip一起直接“开箱即用”运行，因为修改硬编码参数是实现最佳性能所必需的。总体而言，GenCall略微优于HumanExome BeadChip的其他工具。实验重复的使用提供了一个有价值的质量控制工具，基因分型项目与罕见的变异。
Along with the improvement of high throughput sequencing technologies, the genetics community is showing marked interest for the rare variants/common diseases hypothesis. While sequencing can still be prohibitive for large studies, commercially available genotyping arrays targeting rare variants prove to be a reasonable alternative. A technical challenge of array based methods is the task of deriving genotype classes (homozygous or heterozygous) by clustering intensity data points. The performance of clustering tools for common polymorphisms is well established, while their performance when conducted with a large proportion of rare variants (where data points are sparse for genotypes containing the rare allele) is less known. We have compared the performance of four clustering tools (GenCall, GenoSNP, optiCall and zCall) for the genotyping of over 10,000 samples using the Illumina’s HumanExome BeadChip, which includes 247,870 variants, 90% of which have a minor allele frequency below 5% in a population of European ancestry. Different reference parameters for GenCall and different initial parameters for GenoSNP were tested. Genotyping accuracy was assessed using data from the 1000 Genomes Project as a gold standard, and agreement between tools was measured. Concordance of GenoSNP’s calls with the gold standard was below expectations and was increased by changing the tool’s initial parameters. While the four tools provided concordance with the gold standard above 99% for common alleles, some of them performed poorly for rare alleles. The reproducibility of genotype calls for each tool was assessed using experimental duplicates which provided concordance rates above 99%. The inter-tool agreement of genotype calls was high for approximately 95% of variants. Most tools yielded similar error rates (approximately 0.02), except for zCall which performed better with a 0.00164 mean error rate. The GenoSNP clustering tool could not be run straight “out of the box” with the HumanExome BeadChip, as modification of hard coded parameters was necessary to achieve optimal performance. Overall, GenCall marginally outperformed the other tools for the HumanExome BeadChip. The use of experimental replicates provided a valuable quality control tool for genotyping projects with rare variants.
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来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
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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
DOI：
10.1093/bioinformatics/bts180
发表时间：
2012-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Shah TS;Liu JZ;Floyd JA;Morris JA;Wirth N;Barrett JC;Anderson CA
通讯作者：
Anderson CA
影响因子：
1.8
作者：
Liu N;Zhang D;Zhao H
通讯作者：
Zhao H
DOI：
10.1348/000711006x126600
发表时间：
2008-05-01
影响因子：
2.6
作者：
Gwet, Kilem Li
通讯作者：
Gwet, Kilem Li