Comparison of methods for transcriptome imputation through application to two common complex diseases.
Comparison of methods for transcriptome imputation through application to two common complex diseases.
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DOI：
10.1038/s41431-018-0176-5
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发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Cordell HJ
中科院分区：
文献类型：
--
作者：
Fryett JJ;Inshaw J;Morris AP;Cordell HJ
转录组插补已经成为一种流行的方法，用于整合基因型数据与公开可用的表达数据，以研究基因在复杂性状中的潜在因果作用。在这里，我们比较了三种方法（PrediXcan，MetaXcan和FUSION）通过应用于来自Wellcome Trust病例对照联盟的克罗恩病和1型糖尿病的全基因组关联研究（GWAS）数据。我们调查：（i）每种方法的结果如何相互比较以及与标准GWAS分析的结果如何比较;以及（ii）预测工具使用的模型中的变体如何与先前报告为eQTL的变体进行比较。我们发现，当应用于相同的GWAS数据时，所有方法都会产生高度相关的结果，尽管对于一个基因子集（主要是在主要组织相容性复合体中），这些方法存在强烈分歧。我们还观察到，通过这些方法检测到的大多数关联发生在已知的GWAS风险位点附近。应用这些转录组插补方法对克罗恩病和1型糖尿病的荟萃分析进行汇总统计，检测到53个显著表达-克罗恩病关联和154个显著表达-1型糖尿病关联，从而深入了解这些疾病的生物学基础。我们的结论是，虽然目前实现的转录组插补通常检测到的关联比GWAS少，但它们提供了一种有趣的方式来解释关联信号，以识别潜在的因果基因。
Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn’s disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn’s disease and type 1 diabetes detects 53 significant expression—Crohn’s disease associations and 154 significant expression—type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
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影响因子：
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作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian
影响因子：
30.8
作者：
Jin, Ying;Andersen, Genevieve;Yorgov, Daniel;Ferrara, Tracey M.;Ben, Songtao;Brownson, Kelly M.;Holland, Paulene J.;Birlea, Stanca A.;Siebert, Janet;Hartmann, Anke;Lienert, Anne;van Geel, Nanja;Lambert, Jo;Luiten, Rosalie M.;Wolkerstorfer, Albert;van der Veen, J. P. Wietze;Bennett, Dorothy C.;Taieb, Alain;Ezzedine, Khaled;Kemp, E. Helen;Gawkrodger, David J.;Weetman, Anthony P.;Koks, Sulev;Prans, Ele;Kingo, Kulli;Karelson, Maire;Wallace, Margaret R.;McCormack, Wayne T.;Overbeck, Andreas;Moretti, Silvia;Colucci, Roberta;Picardo, Mauro;Silverberg, Nanette B.;Olsson, Mats;Valle, Yan;Korobko, Igor;Boehm, Markus;Lim, Henry W.;Hamzavi, Iltefat;Zhou, Li;Mi, Qing-Sheng;Fain, Pamela R.;Santorico, Stephanie A.;Spritz, Richard A.
通讯作者：
Spritz, Richard A.
影响因子：
9.8
作者：
Mancuso, Nicholas;Shi, Huwenbo;Pasaniuc, Bogdan
通讯作者：
Pasaniuc, Bogdan