MAGMA: generalized gene-set analysis of GWAS data.
MAGMA: generalized gene-set analysis of GWAS data.
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DOI：
10.1371/journal.pcbi.1004219
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发表时间：
2015-04
影响因子：
4.3
通讯作者：
Posthuma D
中科院分区：
文献类型：
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作者：
de Leeuw CA;Mooij JM;Heskes T;Posthuma D
通过以生物学上有意义的方式聚合复杂性状的数据，基因和基因集分析构成了对单标记分析的有价值的补充。然而，尽管目前存在各种基因和基因集分析方法，但它们普遍存在许多问题。大多数方法的统计能力受到标记之间的连锁不平衡的强烈影响，多标记关联通常难以检测，并且依赖排列来计算p值往往使分析的计算成本非常高。为了解决这些问题，我们开发了MAGMA，一种新的基因和基因集分析工具。基因分析是基于多元回归模型，以提供更好的统计性能。基因集分析被构建为围绕基因分析的单独层，以获得额外的灵活性。这种基因集分析还使用回归结构，允许对基因的连续特性进行一般化分析，并同时分析多个基因集和其他基因特性。对克罗恩病数据的模拟和分析用于评估MAGMA的性能，并将其与许多其他基因和基因集分析工具进行比较。结果表明，MAGMA在基因和基因集分析方面比其他工具有更大的能力，可以识别更多与克罗恩病相关的基因和基因集，同时保持正确的1型错误率。此外，对克罗恩病数据的MAGMA分析也被发现要快得多。基因和基因集分析是同时分析多个遗传标记以确定其共同作用的统计方法。当单个标记的影响太弱而无法检测时，可以使用这些方法，这是研究多基因性状时的常见问题。此外，基因集分析可以为性状遗传成分的功能和生物学机制提供额外的见解。虽然有许多基因和基因集分析的方法，但是，它们通常受到各种统计问题的影响，并且运行起来非常耗时。因此，我们开发了一种名为MAGMA的新方法来解决这些问题，并将其与许多现有工具进行了比较。我们的结果表明，MAGMA比其他方法检测到更多的相关基因和基因集，而且速度也快得多。该方法的建立方式也使其具有高度的灵活性。这使得它适合作为旨在调查更复杂研究问题的更一般统计分析的基础。
By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well. Gene and gene-set analysis are statistical methods for analysing multiple genetic markers simultaneously to determine their joint effect. These methods can be used when the effects of individual markers is too weak to detect, which is a common problem when studying polygenic traits. Moreover, gene-set analysis can provide additional insight into functional and biological mechanisms underlying the genetic component of a trait. Although a number of methods for gene and gene-set analysis are available however, they generally suffer from various statistical issues and can be very time-consuming to run. We have therefore developed a new method called MAGMA to address these issues, and have compared it to a number of existing tools. Our results show that MAGMA detects more associated genes and gene-sets than other methods, and is also considerably faster. The way the method is set up also makes it highly flexible. This makes it suitable as a basis for more general statistical analyses aimed at investigating more complex research questions.
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影响因子：
5.2
作者：
Moskvina, Valentina;Schmidt, Karl M.;O'Donovan, Michael C.
通讯作者：
O'Donovan, Michael C.
影响因子：
30.8
作者：
Ripke, Stephan;O'Dushlaine, Colm;Chambert, Kimberly;Moran, Jennifer L.;Kaehler, Anna K.;Akterin, Susanne;Bergen, Sarah E.;Collins, Ann L.;Crowley, James J.;Fromer, Menachem;Kim, Yunjung;Lee, Sang Hong;Magnusson, Patrik K. E.;Sanchez, Nick;Stahl, Eli A.;Williams, Stephanie;Wray, Naomi R.;Xia, Kai;Bettella, Francesco;Borglum, Anders D.;Bulik-Sullivan, Brendan K.;Cormican, Paul;Craddock, Nick;de Leeuw, Christiaan;Durmishi, Naser;Gill, Michael;Golimbet, Vera;Hamshere, Marian L.;Holmans, Peter;Hougaard, David M.;Kendler, Kenneth S.;Lin, Kuang;Morris, Derek W.;Mors, Ole;Mortensen, Preben B.;Neale, Benjamin M.;O'Neill, Francis A.;Owen, Michael J.;Milovancevic, Milica Pejovic;Posthuma, Danielle;Powell, John;Richards, Alexander L.;Riley, Brien P.;Ruderfer, Douglas;Rujescu, Dan;Sigurdsson, Engilbert;Silagadze, Teimuraz;Smit, August B.;Stefansson, Hreinn;Steinberg, Stacy;Suvisaari, Jaana;Tosato, Sarah;Verhage, Matthijs;Walters, James T.;Bramon, Elvira;Corvin, Aiden P.;O'Donovan, Michael C.;Stefansson, Kari;Scolnick, Edward;Purcell, Shaun;McCarroll, Steven A.;Sklar, Pamela;Hultman, Christina M.;Sullivan, Patrick F.
通讯作者：
Sullivan, Patrick F.
影响因子：
30.8
作者：
通讯作者：
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影响因子：
9.8
作者：
Liu, Jimmy Z.;Mcrae, Allan F.;Macgregor, Stuart
通讯作者：
Macgregor, Stuart
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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