A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.
A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer.
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复合零假设下的多效分析的一种强大方法确定了2型糖尿病和前列腺癌之间的新型基因座。
DOI：
10.1371/journal.pgen.1009218
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发表时间：
2020-12
期刊：
影响因子：
4.5
通讯作者：
Chatterjee N
中科院分区：
文献类型：
--
作者：
Ray D;Chatterjee N
越来越多的证据表明，多效性（即多个性状与相同遗传变异/基因座的关联）是一种非常常见的现象。跨表型关联测试通常用于联合分析全基因组关联研究 (GWAS) 中的多个性状。然而，基本方法通常旨在检验全局零假设，即遗传变异与任何性状都没有关联，拒绝该假设并不意味着多效性。在本文中，我们提出了一种新的统计方法 PLACO，用于通过考虑潜在的复合零假设（即变体与任何性状无关或仅与其中一个性状相关）来专门检测两个性状之间的多效性基因座。我们建议根据两项研究中遗传变异的 Z 统计量的乘积来检验零假设，并以混合分布的形式导出检验统计量的零分布，该混合分布允许变异的分数与任何性状无关或仅与其中一种性状相关。我们借用中介分析统计文献中的方法，允许对零分布进行渐近逼近，避免估计与混合比例和方差分量相关的干扰参数。仿真研究表明，所提出的方法可以保持 I 类误差，并且可以比通常用于测试多效性的替代更简单方法实现更大的功率增益。 PLACO 允许研究之间的汇总统计数据关联，这些关联可能是由于疾病性状之间共享控制而产生的。将 PLACO 应用到 2 型糖尿病和前列腺癌的两个大型病例对照 GWAS 的公开汇总数据中，涉及许多新的共享遗传区域：3q23 (ZBTB38)、6q25.3 (RGS17)、9p22.1 (HAUS6)、9p13.3 (UBAP2)、11p11.2 (RAPSN)、14q12 (AKAP6)、15q15 (KNL1) 和 18q23 (ZNF236)。我们提出了一种新方法 PLACO，它使用聚合水平的基因型-表型关联统计数据（通常称为 GWAS 摘要统计数据）来识别影响两种性状或疾病风险的遗传变异。它允许由于疾病特征之间共享控制而可能出现的研究之间的汇总统计数据的相关性。我们证明，与通常使用的替代方法相比，PLACO 可以实现更大的功率增益。我们将 PLACO 应用于两项大型病例对照研究的 2 型糖尿病和前列腺癌汇总数据。之前的许多研究都报道了这两种慢性疾病之间的负相关性，表明存在共同的危险因素。然而，人们对这种关联背后的共同遗传机制知之甚少。 PLACO 发现了一些个体性状分析未检测到的新的共享遗传区域。 PLACO 涉及的许多位点会增加一种疾病的风险，同时降低另一种疾病的风险。 PLACO 同样可以用于其他性状，以揭示共同的遗传风险因素。
There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 (ZBTB38), 6q25.3 (RGS17), 9p22.1 (HAUS6), 9p13.3 (UBAP2), 11p11.2 (RAPSN), 14q12 (AKAP6), 15q15 (KNL1) and 18q23 (ZNF236). We propose a new approach PLACO that uses aggregate-level genotype-phenotype association statistics—commonly referred to as GWAS summary statistics—to identify genetic variants that influence risk of two traits or diseases. It allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. We demonstrate that PLACO can achieve major power gain over alternative methods that are typically used. We applied PLACO to Type 2 Diabetes and Prostate Cancer summary data from two large case-control studies. Many previous studies have reported an inverse association of these two chronic diseases suggesting shared risk factors; however, shared genetic mechanisms underlying this association is poorly understood. PLACO identified a number of novel shared genetic regions that are not detected by individual trait analysis. Many of the loci implicated by PLACO increase risk for one disease while decreasing risk for the other. PLACO can similarly be used on other traits to shed light on shared genetic risk factors.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
1.8
作者：
Ray D;Li X;Pan W;Pankow JS;Basu S
通讯作者：
Basu S
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen