POLARIS: Polygenic LD-adjusted risk score approach for set-based analysis of GWAS data.
POLARIS: Polygenic LD-adjusted risk score approach for set-based analysis of GWAS data.
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DOI：
10.1002/gepi.22117
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发表时间：
2018-06
影响因子：
2.1
通讯作者：
Consortium WTG
中科院分区：
文献类型：
--
作者：
Baker E;Schmidt KM;Sims R;O'Donovan MC;Williams J;Holmans P;Escott-Price V;Consortium WTG
多基因风险评分（PRS）是一种总结由一组SNP捕获的加性性状方差的方法，并且可以通过利用公共全基因组关联研究（GWAS）数据集来增加基于集合的分析的能力。PRS旨在根据独立数据估计的相同或不同表型的多基因风险评估对某些表型的遗传易感性。我们提出将PRS作为一种基于集合的方法，并增加了连锁不平衡（LD）调整的组成部分，并可能扩展PRS方法以分析具有生物学意义的SNP集。我们将此方法称为POLARIS：Polygenic Ld-Adjusted Risk Score。POLARIS使用SNP相关矩阵的光谱分解识别SNP的LD结构，并使用LD调整剂量替换个体的SNP等位基因计数。使用原始基因型数据集以及来自第二个独立数据集的SNP效应量，POLARIS可用于基于集的分析。MAGMA是另一种基于集合的方法，采用主成分分析来解释原始基因型数据集中标记之间的LD。我们使用模拟，无论是简单的构造和真实的LD-结构，比较这些方法的能力。POLARIS显示出比仅应用于原始基因型数据集的MAGMA更高的功效，但对两个数据集的组合分析的功效较低或相当。POLARIS的优势在于，它使用所有可用的SNP生成每个人每个集合的风险评分，并旨在通过利用测试数据集中自包含关联测试中发现集合的效应量来增加功效。
Polygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set‐based analyses by leveraging public genome‐wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set‐based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets. We call this method POLARIS: POlygenic Ld‐Adjusted RIsk Score. POLARIS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals' SNP allele counts with LD‐adjusted dosages. Using a raw genotype dataset together with SNP effect sizes from a second independent dataset, POLARIS can be used for set‐based analysis. MAGMA is an alternative set‐based approach employing principal component analysis to account for LD between markers in a raw genotype dataset. We used simulations, both with simple constructed and real LD‐structure, to compare the power of these methods. POLARIS shows more power than MAGMA applied to the raw genotype dataset only, but less or comparable power to combined analysis of both datasets. POLARIS has the advantages that it produces a risk score per person per set using all available SNPs, and aims to increase power by leveraging the effect sizes from the discovery set in a self‐contained test of association in the test dataset.
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影响因子：
4.3
作者：
Lamparter D;Marbach D;Rueedi R;Kutalik Z;Bergmann S
通讯作者：
Bergmann S
影响因子：
9.8
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Macgregor, Stuart
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通讯作者：
Hubbard TJ
影响因子：
30.8
作者：
Harold, Denise;Abraham, Richard;Hollingworth, Paul;Sims, Rebecca;Gerrish, Amy;Hamshere, Marian L.;Pahwa, Jaspreet Singh;Moskvina, Valentina;Dowzell, Kimberley;Williams, Amy;Jones, Nicola;Thomas, Charlene;Stretton, Alexandra;Morgan, Angharad R.;Lovestone, Simon;Powell, John;Proitsi, Petroula;Lupton, Michelle K.;Brayne, Carol;Rubinsztein, David C.;Gill, Michael;Lawlor, Brian;Lynch, Aoibhinn;Morgan, Kevin;Brown, Kristelle S.;Passmore, Peter A.;Craig, David;McGuinness, Bernadette;Todd, Stephen;Holmes, Clive;Mann, David;Smith, A. David;Love, Seth;Kehoe, Patrick G.;Hardy, John;Mead, Simon;Fox, Nick;Rossor, Martin;Collinge, John;Maier, Wolfgang;Jessen, Frank;Schuermann, Britta;van den Bussche, Hendrik;Heuser, Isabella;Kornhuber, Johannes;Wiltfang, Jens;Dichgans, Martin;Froelich, Lutz;Hampel, Harald;Huell, Michael;Rujescu, Dan;Goate, Alison M.;Kauwe, John S. K.;Cruchaga, Carlos;Nowotny, Petra;Morris, John C.;Mayo, Kevin;Sleegers, Kristel;Bettens, Karolien;Engelborghs, Sebastiaan;De Deyn, Peter P.;Van Broeckhoven, Christine;Livingston, Gill;Bass, Nicholas J.;Gurling, Hugh;McQuillin, Andrew;Gwilliam, Rhian;Deloukas, Panagiotis;Al-Chalabi, Ammar;Shaw, Christopher E.;Tsolaki, Magda;Singleton, Andrew B.;Guerreiro, Rita;Muehleisen, Thomas W.;Noethen, Markus M.;Moebus, Susanne;Joeckel, Karl-Heinz;Klopp, Norman;Wichmann, H-Erich;Carrasquillo, Minerva M.;Pankratz, V. Shane;Younkin, Steven G.;Holmans, Peter A.;O'Donovan, Michael;Owen, Michael J.;Williams, Julie
通讯作者：
Williams, Julie
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ