A comparison of association methods correcting for population stratification in case-control studies.
A comparison of association methods correcting for population stratification in case-control studies.
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DOI：
10.1111/j.1469-1809.2010.00639.x
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发表时间：
2011-05
影响因子：
1.9
通讯作者：
Wang Z
中科院分区：
文献类型：
--
作者：
Wu C;DeWan A;Hoh J;Wang Z
人群分层是疾病标志物相关性病例对照研究中的一个重要问题。如果不能正确解释种群结构，可能会导致虚假关联或功效降低。在这篇文章中，我们比较了六种方法的性能校正人群分层的病例对照关联研究。这些方法包括基因组对照（GC）、EIGENSTRAT、基于主成分的逻辑回归（PCA-L）、LAPECT、ROADTRIPS和EMMAX。我们还包括未校正的阿米蒂奇测试进行比较。在模拟研究中，我们考虑了广泛的人口结构模型的无关样本，包括混合。我们的模拟结果表明，PCA-L和LAPPACT在所有研究的情况下都表现良好，而GC，ROADTRIPS和EMMAX未能纠正单核苷酸多态性（SNP）的群体结构，这些SNP在祖先群体中表现出强烈的分化。由于分层，Armitage检验未调整混杂因素，因此存在夸大的I类错误。在所有校正方法中，EMMAX具有最大的功效，基于针对具有不相关个体的样本考虑的群体结构设置。这三种方法，EIGENSTRAT，PCA-L和LAPPACT，是相当的，并且在几乎所有情况下都优于GC和ROADTRIPS。
Population stratification is an important issue in case–control studies of disease-marker association. Failure to properly account for population structure can lead to spurious association or reduced power. In this article, we compare the performance of six methods correcting for population stratification in case–control association studies. These methods include genomic control (GC), EIGENSTRAT, principal component-based logistic regression (PCA-L), LAPSTRUCT, ROADTRIPS, and EMMAX. We also include the uncorrected Armitage test for comparison. In the simulation studies, we consider a wide range of population structure models for unrelated samples, including admixture. Our simulation results suggest that PCA-L and LAPSTRUCT perform well over all the scenarios studied, whereas GC, ROADTRIPS, and EMMAX fail to correct for population structure at single nucleotide polymorphisms (SNPs) that show strong differentiation across ancestral populations. The Armitage test does not adjust for confounding due to stratification thus has inflated type I error. Among all correction methods, EMMAX has the greatest power, based on the population structure settings considered for samples with unrelated individuals. The three methods, EIGENSTRAT, PCA-L, and LAPSTRUCT, are comparable, and outperform both GC and ROADTRIPS in almost all situations.
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影响因子：
30.8
作者：
Zeggini, Eleftheria;Scott, Laura J.;Saxena, Richa;Voight, Benjamin F.;Marchini, Jonathan L.;Hu, Tianle;de Bakker, Paul I. W.;Abecasis, Goncalo R.;Almgren, Peter;Andersen, Gitte;Ardlie, Kristin;Bostroem, Kristina Bengtsson;Bergman, Richard N.;Bonnycastle, Lori L.;Borch-Johnsen, Knut;Burtt, Noel P.;Chen, Hong;Chines, Peter S.;Daly, Mark J.;Deodhar, Parimal;Ding, Chia-Jen;Doney, Alex S. F.;Duren, William L.;Elliott, Katherine S.;Erdos, Michael R.;Frayling, Timothy M.;Freathy, Rachel M.;Gianniny, Lauren;Grallert, Harald;Grarup, Niels;Groves, Christopher J.;Guiducci, Candace;Hansen, Torben;Herder, Christian;Hitman, Graham A.;Hughes, Thomas E.;Isomaa, Bo;Jackson, Anne U.;Jorgensen, Torben;Kong, Augustine;Kubalanza, Kari;Kuruvilla, Finny G.;Kuusisto, Johanna;Langenberg, Claudia;Lango, Hana;Lauritzen, Torsten;Li, Yun;Lindgren, Cecilia M.;Lyssenko, Valeriya;Marvelle, Amanda F.;Meisinger, Christa;Midthjell, Kristian;Mohlke, Karen L.;Morken, Mario A.;Morris, Andrew D.;Narisu, Narisu;Nilsson, Peter;Owen, Katharine R.;Palmer, Colin N. A.;Payne, Felicity;Perry, John R. B.;Pettersen, Elin;Platou, Carl;Prokopenko, Inga;Qi, Lu;Qin, Li;Rayner, Nigel W.;Rees, Matthew;Roix, Jeffrey J.;Sandbaek, Anelli;Shields, Beverley;Sjogren, Marketa;Steinthorsdottir, Valgerdur;Stringham, Heather M.;Swift, Amy J.;Thorleifsson, Gudmar;Thorsteinsdottir, Unnur;Timpson, Nicholas J.;Tuomi, Tiinamaija;Tuomilehto, Jaakko;Walker, Mark;Watanabe, Richard M.;Weedon, Michael N.;Willer, Cristen J.;Illig, Thomas;Hveem, Kristian;Hu, Frank B.;Laakso, Markku;Stefansson, Kari;Pedersen, Oluf;Wareham, Nicholas J.;Barroso, Ines;Hattersley, Andrew T.;Collins, Francis S.;Groop, Leif;McCarthy, Mark I.;Boehnke, Michael;Altshuler, David
通讯作者：
Altshuler, David
影响因子：
9.8
作者：
Epstein, Michael P.;Allen, Andrew S.;Satten, Glen A.
通讯作者：
Satten, Glen A.
影响因子：
9.8
作者：
Kimmel, Gad;Jordan, Michael I.;Karp, Richard M.
通讯作者：
Karp, Richard M.
影响因子：
9.8
作者：
Thornton, Timothy;McPeek, Mary Sara
通讯作者：
McPeek, Mary Sara
影响因子：
9.8
作者：
Luca, Diana;Ringquist, Steven;Trucco, Massimo
通讯作者：
Trucco, Massimo