Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.
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DOI：
10.1038/nature05911
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发表时间：
2007-06-07
期刊：
影响因子：
64.8
通讯作者：
Worthington, Jane
中科院分区：
文献类型：
--
作者：
Burton, Paul R.;Clayton, David G.;Cardon, Lon R.;Craddock, Nick;Deloukas, Panos;Duncanson, Audrey;Kwiatkowski, Dominic P.;McCarthy, Mark I.;Ouwehand, Willem H.;Samani, Nilesh J.;Todd, John A.;Donnelly, Peter;Barrett, Jeffrey C.;Davison, Dan;Easton, Doug;Evans, David;Leung, Hin-Tak;Marchini, Jonathan L.;Morris, Andrew P.;Spencer, Chris C. A.;Tobin, Martin D.;Attwood, Antony P.;Boorman, James P.;Cant, Barbara;Everson, Ursula;Hussey, Judith M.;Jolley, Jennifer D.;Knight, Alexandra S.;Koch, Kerstin;Meech, Elizabeth;Nutland, Sarah;Prowse, Christopher V.;Stevens, Helen E.;Taylor, Niall C.;Walters, Graham R.;Walker, Neil M.;Watkins, Nicholas A.;Winzer, Thilo;Jones, Richard W.;McArdle, Wendy L.;Ring, Susan M.;Strachan, David P.;Pembrey, Marcus;Breen, Gerome;St Clair, David;Caesar, Sian;Gordon-Smith, Katherine;Jones, Lisa;Fraser, Christine;Green, Elain K.;Grozeva, Detelina;Hamshere, Marian L.;Holmans, Peter A.;Jones, Ian R.;Kirov, George;Moskvina, Valentina;Nikolov, Ivan;O'Donovan, Michael C.;Owen, Michael J.;Collier, David A.;Elkin, Amanda;Farmer, Anne;Williamson, Richard;McGuffin, Peter;Young, Allan H.;Ferrier, I. Nicol;Ball, Stephen G.;Balmforth, Anthony J.;Barrett, Jennifer H.;Bishop, D. Timothy;Iles, Mark M.;Maqbool, Azhar;Yudasheve, Nadira;Hall, Alistair S.;Braund, Peter S.;Dixon, Richard J.;Mangino, Massimo;Stevens, Suzanne;Thompson, John R.;Bredin, Francesca;Tremelling, Mark;Parkes, Miles;Drummond, Hazel;Lees, Charles W.;Nimmo, Elaine R.;Satsangi, Jack;Fisher, Sheila A.;Forbes, Alistair;Lewis, Cathryn M.;Onnie, Clive M.;Prescott, Natalie J.;Sanderson, Jeremy;Mathew, Christopher G.;Barbour, Jamie;Mohiuddin, M. Khalid;Todhunter, Catherine E.;Mansfield, John C.;Ahmad, Tariq;Cummings, Fraser R.;Jewell, Derek P.;Webster, John;Brown, Morris J.;Lathrop, G. Mark;Connell, John;Dominiczak, Anna;Marcano, Carolina A. Braga;Burke, Beverley;Dobson, Richard;Gungadoo, Johannie;Lee, Kate L.;Munroe, Patricia B.;Newhouse, Stephen J.;Onipinla, Abiodun;Wallace, Chris;Xue, Mingzhan;Caulfield, Mark;Farrall, Martin;Barton, Anne;Bruce, Ian N.;Donovan, Hannah;Eyre, Steve;Gilbert, Paul D.;Hider, Samantha L.;Hinks, Anne M.;John, Sally L.;Potter, Catherine;Silman, Alan J.;Symmons, Deborah P. M.;Thomson, Wendy;Worthington, Jane;Dunger, David B.;Widmer, Barry;Frayling, Timothy M.;Freathy, Rachel M.;Lango, Hana;Perry, John R. B.;Shields, Beverley M.;Weedon, Michael N.;Hattersley, Andrew T.;Hitman, Graham A.;Walker, Mark;Elliott, Kate S.;Groves, Christopher J.;Lindgren, Cecilia M.;Rayner, Nigel W.;Timpson, Nicholas J.;Zeggini, Eleftheria;Newport, Melanie;Sirugo, Giorgio;Lynons, Emily;Vannberg, Fredrik;Brown, Matthew A.;Franklyn, Jayne A.;Heward, Joanne M.;Simmonds, Matthew J.;Hill, Adrian V. S.;Bradbury, Linda A.;Farrar, Claire;Pointon, Jennifer J.;Wordsmith, Paul;Gough, Stephen C. L.;Seal, Sheila;Stratton, Michael R.;Rahman, Nazneen;Ban, Maria;Goris, An;Sawcer, Stephen J.;Compston, Alistair;Conway, David;Jallow, Muminatou;Bumpstead, Suzannah J.;Chaney, Amy;Downes, Kate;Ghori, Mohammed J. R.;Gwilliam, Rhian;Inouye, Michael;Keniry, Andrew;King, Emma;McGinnis, Ralph;Potter, Simon;Ravindrarajah, Rathi;Whittaker, Pamela;Withers, David;Easton, Doug;Pereira-Gale, Joanne;Hallgrimsdottir, Ingilef B.;Howie, Bryan N.;Su, Zhan;Teo, Yik Ying;Vukcevic, Damjan;Bentley, David;Caulfield, Mark;Mathew, Christopher G.;Worthington, Jane
越来越多的证据表明，全基因组关联（GWA）研究代表了一种强有力的方法来识别与人类常见疾病有关的基因。我们描述了一项在英国人群中进行的联合GWA研究（使用Affyscore GeneChip 500 K Mapping Array Set），该研究对7种主要疾病中的每种疾病的约2，000人进行了检查，并对约3，000人进行了对照。病例对照比较确定了P<5×10-7的24个独立关联信号：双相情感障碍1个，冠状动脉疾病1个，克罗恩病9个，类风湿性关节炎3个，1型糖尿病7个，2型糖尿病3个。根据先前的发现和迄今完成的重复研究，几乎所有这些信号都反映了真正的易感性效应。我们观察到在许多以前确定的基因座的关联，并发现令人信服的证据表明，一些基因座赋予一个以上的研究疾病的风险。在所有疾病中，我们发现了大量可能产生额外易感性基因座的进一步信号（包括58个单点P值在10-5和5×10-7之间的基因座）。适当大的样本的重要性，证实了在大多数位点观察到的适度的效果大小。因此，这项研究代表了一个彻底的验证全球妇女评估的方法。它还表明，谨慎使用一个共享的对照组代表了一种安全有效的方法来GWA分析多种疾病表型;已经产生了一个全基因组基因型数据库，用于未来研究英国人口中的常见疾病;并表明，如果排除非欧洲血统的个人，英国人口的人口分层程度一般是适度的。我们的发现为探索这些重要疾病的病理生理学提供了新的途径。我们预计，我们的数据、结果和软件将广泛提供给其他研究人员，为人类遗传学研究提供强大的资源。
There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined ~2,000 individuals for each of 7 major diseases and a shared set of ~3,000 controls. Case-control comparisons identified 24 independent association signals at P<5×10-7: 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn’s disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10-5 and 5×10-7) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
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影响因子：
10.5
作者：
Craddock, N;Asherson, P;Farmer, AE
通讯作者：
Farmer, AE
影响因子：
7.7
作者：
Frayling, TM;Walker, M;Hattersley, AT
通讯作者：
Hattersley, AT
影响因子：
3.2
作者：
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通讯作者：
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影响因子：
5.3
作者：
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通讯作者：
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影响因子：
56.9
作者：
Duerr, Richard H.;Taylor, Kent D.;Cho, Judy H.
通讯作者：
Cho, Judy H.