Genetic risk prediction in complex disease.
Genetic risk prediction in complex disease.
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DOI：
10.1093/hmg/ddr378
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发表时间：
2011-10-15
影响因子：
3.5
通讯作者：
Barrett JC
中科院分区：
文献类型：
--
作者：
Jostins L;Barrett JC
试图将患者分为疾病发病或结局的高风险或低风险是流行病学的基石之一。对于一些（但不是全部）疾病，临床可用的风险预测可以使用经典的风险因素，如体重指数、脂质水平、吸烟状况、家族史，在某些情况下，还可以使用遗传学（例如乳腺癌中的BRCA1/2）。全基因组关联研究（GWAS）的出现导致了大多数常见疾病的共同风险位点的发现。这些发现提高了在临床环境中使用这些变异进行风险预测的可能性。我们讨论了测量这些基因座的预测准确性的不同方法，并调查了18种常见疾病的GWAS变异的预测准确性。我们表明，遗传模型的预测准确性在不同疾病之间差异很大，但其范围与非遗传风险预测模型相似。我们讨论了哪些因素导致预测准确性的差异，以及这些预测比经典预测测试增加了多少价值。我们还回顾了风险预测的理想化模型的用途和缺陷。最后，我们展望了未来可能的临床应用遗传风险预测，并讨论了未来实用的现实期望。
Attempting to classify patients into high or low risk for disease onset or outcomes is one of the cornerstones of epidemiology. For some (but by no means all) diseases, clinically usable risk prediction can be performed using classical risk factors such as body mass index, lipid levels, smoking status, family history and, under certain circumstances, genetics (e.g. BRCA1/2 in breast cancer). The advent of genome-wide association studies (GWAS) has led to the discovery of common risk loci for the majority of common diseases. These discoveries raise the possibility of using these variants for risk prediction in a clinical setting. We discuss the different ways in which the predictive accuracy of these loci can be measured, and survey the predictive accuracy of GWAS variants for 18 common diseases. We show that predictive accuracy from genetic models varies greatly across diseases, but that the range is similar to that of non-genetic risk-prediction models. We discuss what factors drive differences in predictive accuracy, and how much value these predictions add over classical predictive tests. We also review the uses and pitfalls of idealized models of risk prediction. Finally, we look forward towards possible future clinical implementation of genetic risk prediction, and discuss realistic expectations for future utility.
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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：
5.5
作者：
Buijsse B;Simmons RK;Griffin SJ;Schulze MB
通讯作者：
Schulze MB
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
158.5
作者：
Green, Robert C.;Roberts, J. Scott;Farrer, Lindsay A.
通讯作者：
Farrer, Lindsay A.