Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.
Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.
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泛癌症分析表明，将多基因风险评分与可改变的风险因素相结合可以提高风险预测。
DOI：
10.1038/s41467-020-19600-4
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发表时间：
2020-11-27
影响因子：
16.6
通讯作者：
Johansson M
中科院分区：
文献类型：
--
作者：
Kachuri L;Graff RE;Smith-Byrne K;Meyers TJ;Rashkin SR;Ziv E;Witte JS;Johansson M
癌症风险由环境和遗传因素的复杂相互作用决定。多基因风险评分（PRS）提供了一个个性化的遗传易感性谱，可用于疾病预测。使用来自英国生物银行的数据（413，753人; 22，755例癌症病例），我们量化了将癌症特异性PRS与家族史和16种癌症的可改变风险因素相结合的额外预测价值。我们发现，结合PRS可显著提高大多数癌症的预测准确性，但这种改善的幅度差异很大。我们还表明，分层的PRS水平确定显着不同的5年的风险轨迹后，占家族史和可修改的风险因素。在人口水平上，PRS分布的前20%占癌症发病率的4.0%至30.3%，超过了许多生活方式相关因素的影响。总之，这项研究说明了通过整合遗传风险评分来改善癌症风险评估的潜力。预测癌症风险需要大型数据集和复杂的模型。在这里，作者整合了英国生物库中多种癌症的多基因风险评分和可改变的风险因素，改善了一般风险预测，并区分了遗传或生活方式因素具有更强关联的病例。
Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores. Predicting cancer risk requires large datasets and sophisticated models. Here the authors integrate polygenic risk scores and modifiable risk factors for multiple cancers in the UK Biobank, improving general risk prediction and distinguishing cases where genetic or lifestyle factors have stronger associations.
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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
影响因子：
5
作者：
Fry A;Littlejohns TJ;Sudlow C;Doherty N;Adamska L;Sprosen T;Collins R;Allen NE
通讯作者：
Allen NE
DOI：
10.1056/nejmoa1605086
发表时间：
2016-12-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
Khera AV;Emdin CA;Drake I;Natarajan P;Bick AG;Cook NR;Chasman DI;Baber U;Mehran R;Rader DJ;Fuster V;Boerwinkle E;Melander O;Orho-Melander M;Ridker PM;Kathiresan S
通讯作者：
Kathiresan S
影响因子：
30.8
作者：
Huyghe, Jeroen R.;Bien, Stephanie A.;Peters, Ulrike
通讯作者：
Peters, Ulrike
影响因子：
2
作者：
Gerds, Thomas A.;Andersen, Per K.;Kattan, Michael W.
通讯作者：
Kattan, Michael W.