Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk.
Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk.
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基于全基因组关联研究的机器学习用于预测年龄相关性黄斑变性风险。
DOI：
10.1167/tvst.10.2.29
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发表时间：
2021-02-05
影响因子：
3
通讯作者：
Ding Y
中科院分区：
文献类型：
--
作者：
Yan Q;Jiang Y;Huang H;Swaroop A;Chew EY;Weeks DE;Chen W;Ding Y
由于年龄相关性黄斑变性(AMD)是一种进行性疾病，而晚期AMD目前很难治愈，因此利用遗传信息准确和信息地预测一个人的AMD风险对于早期诊断和潜在的个性化临床治疗是必要的。这项研究的目的是利用具有不同机器学习方法的大型全基因组关联研究数据集，开发和验证AMD风险的新预测模型。来自DBGaP的国际AMD基因组联合会的32,215名年龄为≥50岁的高加索人的基因数据被用来建立和测试AMD风险的预测模型。实现了四种不同的机器学习方法--神经网络、套索回归、支持向量机和随机森林。使用遗传风险分数的标准Logistic回归模型也被考虑在内。所有基于机器学习的方法在预测晚期AMD病例(与正常对照相比)(曲线下面积=0.81-0.82，在单独的测试数据集中的Brier分数=0.17-0.18)和任何阶段的AMD(与正常对照相比)(在单独的测试数据集中的曲线下面积=0.78-0.79，Brier得分=0.18-0.20)方面都取得了令人满意的效果。在来自英国生物库的783名受试者的独立数据集(曲线下面积=0.67)中，预测性能得到了进一步验证。通过在大型AMD全基因组关联研究数据集上应用多种最先进的机器学习方法，我们建立的预测模型可以基于遗传信息和年龄提供对个人AMD风险概况的准确估计。在线预测界面的网址是：https://yanq.shinyapps.io/no_vs_amd_NN/.。准确、个性化的风险预测模型界面将极大地提高AMD的早期诊断水平，加强对AMD的量身定制的临床管理。
Because age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches. Genotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches—neural network, lasso regression, support vector machine, and random forest—were implemented. A standard logistic regression model using a genetic risk score was also considered. All machine learning–based methods achieved satisfactory performance for predicting advanced AMD cases (vs. normal controls) (area under the curve = 0.81–0.82, Brier score = 0.17–0.18 in a separate test dataset) and any stage AMD (vs. normal controls) (area under the curve = 0.78–0.79, Brier score = 0.18–0.20 in a separate test dataset). The prediction performance was further validated in an independent dataset of 783 subjects from UK Biobank (area under the curve = 0.67). By applying multiple state-of-art machine learning approaches on large AMD genome-wide association studies datasets, the predictive models we established can provide an accurate estimation of an individual's AMD risk profile based on genetic information along with age. The online prediction interface is available at: https://yanq.shinyapps.io/no_vs_amd_NN/. The accurate and individualized risk prediction model interface will greatly improve early diagnosis and enhance tailored clinical management of AMD.
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影响因子：
3.3
作者：
Ding, Ying;Liu, Yi;Chen, Wei
通讯作者：
Chen, Wei
DOI：
10.1146/annurev.genom.9.081307.164350
发表时间：
2009
影响因子：
8.7
作者：
Swaroop A;Chew EY;Rickman CB;Abecasis GR
通讯作者：
Abecasis GR
影响因子：
15.8
作者：
Sudlow C;Gallacher J;Allen N;Beral V;Burton P;Danesh J;Downey P;Elliott P;Green J;Landray M;Liu B;Matthews P;Ong G;Pell J;Silman A;Young A;Sprosen T;Peakman T;Collins R
通讯作者：
Collins R
影响因子：
1.9
作者：
DELONG, ER;DELONG, DM;CLARKEPEARSON, DI
通讯作者：
CLARKEPEARSON, DI
影响因子：
5.8
作者：
Kuhn, Max
通讯作者：
Kuhn, Max