Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.
Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.
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DOI：
10.1002/gepi.22159
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发表时间：
2018-12
影响因子：
2.1
通讯作者：
Cordell HJ
中科院分区：
文献类型：
--
作者：
Cherlin S;Plant D;Taylor JC;Colombo M;Spiliopoulou A;Tzanis E;Morgan AW;Barnes MR;McKeigue P;Barrett JH;Pitzalis C;Barton A;Consortium M;Cordell HJ
虽然类风湿性关节炎（RA）有许多治疗方法，但每种方法在患者中均显示出显着的无应答率。因此，先验预测治疗反应的可能性将对患者有很大的益处。在这里，我们使用来自风湿性关节炎最大化治疗效用（MATURA）联盟的RA患者的全基因组SNP数据，对各种统计方法进行了比较，以预测基线和3个月或6个月之间的三种治疗反应指标。评估了两种不同的处理和11种不同的统计方法。我们使用10折交叉验证来评估预测性能，并在需要时使用嵌套10折交叉验证来调整模型超参数。总的来说，我们发现SNPs只增加了很少的预测信息，仅使用临床特征，如基线性状值。这一观察结果可以解释为缺乏强的遗传效应和相对较小的样本量;在模拟和真实的数据分析中，具有较大的效应和/或较大的样本量，预测性能大大提高。总体而言，与性状的遗传结构一致的方法能够比不一致的方法实现更好的预测能力。对于RA的治疗反应，假设复杂的潜在遗传结构的方法比假设简化的遗传结构的方法实现了略好的预测性能。
Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome‐wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10‐fold cross validation to assess predictive performance, with nested 10‐fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.
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影响因子：
5.5
作者：
Barrera, P;van der Maas, A;van Riel, PLCM
通讯作者：
van Riel, PLCM
影响因子：
3.4
作者：
Geng, Yan;Han, Jingjing;Zhang, Zhuoli
通讯作者：
Zhang, Zhuoli
影响因子：
4.8
作者：
Graff, Philip;Feroz, Farhan;Lasenby, Anthony
通讯作者：
Lasenby, Anthony
DOI：
10.1093/bioinformatics/btu848
发表时间：
2015-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Euesden J;Lewis CM;O'Reilly PF
通讯作者：
O'Reilly PF
影响因子：
2.1
作者：
Dudbridge F
通讯作者：
Dudbridge F