Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction.
Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction.
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DOI：
10.3389/fgene.2012.00176
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发表时间：
2012
影响因子：
3.7
通讯作者：
Sebastiani P
中科院分区：
文献类型：
--
作者：
Hartley SW;Monti S;Liu CT;Steinberg MH;Sebastiani P
全基因组关联研究（GWAS）已经确定了遗传位点和个体表型之间的许多关联;然而，相对较少的GWAS试图检测多效性关联，其中位点同时与多个不同的表型相关。我们表明，多效性协会可以直接通过简单的贝叶斯网络的建设建模，这些模型可以应用于生产单一或集成的贝叶斯分类器，利用多效性，以提高遗传风险预测。所提出的方法包括两个阶段：（1）贝叶斯模型比较，以识别与一个或多个性状相关的单核苷酸多态性（SNP）;和（2）交叉验证特征选择，其中选择最终一组SNP以优化预测。为了证明该方法的能力和局限性，在16种情况下模拟了总共1600个具有两种二分表型的病例对照GWAS数据集，改变了因果SNP的关联强度、发现集的大小、病例和对照之间的平衡以及多效性因果SNP的数量。在这16种情景中，预测准确率从90%到50%不等。在包括多效相关SNP的14个场景中，多效模型搜索和预测方法始终优于朴素模型搜索和预测。在不存在真正的多效性SNP的两种情况下，多效性和朴素模型搜索之间的差异极小。为了进一步评估真实的数据的方法，发现一组1071镰状细胞病（SCD）患者被用来搜索脑血管意外和胎儿血红蛋白水平之间的多效性的关联。对352名SCD患者的较小验证集进行分类，并显示包含多效性SNP可能会略微改善预测，尽管差异无统计学意义。所提出的方法是强大的，计算效率高，并提供了一个强大的新方法检测和建模多效性疾病基因座。
Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The proposed method is robust, computationally efficient, and provides a powerful new approach for detecting and modeling pleiotropic disease loci.