Prediction of Fetal Hemoglobin in Sickle Cell Anemia Using an Ensemble of Genetic Risk Prediction Models
Prediction of Fetal Hemoglobin in Sickle Cell Anemia Using an Ensemble of Genetic Risk Prediction Models
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DOI：
10.1161/circgenetics.113.000387
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发表时间：
2014-04-01
影响因子：
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通讯作者：
Sebastiani, Paola
中科院分区：
文献类型：
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作者：
Milton, Jacqueline N.;Gordeuk, Victor R.;Sebastiani, Paola
背景：胎儿血红蛋白（HbF）是镰状细胞性贫血临床病程的主要调节因子。其水平具有高度遗传性，其人际变异性部分由影响HbF基因表达的3个数量性状位点调节。全基因组关联研究已经确定了这些数量性状位点中的单核苷酸多态性（snp）与HbF高度相关，但仅解释了HbF变异的10%至12%。将snp结合到遗传风险评分中可以帮助解释HbF水平的更大变异性，但这种方法的挑战在于选择纳入遗传风险评分的最佳snp数量。方法和结果-我们建立了14个由不同snp数量组成的遗传风险评分模型，并使用这些模型的集合来预测镰状细胞性贫血患者的HbF。这些模型在841例镰状细胞性贫血患者中进行了训练，并在3个独立队列中进行了测试。14个模型的集合解释了发现队列中23.4%的HbF变异性，而3个独立队列中预测和观察到的HbF之间的相关性在0.28到0.44之间。该模型包括BCL11A、HBS1L-MYB基因间区和HBB基因簇位点的snp，这些位点以前与HbF相关。结论：14种遗传风险模型的集合可以预测HbF水平，准确率在0.28 ~ 0.44之间，并且该方法在其他应用中也可能被证明是有用的。
Background-Fetal hemoglobin (HbF) is the major modifier of the clinical course of sickle cell anemia. Its levels are highly heritable, and its interpersonal variability is modulated in part by 3 quantitative trait loci that affect HbF gene expression. Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) in these quantitative trait loci that are highly associated with HbF but explain only 10% to 12% of the variance of HbF. Combining SNPs into a genetic risk score can help to explain a larger amount of the variability of HbF level, but the challenge of this approach is to select the optimal number of SNPs to be included in the genetic risk score.Methods and Results-We developed a collection of 14 models with genetic risk score composed of different numbers of SNPs and used the ensemble of these models to predict HbF in patients with sickle cell anemia. The models were trained in 841 patients with sickle cell anemia and were tested in 3 independent cohorts. The ensemble of 14 models explained 23.4% of the variability in HbF in the discovery cohort, whereas the correlation between predicted and observed HbF in the 3 independent cohorts ranged between 0.28 and 0.44. The models included SNPs in BCL11A, the HBS1L-MYB intergenic region, and the site of the HBB gene cluster, quantitative trait loci previously associated with HbF.Conclusions-An ensemble of 14 genetic risk models can predict HbF levels with accuracy between 0.28 and 0.44, and the approach may also prove useful in other applications.